The antiplatelet efficacies of these compounds were analysed using human isolated platelets upon activation with cross-linked collagen-related peptide by optical aggregometry. The results demonstrate that the hydroxyl groups in flavonoids are important for optimum platelet inhibitory activities. In addition, the 4-C=O and B ring phenyl groups are less critical for the antiplatelet activity of these flavonoids. This structure-activity relationship of flavonoids with the modulation of platelet function may guide the design, optimisation and development of flavonoid scaffolds as antiplatelet agents.Flavonoids exert innumerable beneficial effects on cardiovascular health including the reduction of platelet activation, and thereby, thrombosis. Hence, flavonoids are deemed to be a molecular template for the design of novel therapeutic agents for various diseases including thrombotic conditions. However, the structure-activity relationships of flavonoids with platelets is not fully understood. Therefore, this study aims to advance the current knowledge on structure-activity relationships of flavonoids through a systematic analysis of structurally-related flavones. Here, we investigated a panel of 16 synthetic flavones containing hydroxy or methoxy groups at C-7,8 positions on the A-ring, with a phenyl group or its bioisosteres as the B-ring, along with their thio analogues possessing a sulfur molecule at the 4 However, unnecessary activation of platelets within the vasculature leads to pathological conditions such as thrombosis, which results in blockage or reduction of blood flow to major organs including heart and brain instigating heart attack and stroke, respectively4. The currently used therapeutic options that involve the use of antiplatelet drugs such as clopidogrel, aspirin, and prasugrel are often linked with adverse side effects such as bleeding and are ineffective in some patients10. As cardiovascular diseases remain the leading cause of death worldwide11, the development of improved therapeutic strategies to prevent and treat thrombotic diseases remains a pressing priority.Platelets are small circulating blood cells that play pivotal roles in the regulation of haemostasis upon vascular injury through blood clotting14. Epidemiological and clinical studies have established a prominent link between the regular consumption of dietary flavonoids and decreased incidences of cardiovascular diseases or their risk markers20. Flavonoids have also been recognised as modulators of platelet function and their inhibitory activities can be attributed to their ability to inhibit reactive oxygen species (ROS) production22, modify cytoskeletal proteins such as actin and tubulin that mediate degranulation24, and to inhibit various kinases29 and receptors31 that play numerous roles in the regulation of platelet activation and thrombosis. The pharmacological potential of flavonoids is strongly related to their molecular structure, hence, the identification of key structural elements that are prerequisites for antiplatelet activity has provoked considerable interest in the area of drug discovery33. With a view to develop flavonoids as potential anti-thrombotic agents, some studies have been carried out to identify the key structural features governing the antiplatelet activity of flavonoids34. However, the current knowledge of structure-activity relationships (SARs) has mainly resulted from analyses involving different subclasses of natural flavonoids34. In order to translate flavonoids into potential molecular templates for drug design, a better understanding of the SAR of flavonoids, and a careful comparison of substitution pattern within a flavonoid subclass is necessary. Hence, to gain greater insights into the SAR of flavonoids with human platelets, this study has focused on assessing the effect of methoxylation, 4-C=S substitution, and different B-ring substitutions using a series of 16 synthetic flavones on the antiplatelet activities.Flavonoids, a group of polyphenolic plant metabolites, have been widely demonstrated to possess beneficial effects in the prevention of cardiovascular diseases35. The hydroxy flavones include 7,8-dihydroxy-2-phenyl-4H-chromen-4-one (F-1), 7,8-dihydroxy-2(thiophen-2-yl)-4H-chromen-4-one (F-2), 2-(furan-2-yl)-7-8,dihydroxy-4H-chromen-4-one (F-3) and 7,8-dihydroxy-2(pyridine-3yl)-4H-chromen-4-one (F-4). The hydroxy 4-thioflavones include 7,8-dihydroxy-2-phenyl-4H-chromen-4-thione (TF-1), 7,8-dihydroxy-2(thiophen-2-yl)-4H-chromen-4-thione (TF-2), 2-(furan-2-yl)-7-8,dihydroxy-4H-chromen-4-thione (TF-3) and 7,8-dihydroxy-2(pyridine-3yl)-4H-chromen-4-thione (TF-4). The methoxy flavones include 7,8-dimethoxy-2-phenyl-4H-chromen-4-one (CYC-1), 7,8-dimethoxy-2-(thiophen-2-yl)-4H-chromen-4 (CYC-2), 2-(furan-2-yl)-4H-chromen-4-one (CYC-3) and 7,8-dimethoxy-2-(pyridine-3-yl)-4H-chromen-4-one (CYC-4). The methoxy 4-thioflavones include 7,8-dimethoxy-2-phenyl-4H-chromen-4-thione (TCYC-1), 7,8-dimethoxy-2-(thiophen-2-yl)-4H-chromen-4-thione (TCYC-2), 2-(furan-2-yl)-7,8-dimethoxy-4H-chromen-4-thione (TCYC-3) and 7,8-dimethoxy-2-(pyridine-3-yl)-4H-chromen-4-thione (TCYC-4). Stock solutions of these flavones were prepared in dimethyl sulfoxide (DMSO) (100%) at 10 mg/mL concentration and the final required test concentrations were obtained by appropriately diluting these stocks. The final concentration of DMSO in platelets was maintained at 0.1% (v/v), which did not affect their function.The synthetic flavones used in this study are grouped into four main classes namely; hydroxy flavones with free –OH and 4-C=O, hydroxy 4-thioflavones with free –OH and 4-C=S, methoxy flavones with –OMe and 4-C=O and methoxy 4-thioflavones with–OMe and 4-C=SAll the experiments in this study were conducted in line with the appropriate institutional and national guidelines and regulations. Human blood was collected by venepuncture from aspirin free, healthy volunteers into vacutainers containing 3.2% (v/v) citrate after obtaining their informed consent. The procedures and consent forms used in this study were approved by the University of Reading Research Ethics Committee.2HPO4.12H2O, 1 mM MgCl2, 12 mM NaHCO3, 20 mM HEPES, pH 7.3) and centrifuged again at 1400 g for 10 minutes28. The final platelet pellet obtained was suspended in modified Tyrodes-HEPES buffer at a density of 4 × 108 cells/mL for aggregation assays and rested at 30 °C for 30 minutes before using in platelet functional assays.Human isolated platelets were prepared by adding 7.5 mL of ACD [(acid citrate dextrose) ] to 50 mL of blood prior to centrifugation at 100 g for 20 minutes at room temperature. The Platelet-Rich Plasma (PRP) was removed using wide bore pipette tips and mixed with 3 mL of ACD and centrifuged at 1400 g for 10 minutes at room temperature. The resulting platelet pellet was resuspended in modified Tyrodes-HEPES buffer -selective agonist, CRP-XL  in the presence or absence of a vehicle control [0.1% (v/v) DMSO] or different concentrations of flavone derivatives by optical aggregometry. Human isolated platelets (267 µL) taken in siliconised cuvettes were incubated with 3 µL of a vehicle control or various concentrations of flavone derivatives for 5 minutes at 37 °C. Following the incubation, 30 µL of CRP-XL (0.5 µg/mL) was added to platelets and the level of aggregation was measured for 5 minutes at 37 °C under constant stirring (1200 rpm). Data were analysed by calculating the percentage of maximum platelet aggregation at 5 minutes, and the level of aggregation obtained with the vehicle control was considered as 100% to quantify the impact of flavones on platelets.The lactate dehydrogenase (LDH) assay was performed using a LDH Cytotoxicity Assay Kit  according to the manufacturer’s instructions. Briefly, to 50 µL of human isolated platelets, 1 µL of a positive control  or a vehicle control [0.1% (v/v) DMSO] or various concentrations of flavone derivatives were added and incubated for 30 minutes at 37 °C. Then, 25 µL of the reaction mixture (provided in the kit) were added to the platelets and further incubated for 30 minutes in dark. Finally, the reaction was stopped by adding 25 µL of stop solution (provided in the kit). The absorbance was measured at 490 and 650 nm using a Fluostar Optima spectrofluorimeter . The level of LDH released with the positive control was considered as 100% to quantify the LDH release in flavone-treated samples.post-hoc test. All the statistical analyses were performed using GraphPad Prism 7 .The statistical significance between the vehicle controls and flavones-treated platelet samples was analysed by one-way ANOVA followed by Bonferroni 35 and the purities of compounds were analysed by reverse phase HPLC, and they were found to be >95%.To determine the relationship between the specific functional groups in the structures of flavones and their antiplatelet activity, a series of hydroxy flavones, hydroxy 4-thioflavones, methoxy flavones and methoxy 4-thioflavones containing different B-ring Figs , 3A & 4AIn order to determine the SAR of flavones with human platelets, the effects of 16 selected synthetic flavones on CRP-XL-stimulated platelet aggregation were evaluated by optical aggregometery. Human isolated platelets were treated with a vehicle [0.1% (v/v) DMSO] or diverse concentrations of flavones  for 5 minutes prior to activation with 0.5 μg/mL CRP-XL for 5 minutes. None of the flavones exhibited activatory effects on platelets on their own, and the vehicle control containing 0.1% (v/v) DMSO did not affect platelet activation.Hydroxy flavone (F-1) with free hydroxyls and carbonyl moiety significantly inhibited CRP-XL-stimulated platelet aggregation at lower concentrations such as 3.125, 6.25 and 12.5 μM but no significant inhibition was observed at concentrations higher than 12.5 μM. The hydroxy 4-thioflavone (TF-1) showed significant inhibitory effects at all the concentrations tested. However, the methoxy flavone (CYC-1) inhibited the aggregation significantly only at 100 μM and the methoxy 4-thioflavone (TCYC-1) did not show any inhibitory effects at any of the concentrations tested Fig. .Hydroxy flavone (F-2) displayed a similar inhibitory trend to F-1 with significant inhibition at lower concentrations up to 12.5 μM, whereas the hydroxy 4-thioflavone (TF-2) and the methoxy flavone (CYC-2) inhibited aggregation only at 100 μM. The methoxy 4-thioflavone (TCYC-2) of this group was found to possess no inhibitory activity on platelet aggregation Fig. .Hydroxy flavone (F-3) and hydroxy 4-thioflavone (TF-3) inhibited the CRP-XL-induced platelet activation at all the concentrations tested, however, the methoxy flavone (CYC-3) and the methoxy 4-thioflavone (TCYC-3) failed to inhibit the aggregation Fig. .None of the flavones with a pyridine B-ring displayed inhibitory effects on CRP-XL-stimulated platelet aggregation Fig.  at conceIt is interesting to note the impact of altering the B-ring on the platelet activity among the same class of flavones. For hydroxy flavones (with –OH and 4-C=O), changing the phenyl group F-1) to a thiofuran group F-2) did not affect the inhibitory potential as both compounds elicited the inhibitory activity up to 12.5 μM. Consistent inhibition (~65–70%) across the tested concentrations (3.125–100 μM) was observed when the phenyl group was replaced with a furan group (F-3). In contrast, substitution of the phenyl group with a pyridine group led to a complete abolition of inhibitory activity in platelets (F-4). A similar trend was observed for the hydroxy 4-thioflavones (with –OH and 4-C=S) when the B-ring phenyl group was replaced with its bioisosteres furan and pyridine. However, the substitution of thiofuran led to a reduction in the activity, for example, TF-2 showed ~50% inhibition only at 100 μM  and methoxy 4-thioflavones (with –OMe and 4-C=S) as these two classes of flavones did not display any significant inhibitory activity on platelets in comparison to their hydroxyl analogues. Nevertheless, amongst the methoxy flavones, flavones with a B ring phenyl group (CYC-1) or thiofuran group (CYC-2) showed similar activities with inhibition only at 100 μM. Methoxy 4-thioflavones (with –OMe and 4-C=S) did not exert any inhibitory effects on platelets.To corroborate the above results, the effects of these flavones on another platelet activation marker, specifically fibrinogen binding , were measured by flow cytometry. The results obtained from this experiment concur with the aggregation data where hydroxy flavones F-1 (at 3.125–12.5 μM), F-2 (at 3.125–12.5 μM) and F-3 , as well as thiohydroxy flavones TF-1 , TF-2 (at 100 μM) and TF-3  significantly inhibited fibrinogen binding Fig. , which iFinally, to determine whether the platelet inhibition observed was the result of a specific pharmacological effect of flavones rather than due to their cytotoxic effects, an LDH cytotoxicity assay was performed. For this, platelets were treated with a vehicle control [0.1% (v/v) DMSO] or diverse concentrations of flavones  and the release of LDH, a cytosolic enzyme, which is an indicator of cellular toxicity was measured. As shown in Fig. Understanding the relationship between distinct functional groups within the structures of flavones and their influence on antiplatelet activity is critical for further development and modification of flavones in order to make them as more potent lead compounds for drug design. Such knowledge will aid in the development of improved therapeutic strategies for the prevention and treatment of cardiovascular disesaes, specifically thrombosis. In the present study, the analysis and comparison of the inhibitory activities of a series of 16 structurally-related synthetic flavones on CRP-XL-stimulated human platelet activation highlighted the key structural features that are required for the inhibition of platelet function.3 group). This highlighted that hydroxy groups, which are hydrogen bond donors, are essential for their inhibitory activities in platelets and that methoxy groups with hydrogen bond accepting profiles are less effective in this regard. However, the study by Bojić et al.34 reports increased anti-aggregatory potencies of O-methylated derivatives in comparison to their hydroxy analogues. This disparity could possibly be due to the difference in the hydroxyl substitution pattern of the flavones studied which would affect the interaction with the molecular target. In addition, several previous studies reported the loss of biological activity of flavonoids upon complete methylation of their active hydroxy groups36. Together with these previous studies, our data suggest that hydroxy groups are a key descriptor for the biological activity of flavonoids. It is worth highlighting that the hydroxy flavones used in this study possess hydroxyl groups at the C-7,8 position as opposed to the C-5,7 position in chrysin, a natural flavone that was previously reported to negatively modulate platelet activity37. When comparing the activity between chrysin and its 7,8-hydroxy analogue (F-1), it can be deduced that the position of the hydroxyl groups also influences platelet function as chrysin exhibited dose-dependent inhibition of platelet activity (6.25–100 μM)38, whereas, 7,8- hydroxyl flavone showed inhibition only between 3.25–12.5 μM. Further studies are required to determine the reasons for the low inhibitory effects obtained from higher concentrations of hydroxyl flavones with phenyl and thiofuran B-rings.In general, comparison between different classes of flavones with the same B-ring moiety suggested that the hydroxy flavones (with free -OH groups) were more effective than their corresponding methoxy flavones , however, no beneficial effects were observed for flavones with methoxy groups (methoxy flavones and methoxy 4-thioflavones). It is interesting to note that the influence of 4-C=S was also found to be dependent on the B-ring functionality as moderate loss of inhibitory activity was observed upon the replacement of 4-C=O with 4-C=S for flavones with a thiofuran B-ring. This demonstrates that the systematic analysis of flavones through careful correlation of effect of each substitution with respect to other functional groups is important for better optimisation of these compounds as molecular templates for drug design and discovery.A number of previous studies have reported the significance of 4-C=O in the C-ring of flavonoids for antiplatelet activities based on the comparison between flavonoids with and without 4-C=ONatural flavonoids contain a phenyl group as the B-ring, hence, previous reports have focused on the influence of the position of the B-ring and its hydroxylation patterns. The present study involving synthetic flavonoids allowed the determination of the effect of incorporating bioisosteres of the phenyl group. It was found that replacing the phenyl group with a furan group was well tolerated for hydroxy flavones and hydroxy 4-thioflavones, whereas replacement of a phenyl group with a thiofuran group led to loss of inhibitory activities in platelets for hydroxy 4-thioflavones but was tolerated for hydroxy flavones. Conversely, replacement of the phenyl group with a pyridine group led to complete loss of inhibition in platelets. These observations suggest that the B-ring phenyl group is not critical for the antiplatelet activity, but the B-ring heteroatoms largely influence the activity. Furthermore, these observations suggest that the orientation and binding modes of the B-ring moieties might influence the interaction with their molecular targets. Hence, identification of the molecular targets for these flavones, and careful optimisation of the nature of the B-ring could lead to more efficacious flavone scaffolds for the development of novel antiplatelet agents. Furthermore, the hydroxy flavones and thiohydroxy flavones showed significant inhibitory effects on fibrinogen binding, a key marker for platelet activation via inside-out signalling to integrin αIIbβ3. This suggests that these flavones specifically with free hydroxy groups may modulate distinctive functions of platelets. The LDH cytotoxicity assay showed that these flavones are not cytotoxic to platelets at the concentrations tested and hence the inhibitory effects observed are due to their pharmacological effects on platelet function.In conclusion, a panel of 16 structurally-related hydroxy flavones, methoxy flavones and their 4-thio analogues were screened for their antiplatelet activity upon CRP-XL-induced activation in human platelets. SAR analysis of these flavones suggested that the free hydroxyl group is essential for antiplatelet activity. Moreover, the modification at 4-C=O to 4-C=S in the C-ring, and B-ring modifications of phenyl group into specific bioisostere such as furanyl group, are well tolerated without any significant loss of their inhibitory activities. The molecular targets and the impact of these synthetic flavones on specific signalling pathways in platelets were not investigated in this study. Since the natural flavonoids posses broad spectrum of binding affinities and inhibitory activities against numerous cellular targets, the synthetic flavones with higher specificity for selective targets may be beneficial in achieving targeted effects. Therefore, further studies will be required to underpin the impact of these synthetic flavones with specific functional groups on various molecular targets in platelets. Together, the results obtained in this study with synthetic flavones enhance the current understanding of the SAR of flavones with human platelets and may aid in the design and development of novel anti-thrombotic strategies using flavones as potential molecular templates.Supplementary Information


Nanosilver plays an important role in nanoscience and nanotechnology, and is becoming increasingly used for applications in nanomedicine. Nanosilver ranges from 1 to 100 nanometers in diameter. Smaller particles more readily enter cells and interact with the cellular components. The exposure dose, particle size, coating, and aggregation state of the nanosilver, as well as the cell type or organism on which it is tested, are all large determining factors on the effect and potential toxicity of nanosilver. A high exposure dose to nanosilver alters the cellular stress responses and initiates cascades of signalling that can eventually trigger organelle autophagy and apoptosis. This review summarizes the current knowledge of the effects of nanosilver on cellular metabolic function and response to stress. Both the causative effects of nanosilver on oxidative stress, endoplasmic reticulum stress, and hypoxic stress—as well as the effects of nanosilver on the responses to such stresses—are outlined. The interactions and effects of nanosilver on cellular uptake, oxidative stress (reactive oxygen species), inflammation, hypoxic response, mitochondrial function, endoplasmic reticulum (ER) function and the unfolded protein response, autophagy and apoptosis, angiogenesis, epigenetics, genotoxicity, and cancer development and tumorigenesis—as well as other pathway alterations—are examined in this review. Together, this exemplified the importance of the antioxidant response involving Nrf2 in protecting the cells from the DNA damage and cell cycle arrest induced by nanosilver treatment [DNA damage in cells can disrupt the cell cycle causing an accumulation of cells halted in one of the cell cycle phases: gap 1 (G1), DNA synthesis (S), or the gap 2 (G2)/mitosis (M) phase. An accumulation of cells in the subG1 phase is indicative of apoptotic cells, potentially due to irreversible DNA damage. Nanosilver  treatment resulted in cell cycle arrest in the G2/M phase, with cancerous human glioblastoma (U251) cells being more affected than non-cancerous IMR-90 cells . Cell cyreatment . Treatmereatment .E. coli cells treated with non-lethal concentrations of nanosilver and contributes to the antibacterial activity that is caused by the nanosilver. In both the MEL and E. coli cells, this mechanism is not a result of the released Ag+ ions from the nanosilver, but is a silver nanoparticle specific effect. The treatment of male and female BALB/c mice with nanosilver via intraperitoneal injection before pregnancy resulted in embryos with a pale and anemic appearance, reduced hemoglobin in the blood, and significantly reduced growth and development. Gene expression in the embryonic liver tissue was greatly affected with the upregulation of 37 genes and the downregulation of 264 genes, with many of the downregulated genes being involved in erythropoiesis [S. cerevisiae gene deletion arrays as a high throughput screening technique to predict cellular effects [Wang et al.  found thopoiesis . Decreas effects .Nrf2 is a transcription factor that is integral to the antioxidant response and induces various xenobiotic-metabolizing and antioxidant enzymes through the electrophile response element/antioxidant response element (EpRE/ARE). When not stimulated, Nrf2 is bound to kelch-like ECH-associated protein 1 (Keap1) and Cullin-3 (Cul3) ubiquitin ligase in the cytosol where it is degraded through ubiquitination and subsequent proteasomal degradation. Nrf2 is activated as a protective response to various stressors such as ROS or electrophiles. Cysteine modifications on C151, C273, and C288 in Keap1 cause Nrf2 to be released and allow it to translocate to the nucleus where it binds to the antioxidant response element (ARE) along with small Maf proteins. This activates the expression of genes involved in the antioxidant response such as HO-1, NQO1, glutathione S transferases (GSTs), and many others ,197,198.In the insulin signalling pathway, insulin binds to the insulin receptor tyrosine kinase, which activates insulin receptor substrate 1 (IRS-1) by phosphorylation, leading to activation of AKT and mTOR. In the liver, AKT regulates glucose metabolism and is involved in inducing glycogen synthesis via the phosphorylation and inhibition of glycogen synthase kinase-3 beta (GSK3β). The levels of phosphorylated AKT increased in the livers of male Sprague Dawley rats treated orally with up to 100 mg/kg/day nanosilver , as did the levels of phosphorylated IRS-1, phosphorylated GSK3β, and phosphorylated mTOR .Drosophila melanogaster with citrate-coated nanosilver (1–50 nm) has been found to result in cuticular demelanization and to detrimentally effected their ability to move, a symptom that is also seen with copper deficiency [+ ions on albino rats, using AgCl mixed into the food. Copper deficiency was observed in the rats, as well as decreased mRNA expression of several genes such as the cuproenzyme cytochrome c oxidase subunit 4 isoform 1 (Cox4i1), and genes involved in copper homeodynamics such as Cu(I)/Ag(I)-transporter 1 (CTR1), Cu(I)/Ag(I)-transporter 2 (CTR2), Cu-chaperone for SOD1 (CCS), MT1A, and Cu(II) binding cytosol protein (Commd1); although the protein expression of MT1A and Commd1 did not decrease [Copper is an essential micronutrient in the human body. Whether nanosilver has any effects on copper homeostasis in human cells or in the human body is currently unexplored. However, non-toxic nanosilver treatment of ficiency . Additioficiency ,24. A reficiency  examineddecrease .Amyloid protein aggregates formed from hen egg white lysozyme in the presence of a low concentration of nanosilver (108 nm), and injected into male Wistar rats as a model for Alzheimer’s Disease, disrupted the formation of amyloid aggregates, with the aggregates formed being non-toxic to the rats . This alThe effect of nanosilver treatment on the levels of various neurotransmitters were examined in female Wistar rats treated orally with nanosilver  by gavage administration for 14 or 28 days . After 22DCFDA, the main dye that is used to detect ROS. Nanosilver activates the immune response and increases inflammation, with this having a beneficial effect and shortening the time required for wound healing. There are currently only a small number of studies that have been done on the effects of nanosilver on hypoxic stress, thus requiring additional research. High dose nanosilver treatment around the EC50 value for the cells leads to increased contact and signalling between the ER and the mitochondria, increased transfer of calcium from the ER to the mitochondria, mitochondrial dysfunction, decrease in ATP production, and mitochondria-mediated apoptosis. It has been found that nanosilver exposure can disrupt ER homeostasis and cause the induction of the ER stress response. The general consensus of the studies to date is that nanosilver treatment inhibits angiogenesis. Epigenetic changes in terms of increased DNA methylation, various histone tail modifications, and changes in non-coding RNA expression have been observed. In terms of genotoxicity, DNA damage, DNA base oxidation, DNA adducts, DNA strand breaks, and chromosomal aberrations have all been observed; however, not all studies observe this, and this effect may not be permanent once the nanosilver treatment is removed. Finally, cancer cells are consistently more susceptible to nanosilver treatment than non-cancer cells, and in vivo studies indicate that nanosilver is an effective and promising anti-cancer agent, with no adverse effects observed in the treated animals. This anti-cancer effect can be further improved by using the nanosilver treatments in combination with other cancer treatments.Nanosilver is generally taken up into cells through endocytosis, with the smaller sized nanosilver being taken up into the cells, interacting with the cellular components, and binding to biomolecules through their sulfhydryl groups. Larger sized nanosilver may be too large to be internalized and may remain on the outside where it can trigger various receptor-mediated signalling mechanisms or cause lipid peroxidation. Inside the cell, it has generally been accepted that nanosilver produces ROS and oxidative stress in the cells, although this has been questioned in some studies where no increase in ROS was seen. This may be due to different experimental conditions or due to problems detecting the actual levels ROS with HThus, in general, nanosilver in reasonable doses has many beneficial effects and applications, especially in its anti-bacterial, anti-viral, anti-fungal, anti-cancer, and wound healing functions; while at the same time not causing adverse effects in vivo. As with any compound, high enough doses will induce toxicity, especially in in vitro situations.


Inasua is one of the traditional fermented fish products in Maluku, Indonesia. There are two types of inasua, i.e. with and without sap. The research aimed to study the succession of lactic acid bacteria (LAB) during fermentation and microbial composition in inasua.inasua was taken from two traditional producers in Layeni village, Ceram Island. The diversity of lactic acid bacteria was analyzed based on the 16S rRNA gene sequence.The sample of Lactobacillus plantarum was found dominant in both inasuas fermentation processes. At end of fermentation, L. plantarum was still found dominant in inasua with sap while inasua without sap was dominated by Leuconostoc mesenteroides. In addition, Lactobacillus paracasei (LAB) was found only in inasua with sap. The result of Denaturing Gradient Gel Electrophoresis (DGGE) revealed that Lactobacillus was the dominant bacteria in inasua with sap while Staphylococcus was dominant in inasua without sap.The succession of lactic acid bacteria was strongly influenced by the physicochemical characteristics during fermentation. Inasua with sap was found with higher bacterial diversity index and lower evenness and dominance indices, as well as more complex LAB succession pattern during fermentation and bacterial composition, as opposed to inasua without sap. It is a local wisdom of Teon, Nila, and Serua communities. In addition to being used as a food reserve during lean times  uses both coconut sap and salt as preservatives, inasua without coconut sap (inasua-NS) uses only salt. Both have distinct sensory characteristics and different shelf life.Fish is the main source of protein consumed by people in Maluku. Fish is often caught abundantly, making preservation necessary. One of the fish preservation techniques with fermentation is an times  in the pInasua fermentation occurs spontaneously and involves various types of microorganisms. The main microbe involved in fish fermentation is LAB . A total of 5 kg brown stripe red snapper (Lutjanus vitta L) was obtained from the sea around the Ceram Island and 2.5 kg was processed into inasua-NS by adding salt only and the remaining 2.5 kg into inasua-S by adding both salt and coconut sap. The inasua was then allowed to ferment in jars at room temperature for 12 weeks. Analysis of LAB succession was carried out in week one until week 12 of fermentation. A 500 g sample from each inasua type was taken into the laboratory for further analysis.The sample of inasua type was mixed with 225 ml sterile peptone solution and homogenized using stomacher bags. One ml of the homogenized and diluted sample was poured into Petri dishes followed with de Man, Rogosa and Sharp agar (MRSA) media containing 1% CaCO3 with 3% and 5% and NaCl prior to incubation at room temperature for 48 hours. All isolates obtained were stained with Gram and spore staining, catalase test, and fermentation of carbohydrates (A 25 g sample from each hydrates .DNA extraction was carried out following procedure from Presto TM Mini GDNA Kit (Gene-aid). The result of DNA extraction was used to amplify 16S rRNA gene. The 16S rRNA gene was amplified using PCR machine with 63F (5′-CAG GCC TAA CAC ATG CAA GTC-3′) and 1387R (5′-GGG CGG WGT GTA CAA GGC-3′) primers  for sequence coupling. The sequences were then compared with GenBank database using Basic Local Alignment Search Total Nucleotide (BLASTN) software. The obtained homologous sequences were then aligned using MEGA 6.0 software  with 100inasua and was performed using P338F-GC (5′-CG-CCCGCCGCGCGCGG-CGGGCGGGG-CGGGGGCCCGGGGGGACTCCGGGAGGCAGCAG-′3) and P518R (5′-ATTA-CCGC-GGCTGCTGG-′3) primers . Amplification of the 16S rRNA gene used PCR machine to detect bacteria in  primers . PCR reaTotally, 20 μg DNA from the amplification was mixed with 4 μl loading dye prior to migration through 6% (b/v) polyacrylamide gel in TAE 1× buffer  with a gel prepared from 30–70% (b/v) acrylamide stock solution  containing denatures (100% denatures: 7 M urea and 40% (v/v) formamide)  dominance values of DGGE results using PAST3 software . In addiinasua fermentation, comprising 22 isolates from inasua-NS and 28 isolates from inasua-S. Analysis of gene sequences encoding 16S rRNA from 18 selected isolates  with GenBank data using the BLAST-N program revealed that all isolates were LAB that are closely related to the Lactobacillus and Leuconostoc groups. The percentage of sequence similarity with target 16S rRNA gene in genBank database was 96–99%  was less varied than from inasua-S (8 band) in polyacryl-amide gel , while inasua-NS was 0.90 (low). The evenness and dominance indices of both types of inasua were also different where evenness index of inasua-NS was 0.68 (high) and inasua-S was 0.52 (medium). The dominance index of inasua-NS was 0.52, whereas inasua-S 0.37   . High saococcus) . Naturalocessing . Low watentation . S. epids 11–24% . L. curiinasua-S fermentation. The LAB are from fish, sap and inasua processing. The diversity of LAB is influenced by the sources as well as salinity, acid and alcohol contents during fermentation. The salt content below 7% can increase the growth of LAB that plays an important role in fish fermentation . The LAB is capable of producing lactic acid through homofermentative pathways and is commonly found in fermented sap to produce traditional alcoholic beverages  was lower than in inasua-S (8 band) due to high salt content in inasua-NS. The addition of coconut sap containing a number of microorganisms contributed to the high OTU richness in inasua-S. Dominance index of inasua-NS (0.52) was higher than inasua-S (0.37). Both types of inasua have one dominant OTU. L. apinorum and S. epidermidis have highest abundance of OTU in inasua-S and inasua-NS, respectively.The OTU richness in inasua fermentation is strongly influenced by physicochemical characteristics of inasua. Lactobacillus plantarum was found dominant during fermentation. At the end of inasua-S fermentation, L. plantarum was found dominant, while inasua-NS was dominated by Leuconostoc mesenteroides. Lactobacillus paracasei is a LAB found only in the inasua-S fermentation. The result of DGGE revealed that the dominant bacteria in inasua-NS was Staphylococcus, while in inasua-S was Lactobacillus. The bacterial diversity index in inasua-S was higher than in inasua-NS.The succession of lactic acid bacteria during


Our findings define a mechanism by which inflammation activates the ISR in a cell-nonautonomous manner and suggest that inhibition of 15-d-PGJ2 production might be a useful therapeutic strategy in some neuroinflammatory contexts.Stress granules (SGs) are cytoplasmic RNA–protein aggregates formed in response to inhibition of translation initiation. SGs contribute to the stress response and are implicated in a variety of diseases, including cancer and some forms of neurodegeneration. Neurodegenerative diseases often involve chronic phosphorylation of eukaryotic initiation factor 2α (eIF2α), with deletions of eIF2α kinases or treatment with eIF2α kinase inhibitors being protective in some animal models of disease. However, how and why the integrated stress response (ISR) is activated in different forms of neurodegeneration remains unclear. Because neuroinflammation is common to many neurodegenerative diseases, we hypothesized that inflammatory factors contribute to ISR activation in a cell-nonautonomous manner. Using fluorescence microscopy and immunoblotting, we show here that the endogenously produced product of inflammation, 15-deoxy-Δ The ISR2–5–In vivo, SGs are most commonly formed because of ISR activation and eIF2α phosphorylation  , 8–10. I8–rylation , 11–14 bation 11–. Strikination 11–, 14–17. on 11–14–, 18, 19.on 11–14–, 6.Prostaglandins are a class of neuroinflammatory molecules that are synthesized after environmental insults to the brain and are chronically secreted under many forms of neurodegenerative disease –24. The –In the course of analyzing the cellular response to 15-d-PGJ2, we observed that 15-d-PGJ2 represses bulk translation and triggers an ISR by causing eIF2α phosphorylation. This leads to the hypothesis that 15-d-PGJ2 is a product of inflammation that can chronically activate the ISR, contribute to cell death, and potentially drive neurodegenerative disease progression in a cell-nonautonomous manner.15-d-PGJ2 has been suggested to inhibit eIF4A, although it was originally observed that this prostaglandin can induce P-eIF2α , 28. To 2-induced SGs, (which form upon eIF2α phosphorylation) and pateamine A  . Conversely, PatA-induced SGs solely displayed a growth phase, which, after about 60 min, resulted in SG area stagnation (B). PatA-induced granules have no known negative feedback loop, and after 200 min, the granules still had no reduction in area.We used cells expressing a stable GFP-G3BP1 fusion protein to track the kinetics of SG assembly (quantified by SG area per cell area) over time. We compared the kinetics of SG assembly upon treatment with 15-d-PGJ2 to NaAsOg eIF4A) . Without 100 min B. Converagnation B. PatA-im ISRIB, SGs that formed because of NaAsO2 or 15-d-PGJ2 stresses disappeared within 5 min, whereas PatA SGs were resistant to ISRIB treatment (A and C). This suggests that 15-d-PGJ2 inhibits translation and promotes SG formation through eIF2α phosphorylation and not through inhibition of eIF4A function.An important result was that, when cells were treated with 5 nreatment , A and CBecause ISRIB was able to inhibit SGs in 15-d-PGJ2–treated cells, we tested whether global translation suppression in 15-d-PGJ2–treated cells was coupled to eIF2α phosphorylation. If global translation suppression occurred simultaneously (and therefore correlated) with eIF2α phosphorylation, then this would suggest that P-eIF2α may be involved in mediating translational attenuation and SG formation. We treated U-2 OS cells with 15-d-PGJ2 over a period of 4 h, and cells were briefly treated with puromycin in a 5-min pulse before lysates were collected to monitor both P-eIF2α and puromycin incorporation into nascent peptides as a measure of bulk translation activity. We then used immunoblotting to quantify P-eIF2α and translational kinetics during the cellular response to 15-d-PGJ2 treatment.C and D). Using a correlation plot to examine how related these two events might be, we found a substantial correlation between the kinetics of eIF2α phosphorylation and translational attenuation (R2 = 0.876), suggesting that the two events are coupled (E).Compared with unstressed cells, cells treated with 15-d-PGJ2 showed about 2-fold more eIF2α phosphorylation, which peaked and plateaued at about 1 h. Additionally, 15-d-PGJ2–treated cells showed a reduction in translation that plateaued simultaneously with P-eIF2α , C and D coupled E.2 or 15-d-PGJ2, ISRIB was added 1 h post-stress, and then cells were pulsed with puromycin for 5 min. Subsequently, the cells were fixed and stained with an anti-puromycin antibody (α-puro), or lysates were collected for immunoblotting.In principle, ISRIB could trigger SG disassembly by restoring translation in 15-d-PGJ2–treated cells or by an unrelated mechanism. To examine whether translation was being restored upon ISRIB treatment, U-2 OS cells stably expressing GFP-G3BP1 were treated with NaAsO2- and 15-d-PGJ2–treated cells showed decreased translation, as assessed by puromycin incorporation detected via immunoblotting (A). In addition, ISRIB rescued translation in cells treated with either NaAsO2 or 15-d-PGJ2 (A). Similar results were observed by examining individual cells, where we determined that treatment with NaAsO2 or 15-d-PGJ2 reduced the α-puromycin fluorescence signal (B and C). Moreover, upon ISRIB treatment, α-puromycin fluorescence signal quantification showed a partial restoration of translation in both NaAsO2- and 15-d-PGJ2–treated cells (B and C). These data suggest that ISRIB is not functioning with off-target effects in the case of 15-d-PGJ2–induced SGs because it is restoring translation in a similar manner as NaAsO2-induced SGs. These results are consistent with 15-d-PGJ2 inhibiting translation through a mechanism dependent on P-eIF2α.As expected, we observed that both NaAsOblotting A. In add5-d-PGJ2 A. Similae signal , B and Ced cells , B and CWT/WT) or MEFs expressing eIF2S1S51A/S51A (MEFsS51A/S51A) were treated for 1 h with NaAsO2, 15-d-PGJ2, or PatA, and immunofluorescence microscopy was performed to image G3BP as a marker for SGs. As expected for PatA-treated MEFs, SGs could form in both cell types, whereas SGs could only form in MEFsWT/WT treated with NaAsO2. Importantly, MEFsS51A/S51A could not form SGs upon treatment with 15-d-PGJ2 (A). These data demonstrate that 15-d-PGJ2 requires phosphorylation of eIF2α to trigger SG formation.The above results were consistent with a model wherein 15-d-PGJ2 triggers SG formation by enhancing eIF2α phosphorylation. To directly test this model, we examined whether 15-d-PGJ2 could trigger SG formation in mouse embryonic fibroblasts (MEFs) that harbor mutations in the phosphorylation site in eIF2α (homozygous for S51A mutations). WT MEFs (MEFs5-d-PGJ2 A. These 2 (known to activate HRI), 15-d-PGJ2, thapsigargin , or PatA (inhibits eIF4A) .Mammalian cells contain four eIF2α kinases (eIF2αKs): HRI, PERK, PKR, and GCN2 . Howevers eIF4A) . Cells wB and C, and Fig. S1). We next monitored the SG response of our panel of stressors in the presence of PERKi and found that all stressors except PatA had a reduced SG area per cell area . Immunoblotting for P-eIF2α showed that PERKi prevented P-eIF2α for both NaAsO2 and 15-d-PGJ2 (D). These data, although inconclusive regarding the responsible kinase for eIF2α phosphorylation in the presence of 15-d-PGJ2, confirm that an eIF2αK is responsible for SG induction and also suggest that PERKi may be affecting other eIF2αKs besides PERK.We observed that both the SG area and cell area of 15-d-PGJ2 and TG SGs were reduced after pretreatment with PKRi, suggesting that either PKRi might inhibit TG-induced activation of PERK or that TG activates PKR (5-d-PGJ2 D. These A–C). This suggests that multiple kinases are activated by 15-d-PGJ2 and produce a combinatorial effect in phosphorylating eIF2α (B and C). As controls, we reproduced earlier results showing that NaAsO2 translation repression depends on HRI   that were either WT or lacking a specific eIF2αK . Surprisng eIF2α , B and Cs on HRI  and thatA and B) . Taken tAFig. S3) (One possible mechanism by which 15-d-PGJ2 could activate eIF2αKs is to inhibit the 26S proteasome . This waig. S3A) . To testA–C). Because similar levels of poly-Ub conjugates are observed with 15-d-PGJ2 and MG132, but P-eIF2α occurs much earlier with 15-d-PGJ2, it strongly suggests that eIF2α phosphorylation in response to 15-d-PGJ2 is not a direct consequence of proteasome inhibition (D). It remains possible that proteasome inhibition may contribute to eIF2α phosphorylation at later time points. It is also formally possible that differential inhibition of proteasomal subunits between MG132 and 15-d-PGJ2 leads to temporal differences in activation of the ISR or that inhibition of other proteins in conjunction with the 26S proteasome causes faster phosphorylation of eIF2α by 15-d-PGJ2. Previous work demonstrated that 15-d-PGJ2 can covalently modify proteins and alter their function , that, in combination, lead to multipronged activation of the ISR . Third, SG induction by 15-d-PGJ2 is reduced by inhibitors of eIF2αKs, but eIF2α phosphorylation is present under various kinase deletion backgrounds  brains , 23, 25 33–36–One potential measure of the utility of targeting 15-d-PGJ2 for treatment of neurodegenerative diseases in humans is the efficacy of COX-2 inhibitors in clinical trials. However, COX-2 inhibition has been met with conflicting results in clinical trials for ALS and AD –41. One 36–40–2. Cells were grown in DMEM with 10% FBS and 1% penicillin–streptomycin. For drug treatments, cells were passaged into 12-mm cell culture dishes at a final concentration 1.87 × 105 cells ml−1 and allowed to adhere overnight. For SG induction, the medium was replaced with serum-free medium containing either NaAsO2 , 15-d-PGJ2 , thapsigargin , pateamine A (100 nm in DMSO), or MG132 , and cells were incubated at 37 °C for the allotted times indicated in each assay. For ISRIB treatment, 5 nm ISRIB  was added after an hour of stress addition. For eIF2αK inhibitor experiments, cells were preincubated with either 1 μm PKRi  or 1 μm PERKi  for 15 min before stress addition. For ribopuromycinylation assays, cells were incubated at 37 °C with puromycin  5 min prior to fixation or lysis.All cell lines were grown and maintained in an incubator at 37 °C at 5% COm Tris-HCl (pH 8.0), 150 mm NaCl, 1% NP-40, and protease inhibitor mixture (Thermo Scientific)). Cell lysates were rocked at 4 °C for 30 min and then clarified by centrifugation . 4× NuPAGE sample buffer was added to lysates to final concentration of 1×. Samples were boiled for 5 min at 95 °C, loaded into 4–12% BisTris NuPAGE gel, and transferred to a nitrocellulose membrane. Membranes were blocked with 5% BSA in TBS with 0.1% Tween 20 (TBST) for an hour and then incubated with primary antibody overnight at 4 °C  for an hour at room temperature. Coverslips were incubated with primary antibody (1:500) overnight at 4 °C in 1% BSA in PBS-T. Coverslips were then washed three times with PBS and incubated with a secondary antibody (1:1000) at room temperature for 2 h in 1% BSA in PBS-T. Coverslips were washed three times in PBS and Vectashield mounting medium containing 4′,6-diamidino-2-phenylindole for visualization of nuclei.2 for the indicated time course. Images were taken every 5 min using a ×100 objective. For fixed-cell microscopy, slides were imaged on a Delta-Vision Olympus IX71 confocal microscope with a ×100 objective using softWorx software. SG area per cell area was quantified using particle finder in ImageJ. The fluorescence intensity of puromycin-labeled nascent peptides in individual cells was quantified using mean gray values in ImageJ. For all microscopy-based experiments, more than 100 cells were analyzed from three independent experimental replicates each.For live-cell imaging, U-2 OS cells expressing GFP-G3BP1 were prepared as described above but grown in 12-mm glass-bottom dishes (Thermo Scientific). Drug treatments were added, and cells were imaged on a Nikon Ti-E spinning disk confocal microscope at 37 °C and 5% COD. T. and R. P. conceptualization; D. T. data curation; D. T. formal analysis; D. T. and R. P. funding acquisition; D. T. and R. P. investigation; D. T. visualization; D. T. and R. P. methodology; D. T. writing-original draft; R. P. resources; R. P. supervision; R. P. project administration; R. P. writing-review and editing.


In the present work, a panel of 40 structurally related flavonoids was tested, concerning their ability to inhibit α-amylase activity, using a microanalysis screening system, an inhibitory kinetic analysis and molecular docking calculations. From the obtained results, it was possible to observe that the flavone with a -Cl ion at 3-position of C-ring, an –OH group at 3′- and 4′- positions of B-ring and at 5- and 7- positions of A-ring and the C2 = C3 double bond, was the most active tested flavonoid, through competitive inhibition. In conclusion, some of the tested flavonoids have shown promising inhibition of α-amylase and may be considered as possible alternatives to the modulation of T2DM.α-Amylase has been considered an important therapeutic target for the management of type 2 diabetes   LLC . The following flavonoids were obtained from Indofine Chemical Company, Inc. : A1, A2, A3, A4, A5, A6, B1, B2, B3, B4, C1, C2, C3, C4, C5, C6, C7, C8, D1 (chrysin), D2  and D5. The flavonoids D11, D12, D13, D14, D15, D16and D18 were synthesized according to previous companion papers,α-Amylase from porcine pancreas, 2-chloro-4-nitrophenyl-α-D-maltotrioside (CNPG3), 2HPO4 and 7 mM NaCl, pH 6.8) was exposed to the flavonoids under study (0–200 µM), dissolved in DMSO [final concentration of DMSO of 4.5% (v/v)]. After an incubation time of 10 min, at 37 °C, the reaction was started by the addition of CNPG3 (0.5 mM), dissolved in 20 mM phosphate buffer (pH 6.8) and monitored along 30 min, at 37 °C, in a microplate reader , with the wavelength set to 405 nm. The concentration of α-amylase and the tested flavonoids was determined before the addition of the substrate. Increasing absorbance values obtained within the 5 to 30 min interval (slope) served for the calculation of catalytic rates and the results in the form of inhibition percentages, representing at least three independent experiments. Acarbose (0–16 µM) was used as positive control. The amount of DMSO used did not interfere with the assay. The results of the in vitro inhibitory activity of flavonoids against the pancreatic α-amylase activity are expressed as mean ± standard error of mean (SEM). Statistical comparison between the active flavonoids was estimated by applying the one-way analysis of variance (ANOVA). Differences were considered to be significant at p values lower than 0.05. All the statistical analysis were performed using GraphPad Prism™ .The evaluation of inhibitory effect on α-amylase activity was based on the method described by Trinh et al.vinic and Vmax represent respectively the initial velocity of formation of absorbing CNP in µmol/min and the maximum achievable velocity when for the 0.2 U/mL of enzyme used all catalytic sites are saturated by the substrate, S is the CNPG3 concentration in mM, Km is the Michaelis–Menten constant in mM and Kic, Kiu are the constants for competitive and uncompetitive inhibition processes expressed in µM−1.The estimation of kinetic parameters and prediction of the actual mechanism of inhibition was performed by means of Microsoft Office Excel™ spreadsheets and using the Solver supplement add-in, according to Bezerra et al.B4 (0–200 µM), C5 (0–100 µM), D11 (0–100 µM) and acarbose (0–2 µM). For each concentration of inhibitor, three concentrations of the substrate CNPG3 were tested: 0.25, 0.5 and 1 mM. The obtained results represent at least three independent experiments.To study the enzyme kinetics, nonlinear regression was applied to the nontransformed data for the most active flavonoids of each group: Vmax and the corresponding counterparts estimated according to each model used. Hence, the values obtained for the parameters of the simplest model (without inhibition) were used as initial values, proceeding to competitive inhibition, noncompetitive inhibition, uncompetitive inhibition and finishing with the more complex model, mixed inhibition. The actual mechanism of inhibition was established after comparison and discrimination between the models with a different number of parameters applying the extra sum-of-square F testFor each tested condition, the best guesses for equation parameters were thrown by Solver tool after iterative minimization of the sum of squared residuals between the experimental values of The jackknife procedure was applied to determinate the error of the kinetic constant values. It consisted of the calculation of standard deviation of all estimates guessed by Solver when each experimental data point was in turn drawn from the initial setSus Scrofa α-amylase (E.C. 3.2.1.1.). From these, it was chosen the PDB entry 1HX0in situ. The K2 unit bound at the active site was used to compare the binding mode of the tested flavonoids and as a template to model the substrate for receptor minimization and conformational sampling.Seventeen entries in the Protein Data Bank (PDB) correspond to Ka, at pH = 7.The protonation states of the enzyme were predicted using the Protoss serverFrom the hexasaccharide compound present in 1HXO , the substrate maltohexose was modelled. On this system, Molecular Mechanics minimization was performed, followed by 20 ns of classical molecular dynamics (MD) simulation to sample receptor conformations. This protocol was performed within the AMBER 12 suite50 was reproduced. The conformation of the receptor with the best performance was obtained after 3.3 ns of simulation.To choose and validate the conformation of the receptor for the docking protocol several receptor conformations were evaluated: the X-ray conformation, the conformation of the receptor minimized with maltohexose and four representative conformations of the clustered MD simulation. The suitability of each conformation was assessed by evaluating if the docking of the inhibitor acarbose in the active site reproduced the X-ray conformation and how well the ranking of the tested flavonoids with the lowest ICThe binding mode of the selected flavonoids was predicted by means of the AutoDock VinaSubsequently, the poses predicted by AutoDock Vina for each flavonoid were scored using the scoring function GoldScore within the GOLD software suiteThe tested flavonoids were divided into five groups (A–E), according to their substitution pattern. The substitution pattern of flavonoids from the A group differs at 3-position of the C-ring and at 3′- and 4′-positions of the B-ring; flavones from B group have an –OH group at 5-position of the A-ring and different substitutions at 3′- and 4′-positions of the B-ring; flavonoids from C group show –OH, –OMe and/or –OBn groups at 3-position of the C-ring, at 3′- and 4′-positions of the B-ring and at 7- and 8-position of the A-ring; flavonoids from D group present –OH substitutions at 5- and 7- positions of the A-ring and also present –OH, –OMe and/or –Cl substitutions at 3-position of the C-ring, at 2′-, 3′-, 4′- and 5′-positions of the B-ring and at 6- and 8-positions of the A-ring; flavanones from E group have –OH groups at 5- and 7-positions of the A-ring and their substitution pattern differs in the presence or absence of –OH groups at 3′- and 4′-positions of the B-ring .No relevant inhibitory activities were observed, up to the highest tested concentration (200 µM), for the flavones belonging to A group with different substitutions in 3-position of C-ring and in 3′- and 4′-positions of B-ring.B4, presenting an IC50 value of 148 ± 5 µM. This result demonstrates that the presence of the catechol group at B-ring translated in a relatively slight increase of the inhibitory effect over α-amylase catalysis when compared to the other flavones of this group. The presence of an –OH group at 5-position of A-ring also seems to contribute to such effect, since flavone B4 was more active than flavone A4 .For flavones of B group, with an –OH group at 5-position of A-ring and different substitutions in 3′- and 4′-positions of B-ring, the most effective was C5, tested here for the first time, with an IC50 value of 59 ± 4 µM. It seems that the presence of –OH groups at 3-position of C-ring, at 4′-position of B-ring and at 7-position of A-ring, is accompanied by a significant inhibitory effect on α-amylase. On the other hand, the presence of -OMe groups in the flavonoid scaffold  did not bring any advantage for the intended effect, since the initial conversion rates of the CNPG3 substrates remained unchanged even for the highest tested concentration (200 µM).From C group, which includes flavonoids with different substitutions at 3-position of C-ring, at 3′ and 4′ positions of B-ring and at 7 and 8 positions of A-ring, the most active flavonoid was compound D11, with an IC50 value of 44 ± 3 µM. Thus, the presence of a –Cl ion at 3-position of C-ring, as well as the presence of a catechol in B-ring and –OH groups at 5- and 7-positions of A-ring, showed to be crucial for the inhibition of α-amylase activity. Comparing flavonoid D11 with D8 (quercetin) (IC50= 138 ± 5 µM), it is clear that the substitution of the Cl- ion for an OH-group at 3-position of C-ring results in a significant decrease of the corresponding inhibitory activity. It was also observed that the presence of a -OMe group at 3′- position of B-ring [D16 (chrysoeriol)] slightly improves the ability to inhibit α-amylase activity (IC50= 192 ± 7 µM), comparing with D17 (acacetin), which presents a -OMe group at 4′-position of B-ring. An increase of the number of -OMe groups in the structure of flavonoids is not favourable for the intended effect, as it can be concluded from a comparison of D16 (chrysoeriol) with D18, the later with an extra -OMe group at 4′-position of B-ring. A glycosylated flavonoid, D19 (rutin), was also tested, but no activity was found up to the highest tested concentration (200 µM).All members of D group have –OH groups at 5- and 7-positions of A-ring and the substitution pattern varies according to the presence or absence of –Cl ions, –OH and –OMe groups in their structure. The most effective flavonoid found was the chlorinated flavone E1 (naringenin), E2 (eriodictyol) and E3 (taxifolin) were not able to inhibit α-amylase activity, at the maximum tested concentration (200 µM). These results demonstrate that the presence of C2 = C3 double bond is very relevant for the intended effect.The studied flavanones of E group, B4, C5 and D11, respectively, and the positive control, acarbose, were henceforth selected for their type of inhibition calculation. No flavonoids from A and E group were tested, due to their lack of inhibitory activity.In accordance with the described results, the most effective flavonoids of B, C and D groups, F test and the AIC test, it was possible to select the best model . In that sense, the model with the lowest sum of the square error value corresponds to the best kinetic model. The comparison between models with a different number of parameters can be done by comparing the extra sum-of-square F test, at the desired level of probability (ƒ 0.95): if the –ƒ value is superior to 0, the more complex model should be applied. The Akaike test was also used to discriminate the different enzyme inhibition models, with a different number of parameters: the model with the lower corrected AIC (AICc) score is the model more likely to be correct.By comparing the sum of the square errors values among the different enzyme inhibition models and, for models with different number of parameters, applying the extra sum-of-square acarbose  is a mixed-type inhibitor, and flavonoids B4 , C5  and D11 are competitive inhibitors .By the obtained results, it was possible to conclude that Vmax, Km, Kic and/or Kiu)  . The tesB4, C5 and D11 derivatives, the ones exhibiting a higher inhibitory effect in their groups.Once evidenced the inhibition mechanism through experimental data and calculations using Solver supplement of Microsoft Excel Office™, it was predicted the binding pose of flavonoids within the active site of α-amylase. Special attention is given to The binding pocket of α-amylase is usually divided in –1 to N subpockets, each one creating a particular environment around each sugar ring. The innermost subpocket is numbered as –1 subpocket and is immediately followed the +1 subpocket while the glycosidic bond to be cleaved stays positioned in-between the sugars and also in-between the –1 and +1 subpockets. The next subpockets are numbered +2, +3, etc. An even deeper subpocket (–2) can be occupied by inhibitors, but generally not by natural substrates.Bacillus subtilis, PDB structure 1UA7, and resolution 2.21 Å). We deleted the acarbose hexasaccharide from the 1UA7 PDB file and re-docked it into the active site, following the protocol described in the methods, using the same parameters, except for the size of the box . The top-ranking solution of the docking assay displayed a very similar pose to that of acarbose in the crystallographic structure . Root mean square deviation (RMSD) analysis of the heavy atoms in the docking solution concurs with visual inspection, with a very low heavy-atom RMSD value of 0.482 Å, confirming that the used protocol has significant predictive power.We started by evaluating the accuracy of our Molecular Docking Protocol. For this purpose, we performed a docking assay using the crystal structure of alpha-amylase complexed with acarbose , but the nucleophilic Asp197 is only at 3.3 Å from the anomeric carbon, Asp300 is hydrogen bonded to the 2-hydroxyl and 3-hydroxyl groups of the glucose unit of the –1 subpocket. An additional hydrogen bond is established between the hydroxymethyl group and Asp197.The study started with the prediction of the binding mode of acarbose, which in the chosen receptor perfectly reproduces the binding interactions of the substrate maltohexose seen in the MD simulation . This waThe most relevant interaction between the glucose unit in the +1 subpocket and the receptor seems to be the hydrophobic interaction between its methyl group and a hydrophobic pocket formed by Leu162 and Val163, and a hydrogen bond between the 3–OH and the backbone of Glu233. For the glucose unit at the +2 subpocket, the most relevant interaction seems to be a hydrogen bond between the 3–OH group and the side-chain of Tyr151. The glucose unit at the +3 subpocket is completely exposed to the solvent and does not interact meaningfully with the receptor.A1 to A5, the –OH groups at 3-position of C-ring and at 3′-position of B-ring establish hydrogen bonds with Asp300, while the –OH group at 4′-position of B-ring establishes hydrogen bonds with Asp197, with this hydrogen bonding network being optimized in flavonoid A5.Among flavonoids from A group, which carry substitutions at the 3-position of C-ring and at 3′- and 4′-positions of B-ring, a clear trend is observed: from flavonoids A1 to A5 bind the active site with the B-ring oriented towards the catalytic triad , occupying a site equivalent to the –1 subpocket of the K2 inhibitor in the PDB structure 1HXOTherefore, flavonoids A1 to A3 flavonoids, the described set of hydrogen bond interactions is not optimized and as such, they do not bind so deep in the active site. Instead, aromatic stacking interactions are observed between the A- and C-rings and Trp58, and between the B-ring and Tyr62.For the less substituted A4 and A5) the hydrogen bonds with the catalytic triad are optimized and these flavonoids bind deeper in the active site, hydrogen bonding with Asp197, Glu233 and Asp300, which might explain the higher inhibitory activity observed for these two flavones within A group  significantly decreased the inhibitory activity towards α-amylase (138 ± 5 µM). The comparison of flavone D11 with D14 show that addition of a –Cl ion at 8-position of A-ring, led to a twofold increase of the IC50 value (82 ± 4 µM). Thus, the presence of a –Cl ion at 3-position of the C-ring is especially advantageous for the ability of flavonoids to inhibit α-amylase activity.In D group we found the most active compound of this work, flavone D9 (myricetin) (IC50 = 107 ± 6 µM) and D8 (quercetin) (IC50 = 138 ± 5 µM), it was possible to observe that the addition of an –OH group at 5′-position of B-ring slightly enhanced α-amylase inhibition. The results of the present work are in accordance with those reported by Meng et al.Through the comparison of the results obtained for flavone D7 (luteolin) (IC50= 78 ± 3 µM) and D8 (quercetin) (IC50 = 138 ± 5 µM), it was also possible to conclude that the addition of an –OH group at 3-position of C-ring decreased the flavonoids’ inhibitory activity towards α-amylase. The obtained results are in agreement with the study of Tadera et al.By comparing D16 (chrysoeriol) and D18 with D7 (luteolin), and D17 (acacetin) with D5 (apigenin). These results are in agreement with previous studies found in literature,Comparing methoxylated flavonoids with their parent hydroxylated flavonoids, it is possible to conclude that the presence of –OMe groups decrease α-amylase inhibition, as it can be seen comparing flavone D19 (rutin), and the presence of a sugar in the flavonoid structure did not favour its ability to inhibit α-amylase activity. In this sense, the obtained results showed that the substitution of an –OH group [D8 (quercetin)] by a rutinose [D19 (rutin)] at 3-position of C-ring reduced the α-amylase inhibition by flavonoids. Accordingly, it was previously described in the literature that the glycosylation of flavonoids decreases their inhibitory effect against α-amylase, possibly due to the increasing molecular size and polarity, and to the nonplanar structure,,A glycosylated flavonoid was also tested, E1 (naringenin) with D5 (apigenin), E2 (eriodictyol) with D7 (luteolin) and E3 (taxifolin) with D8 (quercetin), it was observed that the lack of the C2 = C3 double bond did not favour the intended effect. This conclusion is in agreement with Tadera et al.,,,,The flavanones from E group did not inhibit α-amylase, up to the highest tested concentration (200 µM). Comparing Actinoplanes sp. fermentation and its structure comprises a hydroxymethyl conduritol residue α- linked to a 4-amino-4,6-dideoxyglucose which is, in turn, α- linked to maltose,50 values found for the tested flavonoids were higher than the IC50 value found for acarbose, which is 1.3 ± 0.2 µM. The mild pancreatic α-amylase inhibition prevents the abnormal bacterial fermentation of carbohydrates in the colon and consequent gastrointestinal adverse effects, such as abdominal distention, flatulence and diarrheaAcarbose is a pseudotetrasaccharide produced by B4, C5 and D11) and the positive control, acarbose, to test their type of inhibition.After the study of α-amylase inhibition by flavonoids, we selected the most active flavonoid of each group –5(S)], and flavonoids B4 , C5 –15(S)) and D11  present a competitive inhibition. Accordingly, Kim et al.B4, C5 and D11 were studied here for the first time.In accordance, kinetic analysis consisted in the sequential fitting of available models , and in the estimation of the kinetic constants (parameters) values. It was possible to observe by the analysis of the sum of the square errors values from the different models, and by the application of F-test and AIC test, that acarbose and for flavonoids B4, C5 and D11, the kinetic constants were estimated: Vmax, Km, Kic and Kiu. The calculation of the Ki value is considered a useful tool to compare the potency of inhibitors. The Kic values of the selected compounds demonstrated the following order: B4>C5>D11>acarbose. This means that, as expected, acarbose has the highest binding affinity to α-amylase, followed by flavonoids D11, C5 and B4.Once defined the best kinetic model for The prediction of the binding mode of the most active flavonoids towards α-amylase in each group showed that one of the most important aspects shared by them is a motif bearing a carbonyl group next to a –OH group. This allows the carbonyl group to accept a hydrogen bond from Glu233 and the –OH group to donate a hydrogen bond to Asp300 in the region between the –1 subpocket and +1 subpocket. The interactions with these groups can be further enhanced with a hydrogen bond between His299 and the -OH group.The relative positioning of the -OH and carbonyl groups determine, to a great extent, the orientation of the flavonoid. Thus, the flavonoid part on the side of the carbonyl will be preferably oriented towards the +1 subpocket, while the part on the -OH side will be preferably oriented towards the –1 subpocket.Hydrophobic groups seem to be more tolerated on the –1/–2 subpockets than on the +1/+2 subpockets, where they can interact with Tyr62, Leu162, Val163 and Leu165. Hydrophobic groups on the +1/+2 subpockets, on the other hand, can establish hydrogen bonds with Tyr151 and His201.Halogen substitutions or hydrophobic groups might be used next to the carbonyl to explore the interaction with Ile235 and Phe256, as the best flavonoid tested herein shows.D11. As such, the presence of an –OH group at 5- and 7-positions of A-ring and in the 3′- and 4′-positions of B-ring, and the presence of a –Cl ion at 3-position of C-ring, as well as the C2 = C3 double bond, have shown to be favourable for the intended effect  was tested, in order to establish an accurate structure-activity relationship. For that purpose, the kinetic analysis, the inhibitory mechanism determination and the corresponding molecular docking calculations were applied, to explain the binding model of the most effective flavonoid of each group to the three-dimensional structure of α-amylase catalytic site. Thus, from the obtained results, it may be concluded that α-amylase inhibition by flavonoids is strongly dependent on the nature and position of the substituents. The most effective flavonoid was the chlorinated flavone d effect . This fl


Stable water isotope records from Antarctica are key for our understanding of Quaternary climate variations. However, the exact quantitative interpretation of these important climate proxy records in terms of surface temperature, ice sheet height and other climatic changes is still a matter of debate. Here we report results obtained with an atmospheric general circulation model equipped with water isotopes, run at a high-spatial horizontal resolution of one-by-one degree. Comparing different glacial maximum ice sheet reconstructions, a best model data match is achieved for the PMIP3 reconstruction. Reduced West Antarctic elevation changes between 400 and 800 m lead to further improved agreement with ice core data. Our modern and glacial climate simulations support the validity of the isotopic paleothermometer approach based on the use of present-day observations and reveal that a glacial ocean state as displayed in the GLAMAP reconstruction is suitable for capturing the observed glacial isotope changes in Antarctic ice cores. Despite their importance, the accuracy of the quantitative interpretation of Antarctic ice core stable water isotope records remains a matter of debate. Here, the authors use an isotope-enabled atmospheric general circulation model to test and validate the isotopic paleothermometer approach. For these polar ice cores, stable water isotopes  belong to the key set of analysed climate proxy variables. Such stable water isotope records from the various Antarctic ice cores provide high-resolution information on climate variability at decadal to glacial–interglacial time scales6.Antarctic ice cores provide a wealth of information on past atmospheric composition and climate7 as well as Rayleigh distillation models8 and atmospheric general circulation models equipped with stable water isotopes diagnostics11. The strong linear spatial relationship between annual mean surface air temperature and δ18O today12 with a slope of 0.8‰/°C between the δ18O and local temperatures is fully consistent with theoretical Rayleigh distillation13 and is well captured by atmospheric general circulation models equipped with stable water isotopes (isoGCM), despite model biases for temperature or precipitation in Antarctica15. This relationship has therefore been commonly applied to estimate past changes in temperature from Antarctic ice cores18.Since the 1960s, the relationships between Antarctic stable water isotopes and temperature have been explored using surface measurementsδ18O changes with the same quantitative relationship. Indeed, several processes may affect the temporal isotope–temperature relationship, such as changes in evaporation conditions, atmospheric transport pathways, changes in condensation vs. surface temperature associated with polar boundary layer processes, or changes in precipitation intermittency or seasonality. In Greenland, where alternative paleothermometry methods are available from ice cores, temporal slopes vary from 0.3 to 0.6‰/°C20, much lower than the local present-day spatial slope of about 0.7‰/°C21, an up to a factor 2 difference attributed to changes in precipitation seasonality as well as moisture sources and transport paths23.However, the consistency between present-day observations, distillation theory and isoGCM simulations is no proof that past temperature changes should scale to past precipitation δ18O-temperature relationship has been explored using deuterium excess measurements26 as well as low-resolution isoGCMs using simulations performed for colder than present-day climate27, including water tagging28, or warmer than present day climate projections29. The conclusions from these studies were that under high CO2 and warmer than present conditions, there may be changes in temperature-precipitation covariance accounting for a smaller isotope–temperature slope and for varying isotope–temperature relationships for different ice core sites on the East Antarctic plateau30. Glacial isotope–temperature relationships appear rather homogeneous in the East Antarctic plateau and moisture-source effects had limited impacts on glacial–interglacial temperature reconstructions. However, these isoGCM studies were performed with low-resolution atmospheric models and were not based on the latest reconstructions of ice sheet, sea ice and ocean sea surface temperature changes. Processes associated with moisture transport may not be properly resolved using atmospheric models with a resolution of 200 × 200 km31. Moreover, recent ice core data from West Antarctica have expanded the documentation of glacial–interglacial changes in ice core δ18O and evidenced different amplitude and timing of changes in different areas32, challenging the homogeneity of isotopic changes initially reported in the East Antarctic plateau33, where regional amplitudes also vary through time34. These new findings motivate further investigations of glacial–interglacial changes in isotope–temperature relationships in Antarctica using isoGCMs with an increased spatial resolution.In Antarctica, the importance of several processes that could distort the 39. Here, results of isoGCM simulations with prescribed different ice sheet reconstructions are compared with ice core data from West and East Antarctica. The deviation between simulated water stable isotopes and ice core records provides a constraint on the realism of LGM ice sheet reconstructions.For such isoGCM simulations of the climate of the last glacial maximum (LGM), major uncertainties in required boundary conditions are the prescribed sea surface temperature, sea ice extent and ice sheet topography, while orbital parameters and GHG are well known for the LGM period. Large uncertainties remain associated with the knowledge of glacial West Antarctic ice sheet topography, evidenced by the large spread of existing reconstructionsδ18O values (see Methods for details). This PD climate simulation shows a good agreement between observed and simulated Antarctic surface temperatures (Tsurf) with a correlation coefficient of 0.94 despite an overall warm bias, which grows from 0 to 5 °C in the temperature range of −20 to −35 °C  model boundary conditions with an observational-based distribution of ocean surface  °C Fig. . Such a ias Fig. . Simulature Fig. . ConsistWe have performed six different LGM simulations to evaluate the influence of different boundary conditions on the simulated temperature and isotope changes in Antarctica. The main focus has been put on recent glacial Antarctic ice sheet reconstructions. Furthermore, the influence of different prescribed sea surface temperatures as well as different patterns of the isotopic composition of the surface ocean has been investigated (see Methods).δ18O data from the following 11 ice cores: Vostok, Dome F, Dome B, EDC, EDML, Taylor Dome, Talos Dome, Byrd, Siple Dome, Law Dome and WDC . It is important to note that mean LGM values in Table 32. However, as our idealised LGM simulation has been performed with boundary conditions of 21 ka B.P. (see Methods), we have chosen to use this identical time interval at the end of the last glacial from all isotope records as most appropriate for our model data comparison, using the synchronised Antarctic Ice Core Chronology 2012 (AICC201244) for the Vostok, EDC, EDML and Talos Dome ice cores and the latest individual ice core chronologies for the other sites.In order to evaluate the quality of the different isotopic simulations, we have compiled δ18O changes over the Antarctic continent. The smallest change of −4‰ is found at Taylor Dome43. East Antarctic ice cores  show LGM decreases of δ18O in the range of −4.8 to −5.6‰. A much stronger depletion in the range of −7.3 to −7.8‰ is reported for the West Antarctic sites of Byrd, Siple Dome and WDC. The LGM decrease at the EDML site (−6.3‰) is ranked in between the smaller East Antarctic and larger West Antarctic glacial changes. This change includes a correction for upstream effects at the EDML site, which is a source of uncertainty as it depends on glaciological modelling of past Antarctic ice sheet topography25.Ice core data indicate non-uniform LGM δ18O, ranging from −2 to −15‰  as well as Byrd and WDC . These results are noteworthy as the validity of the PMIP3 Antarctic LGM ice sheet topography has been debated38. In West Antarctica, recently measured borehole temperatures at the WDC site indicate a modest LGM ice sheet height change in the range of −300 to +450 m45. A modest increase is in better agreement with the ICE-6G_C and GLAC-1D ice sheet reconstructions . Our results indicate that the stronger LGM cooling and δ18O changes for all 11 ice core sites is used as a measure to compare our LGM reference simulation with the additional simulations with alternate ice sheet reconstructions. We find that the PMIP3 ice sheet results in an overall best δ18O agreement with the ice core data (Table The root mean square error (RMSE) between modelled and measured LGM-PD ta Table . RMSE isδ18O changes at the WDC, Byrd and Siple Dome drilling site  and Byrd (−0.7‰/100 m) match the simulated PD spatial slope (−0.8‰/100 m), while a 50% lower slope is found for Siple Dome (−0.4‰/100 m). For a 'best guess' of LGM ice sheet height changes, we combine the simulated δ18O–height relations with the glacial δ18O changes measured in the three ice cores and assume a potential underestimation of the real δ18O–height relation by up to 20% in our simulation , suggesting that cooler and more uniform ocean surface boundary conditions than the ones used in our LGM reference simulation are less plausible  as compared to the older ICE-5G data set used in the ECHAM4 study. Third, refined model resolution: In Werner et al.15, we have already demonstrated that a higher spatial model resolution is key for an improved simulation of the isotopic composition of precipitation. To quantify this effect for glacial changes in Antarctica, we have repeated both our PI and LGM reference simulation with a coarse T31 spectral resolution . For the selected ice core sites, the RMSE increases by only +0.3‰ as compared to our reference simulations . The coarser model resolution leads to a lowered glacial ice sheet change and thereby to a better agreement with the ice core data, especially over West Antarctica. However, for both the PD climate and LGM climate, the simulated absolute δ18O values in precipitation are much more enriched  for this coarse model resolution and do not fit the ice core data well.Overall, we rate our LGM reference simulation with a prescribed PMIP3 ice sheet reconstruction, GLAMAP ocean state and GISS ocean isotope distribution as the best choice for analysing the validity of the isotope paleothermometer in Antarctica  as a surrogate of the temporal slope to interpret ice core isotopic records. Such a model correctly accounts for the observed present-day linear relationship between the isotopic composition of snow and the temperature of the site8 and predicts similar slopes for present-day and LGM conditions. The same approach of comparing observed, present-day and LGM slopes can be followed from our isoGCM simulations. First, the observed linear relation between present-day values of δ18O in snow and Tsurf is well simulated by ECHAM5-wiso (see Supplementary Note m = 0.77 ± 0.01‰/°C) is very close to the observed relationship (m = 0.79 ± 0.01‰/°C). The observed and simulated spatial slopes are also very close to Rayleigh distillation lines in both West and East Antarctica, when they are considered separately . Simulated LGM spatial slopes for both West and East Antarctica are similar to the simulated present-day ones. Despite the much stronger simulated LGM cooling in West Antarctica, the modelled δ18O-Tsurf slope in this region (m = 0.88 ± 0.01‰/°C) follows the same distillation line as simulated in East Antarctica (m = 0.85 ± 0.01‰/°C).Initially, simple Rayleigh-type isotopic models have been applied to justify the use of the observed present-day Tsurf-δ (δ18O or δD) slope observed over a large area around the ice core site. In this line, we have divided Antarctica in six large sectors  prior to the calculations of the temporal slopes27. Over the six sectors, the temporal slope is slightly—but consistently—lower than the present-day observed and modelled spatial slope, by 17–26%. Using the isotopic paleothermometer based on PD observations, therefore, implies an underestimation of LGM-PD temperature change by this amount. Daily temperatures weighted by precipitation amount should in principle lead to a better fit between annual mean isotope and temperature changes, as the link between these two quantities is only expected to hold during precipitation events. When calculating the slopes using precipitation-weighted temperature, we indeed obtain very similar slope values between modelled PD slopes and temporal slopes, with deviations lower than 8% for five of the six Antarctic sectors (see Supplementary Note 51). This holds also true for spatial slopes, which should be ideally based on precipitation-weighted temperatures. However, a precipitation weighting cannot be applied when using observations, only.Antarctic ice core data are generally interpreted using the present-day spatial ors Fig.  and compδ18O and temperatures for calibrating the Antarctic isotopic thermometer. For our reference simulation, we find seasonal slopes which are substantially lower than the simulated spatial slopes both for present-day and LGM conditions. These lower slopes are in agreement with observations and would imply to an unrealistic strong LGM cooling in Antarctica  and the observed present-day spatial slope. The results are listed in the second last column of Table 27 with however a slight systematic underestimation, by about 15%, of modelled LGM cooling. Our findings thus expand the conclusions of a previous study55, suggesting that the isotope-based approach  slightly underestimates glacial temperature changes on the East Antarctic Plateau, to the whole Antarctic continent despite the different characteristics of the various isotopic records in West and East Antarctica during the last glacial maximum and deglaciation32. These findings hold also true despite the overestimation of the glacial West Antarctic ice sheet changes in the PMIP3 reconstruction, as temperature and δ18O changes are both strongly linked to the prescribed height of the glacial ice sheet. It is noteworthy that such match of the spatial and temporal slopes may not hold for a shift to a warmer climate in Antarctica30, and neither for glacial and last deglaciation δ18O-Tsurf slopes in Greenland58.Our simulations support the validity of the modern analogue hypothesis for the Vostok and EDC record based on previous findings derived from ECHAM4-wiso simulationsδ18O values in surface snow. As for many other AGCMs41, a warm model bias still exists over the Antarctic continent, despite the chosen high-spatial model resolution. Simulated LGM δ18O anomalies are also in good agreement with ice core data. The observed stronger depletion of water isotopes in West Antarctic deep ice cores as compared to East Antarctic ones can be related to a stronger LGM cooling of the former. This larger cooling and isotopic depletion is attributed to the prescribed glacial ice sheet elevation changes. Our model results with prescribed different LGM ice sheet reconstructions of Antarctica indicate an overall good match of the modelled glacial δ18O changes for the PMIP3 reconstruction. The suggested West Antarctic elevation changes seem to be too large by 30–50% at the Byrd and WDC drilling site, though.We conclude that the ECHAM5-wiso simulation results are in good agreement with observed present-day Antarctic surface temperatures and δ18O-Tsurf slopes are in good to very good agreement on a large geographical scale, i.e., for East and West Antarctica, as well as on a smaller sector scale for the present-day climate. It should be noted though that the sector scale agreement might depend on the choice of the spatial region29. For the LGM, modelled spatial slopes are almost identical to the present-day ones. Analyses of the different ECHAM5-wiso simulations indicate that the LGM present-day temporal δ18O-Tsurf slope is slightly lower as compared to the mean present-day spatial value for all regions of Antarctica. Thus, in accordance with previous findings for a few selected East Antarctic sites, the classic δ18O paleothermometer approach is supported by these new ECHAM5-wiso model results for the whole Antarctic continent. Applying the mean spatial δ18O-T slope for calculating last glacial maximum temperature changes from Antarctic ice core records might lead to a systematic underestimation, on average of around 15%, only.Observed and modelled spatial 59. Our simulation results might also help to constrain estimates of past temperature changes from some of these upcoming new ice core δ18O data.In this study, we do not only compare our model results to already published Antarctica ice core data, but also report model findings for some of the more recent, ongoing and future drill sites. Further deep-drilling projects are already in progress, e.g., the search for finding 1.5 million years old ice in Antarctica216O, H218O and HDO15. This model has its origin in the fifth version of the atmospheric general circulation model ECHAM560 and is enhanced by including a water isotope module in the model’s hydrological cycle, following the work of Joussaume et al.9, Jouzel et al.10 and Hoffmann et al.11. The isotope module computes changes of the composition of different water masses within the entire hydrological cycle, including evaporation from the ocean, cloud condensation, precipitation, surface water reservoirs and river runoff15.ECHAM5-wiso is an atmospheric general circulation model that also explicitly allows the simulation of the three isotopic water species H61, including prescribed monthly climatological sea surface temperatures (SST) and sea ice cover for the period 1979–1999. The isotopic composition of surface seawater is prescribed using the global gridded data set of LeGrande and Schmidt47. As no equivalent data set of the δD composition of seawater exists, the deuterium isotopic composition of the seawater in any grid cell has been set equal to the related δ18O composition, multiplied by a factor of 8, in accordance with the observed relation for meteoric water on a global scale62.Two different simulations were performed for the present-day climate. For our PD reference simulation, orbital parameters and GHG concentrations are set to modern values. For the prescribed ocean surface state, we used AMIP-style boundary conditions46. For this simulation, the modelled pattern of δ18O in seawater is in agreement with the data set of LeGrande and Schmidt47 on a global scale, but regional differences of up to ±0.4‰ exist.In a second PD simulation, we use the same boundary conditions as for the reference one, but vary the prescribed isotopic composition of surface seawater by using results obtained with a fully-coupled ocean–atmosphere GCM including isotope diagnosticshttps://pmip3.lsce.ipsl.fr). Prescribed glacial climatological monthly sea ice coverage and SST changes are based on the GLAMAP data set63, which combined the global CLIMAP64 ocean state reconstruction with more recent LGM temperature and sea ice estimates of the Atlantic65. A uniform glacial enrichment of sea surface water and sea ice of +1‰ (δ18O) and +8‰ (δD) on top of the present-day isotopic composition of surface seawater has been applied.For the LGM climate, six different simulations have been performed. For all simulations, the set-up of orbital parameters, greenhouse gas concentrations (GHG), and land-sea distribution follows the guidelines of the Paleoclimate Modelling Intercomparison Project Phase III . In ICE-5G, a less (stronger) increase of LGM ice sheet height in West (East) Antarctica as compared to the PMIP3 reconstruction was suggested38. In two further simulations, we prescribe two more recent glacial ice sheet reconstructions, ICE-6G_C39 and GLAC-1D69. These two reconstructions will be used within the framework of the upcoming PMIP4 simulations70. Both reconstructions suggest the least ice sheet height and extension changes in West Antarctica, and also slighlty lower changes of the glacial East ice sheet height as compared to the PMIP3 reconstruction.In a second LGM simulation, we have prescribed the ice sheet reconstruction ICE-5G46. Together with the corresponding PD simulation it allows us to estimate the influence of different prescribed δ18O values in seawater on the simulated glacial δ18O changes in Antarctic precipitation.In a fifth LGM simulation, we vary the prescribed isotopic composition of surface seawater by using results obtained with a fully-coupled ocean–atmosphere isoGCM71. This simulation is used to explore the dependence of simulated glacial δ18O changes in Antarctic precipitation on the prescribed glacial ocean surface state.For a sixth LGM simulation, we have replaced the GLAMAP-based glacial sea ice coverage and SST changes by values derived from an LGM climate study with the fully-coupled ocean–atmosphere model set-up ECHAM5/MPIOM-wisoFor a factor separation analysis of ice sheet height versus other glacial boundary conditions on the simulated isotope signal in Antarctica, we have performed two further sensitivity experiments. In the first sensitivity experiment, we use present-day boundary conditions but increase the Antarctic ice sheet height to PMIP3 LGM values. In the second one, we use all LGM boundary conditions as in our LGM reference simulation but decrease the Antarctic ice sheet height to present-day values. These two experiments, combined with the PD and LGM reference simulations, allow us to separate the effect of glacial Antarctic PMIP3 ice sheet height from other glacial boundary conditions .15. All ECHAM5-wiso simulations in this study have therefore been run using the high-spatial T106 model resolution  with 31 vertical model levels between surface and 10 hPa. Reported model results are mean values of the last 10 simulation years, with a total simulation period of 22 years for the present-day and LGM reference simulation, and 12 years for all other experiments, respectively. If not stated otherwise, all model mean δ values in this study are calculated as amount-weighted averages.Former tests with ECHAM5-wiso had evidenced the sensitivity of model performance for Antarctic stable water isotope distribution to model resolution, and an overall consistency with observations at T106, probably because this resolution allows a reasonable representation of moisture transport within stormsSupplementary InformationReviewers' comments