Bacillus subtilis CF-3 and their biocontrol effects on common fungal pathogens were researched in this study. The results showed that the VOCs in 24-h fermentation liquid (24hFL) of B. subtilis CF-3 inhibited mycelial growth of Botrytis cinerea, Colletotrichum gloeosporioides, Penicillium expansum, Monilinia fructicola, and Alternaria alternata, with a mean inhibition rate of 59.97%. The inhibitory effect on M. fructicola and C. gloeosporioides was the highest; they were therefore selected as target fungal pathogens for further experiments. Based on headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), 74 potential VOCs were identified during the fermentation: 15 alcohols, 18 ketones, 4 pyrazines, 4 esters, 10 acids, 5 phenols, 3 hydrocarbons, 3 amines, 2 aldehydes, 5 ethers, and 5 other components. At different fermentation times, the type and content of VOCs were different. Most of the potential VOCs (62 VOCs) were identified in the 48hFL. The inhibition rates of all VOCs reached their peaks (73.46% on M. fructicola and 63.63% on C. gloeosporioides) in the 24hFL. Among the identified VOCs, 2,4-di-tert-butylphenol, 1-octanol, and benzothiazole showed significant positive correlations with the rates of M. fructicola and C. gloeosporioides inhibition. Benzoic acid and benzaldehyde showed a significant positive correlation with the rates of M. fructicola inhibition, and anisole and 3-methylbutanal showed a significant positive correlation with the rates of C. gloeosporioides inhibition. In vitro, 2,4-di-tert-butylphenol showed a strong inhibitory effect on both M. fructicola and C. gloeosporioides. In vivo, benzothiazole showed the strongest inhibitory effect on the mycelial extensions of both M. fructicola and C. gloeosporioides, which also led to an increased rate of healthy fruit. The results of the present study clarified that 2,4-di-tert-butylthiophenol and benzothiazole are key inhibitory VOCs produced by B. subtilis CF-3.The dynamic changes of the levels of volatile organic compounds (VOCs) produced by  Postharvest decay caused by fungal pathogens is one of the main factors limiting the storage life of fruit, and it results in substantial economic losses, especially in the fruit market supply chain Prusky, . The conAureobasidium pullulans  . Recently, VOCs produced by B. subtilis have been proposed as an alternative control method for postharvest fruit diseases  to identify the dynamic changes in the levels of VOCs secreted by B. subtilis CF-3 during fermentation using headspace solid-phase micro-extraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS); (3) to discover the correlation between compounds and inhibition rates by evaluating the dynamic inhibition rates of VOCs secreted from B. subtilis CF-3; and (4) to ascertain which compounds were key factors in the inhibition of fungal pathogens both in vivo and in vitro.In a previous study, we isolated, identified, and named the Bacillus subtilis CF-3  was isolated from fermented bean curd and identified by the Laboratory of Food Safety and Quality Control   agar  medium /mL were counted using a hemocytometer. Afterwards, 4.15 mL of the prepared B. subtilis CF-3 liquid was transferred into 41.5 mL of fresh culture solution, which was then sealed with a silicone pad and aluminum cap in a custom-made conical flask with side arms bound with eight layers of sterile gauze , and the cell concentration was adjusted to the approximate cell concentration of the 24hFL. The CFF and BS were selected to explore whether the bacterium or its secretions have a stronger inhibitory effect on the fungi.After 24 h of incubation on LB agar, g et al. . A bactePrunus persica cv. DaJiubao) and litchi (Litchi chinensis Sonn.) were harvested in orchards in the Shandong and Guangxi provinces, China, respectively. After being harvested and transported to the laboratory, the fruits were processed immediately. Fruits were selected based on size uniformity, ripeness, and the absence of visual injuries or infections. Subsequently, they were superficially disinfected by immersion in 0.1% (v/v) sodium hypochlorite for 1 min, rinsed with tap water, and air-dried at room temperature (25°C).Peaches (5 conidia/mL using distilled water.Pathogenic fungi were prepared as spore suspensions by flushing the surface of the PDA medium containing Tween 80 diluted in distilled water to 0.05% (v/v) that was used for cultivating the fungi for 7 days at 28°C. The liquid was then filtered through eight layers of sterile cheesecloth, and the concentration was adjusted to 1 × 10B. subtilis CF-3 were used as three treatments, and A. alternata, B. cinerea, M. kuwatsukai, P. expansum, C. gloeosporioides, and M. fructicola were selected as the target pathogenic fungi for the experiments. The inhibition rate was evaluated by mycelial growth of the fungi. The inhibitory effects of VOCs produced by fermentation and the components of the fermentation liquids of B. subtilis CF-3 were evaluated using the methods described by Arrebola et al.  or TPS (for BS of the 24hFL) were used as negative controls. The mycelial diameter (mm) of the fungi was measured after 7 days. There were three replicates for each treatment, and the experiment was repeated three times.The 24hFL, CFF of the 24hFL, and BS of the 24hFL of a et al. . In briea et al.  using thin vitro (R) was calculated using the equation:R is the percent of inhibition of mycelial extension; D1 is the mycelial diameter (mm) of the negative control set; D2 is the mycelial diameter of the treated set, including the size of the fungal agar plug (mm); and D0 is the original mycelial diameter (Ø7 mm) of the punched fungal agar plug.The inhibition rate of mycelial growth B. subtilis CF-3. Peak area was used to evaluate the dynamic quantitative changes of VOCs during fermentation. The conical flasks loaded with the fermentation liquid were taken out one by one in 12-h intervals and prepared for the extraction process. The flasks were sealed with silicone septa and aluminum caps, placed in a temperature-controlled water bath for 30 min at 30°C to equilibrate, and then maintained in water baths at 39°C. To extract VOCs secreted by B. subtilis CF-3, three different kinds of extraction fibers were used: 85-μm polyacrylate  fiber for extracting strongly polar compounds, 100-μm polydimethylsiloxane  fiber for extracting low-molecular weight VOCs, and 7-μm PDMS (Supelco) fiber for extracting high-molecular weight VOCs. Before analysis, the fibers were preconditioned as follows: the 85-μm PA fiber was heated at 280°C for 1 h, the 100-μm PDMS fiber was heated at 250°C for 30 min, and the 7-μm PDMS fiber was heated at 320°C for 1 h. The fibers were inserted into the conical flasks through the silicone pads, fixed at about 1 cm above the surface of the liquid, and exposed to headspace for 41 min at 39°C to extract the VOCs. One flask of fresh LB liquid medium was used as a control and processed under the same conditions to eliminate the substances volatilized from the liquid medium. Subsequently, the fibers were removed from the flasks and immediately inserted into the injection port of the GC-MS apparatus for analysis. The GC-MS conditions and procedures were selected as previously described  of B. subtilis CF-3  were uniformly spread onto LB agar plates (Ø90 mm). Subsequently, a plug (Ø7 mm) from the agar of each of the fungi, which were incubated for 7 days, was punched and placed onto the center of a fresh PDA plate. A sandwich was made, with the PDA medium with the fungi on the bottom and the B. subtilis CF-3 liquid-coated LB agar on the top. The set of two plates was sealed with parafilm and incubated at 28°C. Plate sets of the fungal pathogen PDA medium on the bottom and the LB plate coated with distilled water on the top were used as negative controls and incubated under the same conditions. The mycelial diameter (mm) of fungal pathogens was measured after 7 days. The inhibition rate was calculated by Equation 1. There were three replicates for each treatment, and the experiment was repeated three times.Fermentation liquids of B. subtilis CF-3 on fungal pathogens during fermentation through bivariate correlation analysis, and those that showed significant (P < 0.05) positive correlations were selected for the subsequent experiments. The selected compounds  were purchased from Sinopharm Chemical Reagent Co., Ltd. . The compounds were serially diluted six times by 10-fold dilution in dimethyl sulfoxide. All purchased original reagents that were in solid state under normal conditions were dissolved in advance and diluted to 1 mol/L using dimethyl sulfoxide. The mycelial inhibition rates of fungal pathogens were evaluated as the inhibition activity. The reagent liquid (20 μL) was uniformly spread onto a water-agar plate (18 g agar diluted with distilled sterile water to 1 L). Subsequently, plugs (Ø7 mm) from the agars on which the fungi were incubated were punched and placed onto the center of fresh PDA media, and sandwiches of the two plates was sealed together with parafilm, as described previously. Water-agar plates not coated or coated with dimethyl sulfoxide were incubated and used as controls. The mycelial extension (mm) of the fungi was measured after 7 days. The inhibition rate of mycelial growth was calculated by Equation 1. In addition, the half maximal effective concentration (EC50) was calculated. There were three replicates for each treatment, and the experiment was repeated three times.Compounds were selected based on the correlation between the dynamic change of peak area in one compound during fermentation and the dynamic inhibition rate of the VOCs from in vivo experiments on peaches and litchi fruit were performed. For the peaches, a uniform wound (10 mm in diameter × 5 mm deep) was made at the equator of each peach using a sterilized puncher. Subsequently, a spore suspension of M. fructicola (20 μL) was applied to the wound sites at the same depth and air-dried. For litchi fruit, a spore suspension of C. gloeosporioides (20 μL) was directly injected into the flesh (2 mm deep) using a sterile syringe, and the fruit was air-dried. After inoculation, the fruits were placed on sterile plastic trays , and the first and the fourth well in the middle row were left empty for applying VOCs. Afterwards, two filter papers (Ø90 mm) were placed onto the two empty wells, and 200 μL of the original reagent liquid of each single identified VOCs or of the 24hFL of B. subtilis CF-3 was dripped onto filter paper. All purchased original reagents that were in solid state under normal conditions were dissolved in advance and diluted to 1 mol/L using dimethyl sulfoxide. The trays were then put into a carton (450 × 350 × 100 mm). After being sealed, the carton was packed into polyethylene packaging bags and stored at room temperature (25°C). Cartons with filter papers that were not soaked  or soaked with distilled water (negative control for the 24hFL of B. subtilis CF-3) or dimethyl sulfoxide (negative control for dissolved solid reagents) were also stored under the same conditions. The mycelial diameter (mm) and healthy fruit rate (%) were evaluated every day at the same time up to the fifth day after inoculation. Considering the instability of VOCs, the fruits were discarded after daily evaluation and new cartons were opened the next day. There were three replicates of 10 peaches and 20 litchi fruit for each treatment, and the experiment was repeated three times.Two in vivo (Rf) was calculated using the equation:Rf is the percent of inhibition of mycelial extension, Df1 is the mycelial diameter (mm) of the negative control set, and Df2 is the mycelial diameter (mm) of the treated set.The inhibition rate of mycelial growth post-hoc test was applied to compare the mean values of the VOCs. A significance level of P < 0.05 was used to determine the significant differences of the content of VOCs produced after different fermentation times. Mean values with standard deviations were reported.As one of the objectives of this study was to assess the dynamic change in VOCs, the differences in the volatile profiles during fermentation were analyzed using analysis of variance (ANOVA), using the SPSS 19.0 software . Duncan's 50 of single VOCs was calculated using GraphPad Prism 6.01 . All graphs were produced by Origin Pro 8.5 .Additionally, analysis of the correlations between the peak areas of identified VOCs and the inhibition rates was carried out by bivariate correlation analysis and calculated using the SPSS 19.0 software. The ECB. subtilis CF-3 on different common fungal pathogens, the inhibition rate of VOCs was evaluated in three different fractions: the 24hFL, CFF of the 24hFL, and BS of the 24hFL. The effects of VOCs from the 24hFL on M. fructicola and C. gloeosporioides had already been estimated in a previous study  in the amount of total possible VOCs between the 12hFL and 24hFL, and between the 72hFL and 84hFL.In this study, 74 different compounds volatilized in the liquids of B. subtilis CF-3, amounting to 33 different compounds. In total, 15 kinds of alcohols were identified in this study. The number of volatilized alcohols was constant at approximately 13 kinds in all fermentation liquids, regardless of fermentation duration. The relative content of alcohols was 15.03% in the 12hFL, and it gradually increased to the 60hFL, when it peaked at 24.41%; afterwards, it gradually decreased. During the whole fermentation period, 18 ketones were identified. Fourteen were identified in the 12hFL, after which the number declined and reached the minimum (9 different ketones) in the 36hFL. However, the number of ketones rapidly rose after the 36hFL and peaked at 15 in the 48hFL, after which it gradually declined. At first, the relative content of ketones was 22.54% in the 12hFL and gradually decreased, reaching 10.56% in the 48hFL. Afterwards, the content gradually increased until it reached 16.55% in the 84hFL; in the 96hFL, it dropped to the minimum of 7.40%. Furthermore, 10 acids were identified in this study. In the early stage, the number of identified acids was about 5, after which the highest number of different volatilized acids (9 kinds) was identified in the 48hFL. In the mid and late fermentation periods, the number of different acids decreased. In the 12hFL and 48hFL, but not the 36hFL, the relative content of acids was relatively high. Subsequently, the relative content decreased. Among the VOCs, 5 identified compounds were phenols. The number of volatilized phenols was constant at about 4 kinds during the entire fermentation. The highest relative content of phenols (13.71%) was identified in the 24hFL. Furthermore, all fermentation liquids, regardless of fermentation duration, contained only two volatilized aldehydes, benzaldehyde and 3-methylbutanal. The relative content of aldehydes increased at first and peaked in the 24hFL at 7.73%, after which it gradually decreased. Of these two aldehydes, benzaldehyde occupied the larger portion. In addition, 2 unsorted compounds were identified in this study: methoxy-phenyl-oxime and benzothiazole. As the fermentation progressed, the relative content of methoxy-phenyl-oxime increased, peaking at 58.68% in the 96hFL, wherein it occupied the largest portion of unsorted compounds. Anisole, as the ether most commonly identified in this study, occupied a large proportion among the VOCs and was mostly produced in the 60hFL. Finally, the major VOCs produced by CF-3 that were identified at different fermentation times are presented in Table S)-1-octanol, benzoic acid, 2,4-di-tert-butylthiophenol, benzaldehyde, and benzothiazole, were already produced by the 24hFL. As the fermentation progressed, the quantity of the major VOCs gradually decreased.Alcohols and ketones were the two dominant classes of VOCs produced by M. fructicola and C. gloeosporioides inhibition by the VOCs produced by B. subtilis CF-3 were measured at different fermentation times and listed in Table M. fructicola reached its peak of 73.46 ± 3.88% by 24hFL, and there was a subsequent overall declining trend in the inhibition that included one rebound by 96hFL. The inhibition rate of C. gloeosporioides first reached its peak of 63.63 ± 4.69% by 24hFL, then decreased gradually except aside from a rebound by 60hFL. Although the most VOCs were identified in 48hFL, the VOCs from 24hFL showed the strongest inhibitory effects among all the liquids on both fungal pathogens. The GC-MS chromatograms of VOCs produced by 24hFL are shown in Figure The rates of B. subtilis CF-3 and the inhibition rates on M. fructicola and C. gloeosporioides were analyzed by bivariate correlation analysis. The key compounds that had significant (P < 0.05) positive correlation with the inhibition rate are listed in Table P < 0.05) positive correlations between the amounts of produced 2,4-di-tert-butylphenol, 1-octanol, and benzothiazole and the inhibition rates against both M. fructicola and C. gloeosporioides. Additionally, benzoic acid and benzaldehyde were significantly (P < 0.05) positively correlated with the inhibition rate against M. fructicola, and anisole and 3-methylbutanal were significantly (P < 0.05) positively correlated with the inhibition rate against C. gloeosporioides. Those VOCs that exhibited significant (P < 0.05) positive correlations with inhibition rates were chosen as target VOCs in the following experiment.Moreover, correlations between the peak area of VOCs produced by M. fructicola and C. gloeosporioides after different dilution times is shown in Figure 50 of the VOCs are shown in Table 50 values of 2,4-di-tert-butylphenol and 1-octanol on the two selected fungal pathogens were reported previously  and benzaldehyde (EC50 = 6.70 × 10−4 mol/L) exhibited strong inhibitory effects as well. Additionally, 3-methylbutanal showed the strongest inhibitory effect on C. gloeosporioides, with an EC50 of 7.67 × 10−3 mol/L, and 2,4-di-tert-butylthiophenol showed a strong inhibitory effect (EC50 = 1.26 × 10−2 mol/L) as well; the remaining compounds exhibited relatively weak inhibitory effects.The inhibition rate of single identified VOCs on B. subtilis CF-3 showed relatively strong inhibitory effects in vivo, which were inferior only to those of benzothiazole. In litchi fruit, all compounds showed inhibitory effects on pathogen mycelial growth, and, among the compounds, anisole showed the weakest inhibitory effect -1-octanol, benzoic acid, 2,4-di-tert-butylthiophenol, benzaldehyde, and benzothiazole  correlation with the rates of M. fructicola and C. gloeosporioides inhibition. Previous studies have reported that 2,4-di-tert-butylphenol has antifungal abilities against several fungi such as Aspergillus niger and Penicillium chrysogenum  was identified in the essential oil obtained from Senecio graveolens (Compositae), which showed inhibitory effects on the growth of Candida albicans  were identified in the 48hFL. Inhibition rates of the VOCs all reached their peaks in the 24hFL. Significant (P < 0.05) positive correlations between levels of 2,4-di-tert-butylphenol, 1-octanol, and benzothiazole and the rates of M. fructicola and C. gloeosporioides inhibition were observed. Benzoic acid and benzaldehyde levels showed significant (P < 0.05) positive correlations with the rates of M. fructicola inhibition, and anisole and 3-methylbutanal showed the same against C. gloeosporioides. Treatment with 2,4-di-tert-butylphenol showed strong inhibitory effects on both M. fructicola and C. gloeosporioides in vitro. Benzothiazole showed the strongest inhibitory effects on the mycelial extension of both M. fructicola and C. gloeosporioides in vivo, and also increased the healthy fruit rate. Our results demonstrated that 2,4-di-tert-butylthiophenol and benzothiazole are key inhibitory VOCs produced by B. subtilis CF-3. These results lay a preliminary foundation for the clarification of key biocontrol VOCs and provide a theoretical basis for improving biocontrol efficiency and application of B. subtilis strains in the biocontrol field.The VOCs produced by HG, PL, XX, and WG designed the experiments. PL and XX performed the experiments. PL, XX, and QZ analyzed the data. HG and PL drafted the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phase III studies showed that regorafenib has efficacy in patients with advanced gastrointestinal stromal tumors or treatment‐refractory metastatic colorectal cancer. In clinical studies, steady‐state exposure to the M‐2 and M‐5 metabolites of regorafenib was similar to that of the parent drug; however, the contribution of these metabolites to the overall observed clinical activity of regorafenib cannot be investigated in clinical trials. Therefore, we assessed the pharmacokinetics and pharmacodynamics of regorafenib, M‐2, and M‐5 in vitro and in murine xenograft models. M‐2 and M‐5 showed similar kinase inhibition profiles and comparable potency to regorafenib in a competitive binding assay. Inhibition of key target kinases by all three compounds was confirmed in cell‐based assays. In murine xenograft models, oral regorafenib, M‐2, and M‐5 significantly inhibited tumor growth versus controls. Total peak plasma drug concentrations and exposure to M‐2 and M‐5 in mice after repeated oral dosing with regorafenib 10 mg/kg/day were comparable to those in humans. In vitro studies showed high binding of regorafenib, M‐2, and M‐5 to plasma proteins, with unbound fractions of ~0.6%, ~0.9%, and ~0.4%, respectively, in murine plasma and ~0.5%, ~0.2%, and ~0.05%, respectively, in human plasma. Estimated free plasma concentrations of regorafenib and M‐2, but not M‐5, exceeded the IC V600E), and the tumor microenvironment (PDGFR and FGFR) Regorafenib is an oral multikinase inhibitor that blocks the activity of multiple protein kinases, including those involved in the regulation of tumor angiogenesis , oncogenesis  Given the substantial systemic exposure to the M‐2 and M‐5 metabolites observed in clinical trials, we investigated the pharmacokinetics and pharmacodynamics of regorafenib and these two major human metabolites in vitro and in an in vivo murine model in order to assess their potential contribution to the overall pharmacologic activity of regorafenib in humans.G13D, BRAFG464V) and the human CRC cell line HT‐29 (BRAFV600E) were purchased from the American Type Culture Collection .HuVECs and HuLECs were purchased from Lonza . The human breast cancer cell line MDA‐MB‐231  were purchased from R&D . Antibodies against pAKT , AKT (#4691), pERK1/2 , ERK1/2 (#9107), pVEGFR2 , and VEGFR2 (#2479) were from Cell Signaling Technologies ; pVEGFR3  was from Cell Applications ; anti‐pFlg antibody  was from Santa Cruz Biotechnologies , and anti‐VEGFR3 antibody (MAB3757) was from Millipore .Regorafenib and its metabolites M‐2 and M‐5, both cold and radio‐labeled, were obtained from Bayer Pharma R&D . They were dissolved in 100% dimethyl sulfoxide for in vitro applications and in polypropylene glycol/polyethylene glycol 400/Pluronic F68 (42.5 g/42.5 g/15 g) + 20% aqua for in vivo tumor growth inhibition and pharmacokinetic studies.μg/L using a sample volume of 100 μL.Analyte concentrations were measured by high‐performance liquid chromatography using a Hewlett Packard 1100 Series system , with tandem mass spectrometric detection using an Applied Biosystems API 4000 . The assay was validated according to the applicable guideline on method validation scan, DiscoveRx, Fremont, CA), and the binding inhibition activity was expressed as the Kd. A panel of kinases was selected, based on a previous experiment with the same assay in which regorafenib inhibited the binding of a reference compound by at least 80% at a concentration of 1 μmol/L.The kinome‐wide selectivity profiles of regorafenib, M‐2, and M‐5 were analyzed using an active‐site competitive binding assay as previously described V600E assays were performed by ProQinase . Cell lines used were Chinese hamster ovary (TIE‐2 assay), human acute megakaryoblastic leukemia cell line , and rat fibroblasts . VEGFR2 assays were performed in murine fibroblasts (NIH‐3T3) in the presence of 0.1 mg/mL human serum albumin. KITK642E assays were performed in a GIST cell line (GIST882) in the presence of 0.1 mg/mL BSA. Substrate phosphorylation was determined from total cell lysates by enzyme‐linked immunosorbent assay. In addition, an FGFR phosphorylation assay was performed using a human gastric carcinoma cell line (SNU‐16), with pFGFR assessed by western blot. The PDGFRA phosphorylation assay was performed by DiscoverRx using the DiscoverRx PathHunter cell line, modified to express PDGFRA. Cells were preincubated with varying concentrations of regorafenib, M‐2, or M‐5 for 60 min and subsequently stimulated with 20 ng/mL PDGF  for 90–180 min. The assay signal was generated using a detection reagent cocktail and measured by chemoluminescence Pharmacologic activities of regorafenib, M‐2, and M‐5 were measured in cellular kinase phosphorylation assays. TIE‐2, wt KIT, and BRAF5 cells were treated with various concentrations of each compound for 1 h before stimulation with VEGF‐A (50 ng/mL) or VEGF‐C (200 ng/mL) for 10 min. Cells were lysed, and total cell lysates were analyzed for inhibition of phosphorylation by western blot, using antibodies against total and phosphorylated VEGFR2 and VEGFR3, ERK1/2, and AKT. Signals were detected by electrochemical luminescence .Endothelial cell assays were performed as described previously 5 HuLECs per well were grown overnight on a gelatin‐coated 6‐well plate, serum starved for 6 h in EBM‐2 containing 0.1% BSA, and treated with regorafenib, M‐2, or M‐5 100 nmol/L for 1 h before the addition of VEGF‐C to a final concentration of 200 ng/mL. A sterile pipette tip was then used to scratch the cell layers, and images were taken after continued incubation for 40 h.For the migration inhibition assay, 2–3 × 107 MDA‐MB‐231 or HT‐29 cells, which were allowed to grow subcutaneously to a palpable size of approximately 60–100 mm3. After random assignment, eight animals per group were administered a daily oral dose (3 mg/kg or 10 mg/kg) of regorafenib, M‐2, M‐5, or vehicle for 27 days, starting at Day 11 (HT‐29) or Day 13 (MDA‐MB‐231) after tumor inoculation. All administrations were performed in the morning, without anesthesia. Tumor volume was determined twice per week using caliper measurements, and the volume was calculated using the formula (D × d2)/2, with d defined as the minor axis and D as the major axis of the measurement. Tumor growth inhibition was calculated as relative tumor volume in relation to the first treatment day.Mouse experiments were approved by the relevant regulatory agency of the federal state of Berlin . Mice were maintained in individually ventilated cages in groups of four mice per cage. They received autoclaved food and bedding  and acidified (pH 4.0) tap water ad libitum. The animal facility was equipped with an automatic 12 h light/dark regulation, temperature regulation at 22 ± 2°C, and relative humidity of 50 ± 10%. Female NMRI nu/nu mice  were inoculated with 1 × 10P < 0.05 was regarded as significant. Additionally, body weight of all mice was determined twice weekly, and the health status of the animals, including behavioral changes, was recorded daily.Changes in tumor volume were statistically evaluated by two‐way repeated‐measures analysis of variance, followed by Bonferroni correction, using GraphPad Prism 5 , and ss and Cmax, were calculated from plasma concentrations using a noncompartmental analysis.Pharmacokinetic studies were performed in female NMRI Foxn1 nu/nu mice, supplied by Harlan‐Winkelmann GmbH . Regorafenib, M‐2, and M‐5 were each given to three animals per sampling time point at a dose of 10 mg/kg orally once daily for 5 days. Pharmacokinetic parameters, namely the AUC(0–24)14C]regorafenib, [14C]M‐2, and [3H]M‐5) were used for protein‐binding studies. A radioactivity analysis was performed by liquid scintillation counting. At least three incubations per drug concentration were performed at neutral pH and at either 37°C ([14C]regorafenib and [14C]M‐2) or room temperature ([3H]‐M‐5). All compounds were tested for stability at the incubation conditions used. Investigated concentrations in plasma ranged between 0.4 and 18 mg/L for regorafenib, between 0.5 and 62 mg/L for M‐2, and between 0.8 and 8 mg/L for M‐5.The extent of binding of regorafenib and its metabolites, M‐2 and M‐5, to plasma proteins from male CD‐1 mice and white men was determined in vitro using methodology developed by Schuhmacher et al. Kd values were determined. The results demonstrate comparable kinase inhibition profiles for regorafenib, M‐2, and M‐5 , KIT , RET , PDGFRs , and RAFs . TIE‐2  and FGFRs  revealed moderate inhibition (data not shown). A number of additional kinases were inhibited with Kd values of 100 nmol/L or less, but this activity still requires confirmation in cell‐based assays (Table S1). No inhibition was observed for EGFR family kinases, the protein kinase C family, insulin and insulin‐like growth factor receptor kinases, MET, MEK family kinases (with the exception of MEK5), ERK1/2, AKT, PI3 family kinases, and ATM/ATR family kinases, even with concentrations up to 1 μmol/L (data not shown).To assess the pharmacologic activity of M‐2 and M‐5 in comparison with the parent regorafenib compound, a competitive binding assay was performed using a selected panel of kinases for which 50 values for M‐2 and M‐5, estimated from western blots, were similar to that of regorafenib for both receptors, with M‐5 being slightly less effective . Similarly, all agents showed significant inhibition over vehicle controls in the HT‐29 model , but generally at a slightly lower range than that observed in the MDA‐MB‐231 model. The antitumor effects of 3 mg/kg/day doses of regorafenib, M‐2, and M‐5 were also investigated in the two xenograft models . Tumor growth inhibition was similar for regorafenib and M‐2 at the 3 mg/kg/day dose in both models, but with lower inhibition than the 10 mg/kg/day dose. M‐5 showed less growth inhibition at the 3 mg/kg/day dose in the HT‐29 model than the other agents, but higher inhibition in the MDA‐MB‐231 model (similar inhibition to the 10 mg/kg/day dose). All treatments were well tolerated, with body weight losses below 5% and no signs of adverse effects.The dose‐dependent in vivo activity of M‐2 and M‐5 in HT‐29 colon cancer and MDA‐MB‐231 breast cancer xenograft models was investigated in NMRI nu/nu mice—a strain that is genetically similar to the one used in the pharmacokinetic analyses. These studies demonstrated that M‐2 and M‐5 caused potent inhibition of tumor growth at 10 mg/kg/day, similar to that observed with the regorafenib parent compound at the 10 mg/kg/day dose , while M‐2 and M‐5 accounted for 16% and 2% of total exposure, respectively (Table Cmax of 4146 μg/L was measured for regorafenib (administered compound), whereas the Cmax of in vivo formed metabolites M‐2 and M‐5 reached only 753 μg/L and 63 μg/L, respectively, resulting in an overall total Cmax for all three compounds of about 5000 μg/L ss of 67,093 μg × h/L, with M‐2 accounting for 78%, regorafenib  for 17%, and M‐5 for 5% of total exposure ss of 52,796 μg × h/L and a Cmax of approximately 5300 μg/L (Table Administration of M‐5 resulted in an AUC0–24)4ss of 52The pharmacologic activity of small‐molecule compounds, in vitro and in vivo, is strongly dependent on their binding to plasma proteins Clinical trials have shown that total steady‐state exposure to the M‐2 and M‐5 metabolites of regorafenib in humans is similar to that of the parent drug Kd of regorafenib at VEGFR2 and RAF‐1 might be attributable to interspecies differences, variations in the protein preparation, or variations in assay conditions.Combined data from biochemical and cellular assays, as well as preclinical in vivo studies, indicate that M‐2 and M‐5 show similar pharmacologic activity to that of the parent compound, with both metabolites demonstrating similar kinase inhibition activity and selectivity profiles to those of regorafenib. The biochemical kinase activity results were generated by a competitive binding assay Kd values below 100 nmol/L (Table S1). Some of these kinases have been implicated in cancer, such as mutant forms of the receptor tyrosine kinases FLT3 and DDR2, which have been identified in acute myeloid leukemia and lung cancer, respectively Regorafenib was found to potently inhibit a number of mutant and wt kinases in biochemical assays, including its previously identified key target kinases, with Neither regorafenib nor M‐2 and M‐5 significantly inhibit ERK1/2 and AKT in biochemical assays (data not shown); therefore, the observed reductions of pERK1/2 and pAKT levels in cell‐based assays are presumably indirect effects due to the inhibition of kinases located upstream in their signaling pathway, such as VEGFR3 and Raf.50 values to those previously reported 50 range for the inhibition of VEGFR2 phosphorylation in biochemical assays was similar to that observed in cellular kinase assays, indicating comparability between these two analyses. The antilymphangiogenic properties of M‐2 and M‐5 were further demonstrated in a cell migration assay, showing a similar effect to that of regorafenib. In addition, it is likely that the FGFR1 assay in SNU‐16 cells was detecting FGFR2 activity because FGF7 (the ligand used in the assay) has previously been reported to be specific for FGFR2 Cellular kinase assay data for regorafenib‐mediated inhibition of VEGFR2 and FGFR2 autophosphorylation show comparable ICCmax in mice. The originally administered compound accounted for the majority of the total AUC and Cmax, consistent with the proposed biotransformation pathway of regorafenib in humans shown in Figure S1, with a reversible biotransformation leading to the formation of the M‐2 metabolite from regorafenib and vice versa. However, formation of the M‐5 metabolite from M‐2 results from a nonreversible biotransformation due to demethylation.In vivo, tumor xenograft studies showed that the M‐2 and M‐5 metabolites both had similar antitumor activity to that of the parent compound. This finding is consistent with the in vitro data showing the generally similar ability of the three compounds to inhibit the target kinases, as well as the pharmacokinetic data for all three compounds demonstrating comparable protein binding, total exposure (sum of AUCs), and Cmax plasma concentrations in mice after once‐daily dosing with regorafenib 10 mg/kg for 5 days were comparable to those in humans receiving 160 mg/day in a 3 weeks on/1 week off dose regimen, which was defined as the maximum tolerated dose To assess antitumor activity in murine models and perhaps improve the usefulness of these models for predicting activity in humans, we analyzed various pharmacokinetic parameters and the protein binding of regorafenib and its metabolites M‐2 and M‐5. Notably, regorafenib exposure and total Another essential factor for the assessment of the pharmacologic activity of small‐molecule compounds is the proportion bound to biologic molecules, in particular, plasma and tissue proteins. A high proportion of protein‐bound drug (more than 99%) was observed for regorafenib, M‐2, and M‐5 in both murine and human plasma. Although the unbound fraction of regorafenib was similar in murine and human plasma, marked interspecies differences were observed in the unbound fractions of M‐2 and M‐5, with ~5‐fold and ~8‐fold lower unbound fractions, respectively, in human than in murine plasma.50 values of the target kinases determined in biochemical or cell‐based assays, providing an estimate of the contribution of each compound to the in vivo pharmacologic activity of regorafenib. The calculated maximum free drug concentrations for regorafenib, M‐2, and M‐5 were 49.4, 13.4, and 0.5 nmol/L, respectively, in murine plasma. These closely matched the free drug concentrations for regorafenib, M‐2, and M‐5 of 42.1, 13.5, and 3.4 nmol/L, respectively, in human plasma. These concentrations are well above the IC50 values of approximately 4 nmol/L for regorafenib and M‐2 for human VEGFR2 in HuVECs demonstrated in this study, as well as the IC50 values of approximately 4 nmol/L for recombinant murine VEGFR2 50 value of approximately 16 nmol/L for human VEGFR2, which suggests that M‐5 may have a limited contribution to the overall efficacy of regorafenib, despite the similarity of its pharmacologic profile to parent regorafenib. Ultimately, pharmacokinetic and pharmacodynamic analyses of tumor tissue that provide an integrated read‐out are required in order to determine the kinetics of local target kinase inhibition.To relate the unbound fraction with in vivo pharmacological activity, it is important to consider that total drug concentration is only a relative value, and that the free drug concentration is the more relevant measure. This allows comparison of the free plasma drug concentration with the ICIn this study, in vitro experiments and in vivo studies in mice were used to assess the pharmacologic activity of the major human metabolites of regorafenib and extensive pharmacokinetic analyses were performed to provide an estimate for their contribution to their therapeutic efficacy in tumor patients. Although the compounds show behavioral similarities between species, such as the oxidative biotransformation of the parent compound regorafenib, variabilities were observed in other cases such as protein binding. Considering that physiology is more complex, predictions across species have, therefore, to be taken with some caution.Overall, these preclinical data show that the biochemical and antitumor activities of M‐2 and M‐5 are similar to those of the regorafenib parent compound. When the estimated in vivo free plasma concentrations of regorafenib and its metabolites are taken into consideration, the data suggest that M‐2, but not M‐5, has a high likelihood of contributing to the overall efficacy of regorafenib.DZ, WS, KD, SW, KHT, FT‐H, and MG are full‐time employees of Bayer Pharma AG. IF is the chief scientific officer of Experimental Pharmacology & Oncology GmbH. AB is the chief scientific officer of Shakti BioResearch LLC.Figure S1. Oxidative biotransformation pathways of regorafenibFigure S2. Effects of regorafenib and its metabolites M‐2 and M‐5 on the growth of human tumor xenografts in mice.Table S1. Biochemical kinase selectivity profiles of regorafenib, M‐2, and M‐5. Part a lists kinases with Kd values ≤ 100 nmol/L for at least one compound.Table S2. Growth inhibition of human colorectal cancer (HT‐29) and breast cancer (MDA‐MB‐231) xenografts in mice by regorafenib, M‐2, and M‐5.Click here for additional data file.


Peroxisome proliferator-activating receptor γ (PPARγ), a transcription factor, is involved in many important biological processes, including cell terminal differentiation, survival and apoptosis. However, the role of PPARγ, which regulates tumour promoter and oncogene expression, is not well understood in hepatocellular carcinoma (HCC). In the present study, based on evidence from clinical samples that phosphorylation of PPARγ at Ser84 is up-regulated in human liver tumours, we confirmed that phosphorylation of PPARγ was also significantly increased in an HCC mouse model and was increased by Mitogen-activated protein kinase (MEK)/ Extracellular-signal-regulated kinases (ERK) kinase. Next, we performed an RNA microarray analysis, and our data indicated that dephosphorylation of PPARγ at Ser84 affects the expression of glycolysis-related genes and pro-proliferation genes, which supposedly promote proliferation of HCC cells. Using a chromatin immunoprecipitation (ChIP) assay, we demonstrated that the observed PPARγ-mediated induction of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 (PFKFB4) expression was directly modulated by the transcriptional activity of its promoter. Furthermore, using knockdown of PFKFB4, we elucidated that the stimulation of PPARγ phosphorylation on glycolysis and proliferation in HCC is dependent on PFKFB4. Together, these findings extend our understanding of how liver tumour cells reprogram their glycolytic pathways by post-translational modification of specific transcription factors and lay a foundation for the screening of new targets for the treatment of HCC. Hepatocellular carcinoma (HCC) is currently the sixth most common cancer worldwide and remains an extremely complex condition with a poor prognosis , 2. HCC Peroxisome proliferator-activating receptor γ (PPARγ) is an isoform of the PPAR nuclear receptor family that functions as a transcription factor. Numerous studies have proven that PPARγ is involved in many important biological processes, including cell terminal differentiation, survival and apoptosis, and thereby plays an essential role in regulating adipogenesis, inflammation, tumourigenesis and metastasis etc.. In factPPARγ can be phosphorylated at Ser112 (Ser82 in PPARg1) and Ser273 by mitogen-activated protein kinases (MAPKs) and cyclin-dependent kinases, respectively –19. Normin vivo is still not clear.Liver tumour cells reprogram their metabolic pathways to meet their needs during the process of tumour progression. The best-characterized metabolic phenotype observed in tumour cells is the Warburg effect, which is a shift from ATP generation through oxidative phosphorylation to ATP generation through glycolysis, even under normal oxygen concentrations . The enzIn the present study, we found that phosphorylation of PPARγ at Ser82/Ser84 was up-regulated in mouse (Ser82) and human (Ser84) liver tumours and is increased by MEK/ERK kinase. Next, our data indicated that phosphorylation of PPARγ at Ser84 stimulated the expression of glycolysis-related genes and pro-proliferation genes. We also demonstrated that the observed PPARγ-mediated induction of PFKFB4 expression was directly increased by the transcriptional activity of its promoter. Together, these findings extend our understanding of how liver tumour cells reprogram their glycolytic pathways by post-translational modification of specific transcription factors and lay a foundation for the screening of new targets for the treatment of HCC.A diethylnitrosamine (DEN) mouse model of HCC was established, and mice were randomized to control. Sixteen pairs of liver samples from normal (N) mice or tumour-bearing (T) mice were collected to detect the phosphorylated and total PPARγ by western blot Figure . AccordiP > 0.05). The huge difference of PPARγ expression in WD-SCC might be caused by heterogeneity of tumor cells  in liver and well-differentiated HCC (WD-HCC). Proliferating cell nuclear antigen (PCNA), evaluated as a marker of cell proliferation, was elevated Figure  in ACF as Figure .The human HCC liver samples were used for further verification. In the sections of human HCC analysed by immunostaining, phosphorylated PPARγ in the tumour was increased compared with that in the paratumour  down-regulated (12.4-fold) transcriptional activity in mouse HCC, in concert with the up-regulated phosphorylation of PPARγ. To verify that PPARγ phosphorylation suppresses PPARγ transcriptional activity, we established stable clones of HepG2 overexpressing PPARγWT, PPARγSA  or PPARγSD . As expected, compared to the WT, PPARγ transcriptional activity in HepG2 overexpressing PPARγSA was up-regulated, but that in PPARγSD was down-regulated  in HepG2 and Hepa1-6, not in SMMC7721 or Hep3B, according to a luciferase reporter gene assay , an agonist of PPARγ, were better than with PD0325901 alone. ). Interestingly, this showed that the amount of Ki67 mRNA positively correlated with PFKFB4 mRNA Figure . MoreoveHere, we show for the first time that PPARγ phosphorylation at Ser84/Ser82 (Ser112 in PPARγ2) occurs in the majority of HCC cases investigated and in liver tumours from human samples and a mouse model, whereas blockade of PPARγ phosphorylation by a kinase inhibitor or site-mutation both decrease the aggressiveness and tumourigenicity of hepatoma/hepatocarcinoma cells.PPARγ is most famous for its roles in adipogenesis, in which it induces the expression of genes involved in lipid synthesis and adipocyte differentiation. Accumulating evidence has demonstrated the involvement of PPARγ in glucose metabolism as well. For example, the thiazolidinediones, PPARγ agonists, have been widely used for the treatment of type 2 diabetes mellitus owing to their effectiveness in lowering blood glucose levels . Interesin vivo study verified that DEN-induced HCC cannot be inhibited by a PPARγ agonist (RSG) alone because TZD drugs can exert protumourigenic actions in certain rodent models [The paradoxical effects resulting from PPARγ activation are derived from a complex balance of the anti- versus pro-tumour functions of the PPARγ protein and its ligands in a given system. As expected, our t models . Howevert models , currentConsistently, our results indicated that phosphorylation of PPARγ up-regulated expression of PFKFB4 at the transcriptional level, suggesting that some “alternative” PPARγ transcriptional activity is increased under this condition. Because the site and kinase for PPARγ phosphorylation are not altered, the reason might lie in a specific PFKFB4 promoter that affects the binding between PPARγ and cis-acting elements upstream of the PFKFB4 gene. Furthermore, there is another possible explanation that phosphorylation of PPARγ results in this repressive transcription factor inactivation and thus activate/mobilize the target genes, such as PFKFB4. The exact mechanism remains to be elucidated in further studies.In summary, our study provides the first evidence that the novel roles of PPARγ are linked to glycolysis by PFKFB4 for the regulation of glycolysis and proliferation in HCC cells. Deregulation of cellular metabolism is one of the hallmarks of cancer cells, and altered components of the metabolic pathway represent attractive therapeutic targets. Thus, the identification of the MEK/ERK/PPARγ/PFKB4 axis regulating glycolysis elicits a potentially new approach to targeting a tumour-specific metabolic pathway and understanding the mechanisms of hepatocarcinogenesis, its detection, therapeutic intervention, and prevention.Fourteen-day-old C57BL/6 mice were injected intraperitoneally (i.p.) with 25 mg/kg DEN . Control mice were given an equivalent volume of saline. After 8 months, mice were euthanized, and their livers were collected and fixed in 10% formalin for histological and immunohistochemical analysis.7 HepG2 cells stably expressing PPARγ were crosslinked with 3.7% formaldehyde at room temperature for 10 min. Cells were incubated with 0.125 M glycine to terminate crosslinking and washed twice with PBS. DNA was prepared by using a SimpleChIP Enzymatic Chromatin IP Kit  according to the manufacturer's instructions. A DNA fragment encompassing the indicated region of the human PFKFB4 promoter was amplified using 35 cycles of PCR at 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s with specific primers. All amplified products were resolved on 4% agarose gel [A total of 1 × 10rose gel .Total RNA was isolated from HepG2 cells stably expressing WT and S112A mutant of PPARγ. Array hybridization and scanning were performed using a NimbleGen Hybridization System and Axon GenePix 4000B microarray scanner. All array data were imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Further analysis was performed using Agilent GeneSpring GX software (version 11.5). The raw data were uploaded as the supplementary data.


Avian coronavirus infectious bronchitis virus (IBV) is a respiratory pathogen of chickens that causes severe economic losses in the poultry industry worldwide. Major advances in the study of the molecular biology of IBV have resulted from the development of reverse genetics systems for the highly attenuated, cell culture-adapted, IBV strain Beaudette. However, most IBV strains, amongst them virulent field isolates, can only be propagated in embryonated chicken eggs, and not in continuous cell lines.We established a reverse genetics system for the IBV strain H52, based on targeted RNA recombination in a two-step process. First, a genomic and a chimeric synthetic, modified IBV RNA were co-transfected into non-susceptible cells to generate a recombinant chimeric murinized (m) IBV intermediate (mIBV). Herein, the genomic part coding for the spike glycoprotein ectodomain was replaced by that of the coronavirus mouse hepatitis virus (MHV), allowing for the selection and propagation of recombinant mIBV in murine cells. In the second step, mIBV was used as the recipient. To this end a recombination with synthetic RNA comprising the 3′-end of the IBV genome was performed by introducing the complete IBV spike gene, allowing for the rescue and selection of candidate recombinants in embryonated chicken eggs.in ovo growth kinetics and in vivo characteristics of the recombinant virus were similar to those of the parental IBV strain H52.Targeted RNA recombination allowed for the modification of the 3′-end of the IBV genome, encoding all structural and accessory genes. A wild-type recombinant IBV was constructed, containing several synonymous marker mutations. The Targeted RNA recombination allows for the generation of recombinant IBV strains that are not able to infect and propagate in continuous cell lines. The ability to introduce specific mutations holds promise for the development of rationally designed live-attenuated IBV vaccines and for studies into the biology of IBV in general.The online version of this article (doi:10.1186/s12985-017-0775-8) contains supplementary material, which is available to authorized users. Avian coronavirus infectious bronchitis virus (IBV) primarily infects the upper respiratory epithelium of chickens, causing a respiratory disease that is frequently complicated by secondary bacterial pathogens [athogens . In addiathogens . IBV is Coronaviridae, order Nidovirales [. The enveloped virus particles have a positive-sense RNA genome of 27.6 kb  for IBV which allow the manipulation of its genome –26. All Alphacoronavirus and Betacoronavirus [in ovo and the in vivo phenotypic characteristics in one-day-old chickens similar to IBV wild type. The results presented here demonstrate for the first time a host species switch for an avian gammacoronavirus by exchanging the spike gene with that of the highly divergent betacoronavirus MHV. This RGS enables the manipulation of the structural and accessory protein genes from the genome of virulent IBV.Targeted RNA recombination is another reverse genetics approach, so far only developed for mammalian coronaviruses from the genera onavirus , 19, 33.onavirus . As a coonavirus , 19, 33.2. Virus titers in cells were obtained by determining the 50% tissue culture infective dose (TCID50) per ml at 2 days post inoculation (p.i.) according to the Spearman–Kärber method [Baby hamster kidney (BHK-21) cells (ATCC CCL-10) and murine LR7 cells KUO2000 were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (BioWhittaker), supplemented with 4 mM L-glutamine , 10% fetal bovine serum (FBS) (BioWhittaker) and 0.05 mg/ml gentamicin (Gibco Invitrogen), at 37 °C and 5% COr method .in ovo was based on the determination of the 50% embryonic infectious dose (EID50) per ml, as determined at day 7 p.i. according to Reed and Muench [50, incubated for 24 h, and subsequently cooled for 16–24 h before the AF was harvested and pooled.Fertilized specific pathogen free (SPF) white leghorn eggs  were incubated at 37.5 °C and 45–65% relative humidity. Eight-day- embryonated chicken eggs (ECE) were inoculated via the allantoic cavity unless stated otherwise, and candled twice daily. Upon embryonic death or no later than 7 days p.i., eggs were transferred to 4 °C for 16–24 h prior to allantoic fluid (AF) and chorio-allantoic membrane (CAM) collection. Virus titration d Muench . For theIBV strain H52 (Boehringer Ingelheim (BI), Ingelheim, Germany) was propagated in embryonated SPF eggs and titrated. IBV strain Beaudette  was propagated and titrated in BHK-21 cells. Mouse hepatitis virus (MHV) strain A59 was propagated and titrated in LR7 cells.Monoclonal antibody (MAb) Ch/IBV 26.1 against the IBV S2 protein was obtained from Prionics  , 38. TheCAMs were collected from ECEs, washed in PBS, fixed in neutral buffered 10% formalin in PBS for 24 h, stored in 70% ethanol and finally paraffin-embedded. Four micrometer sections of CAM were mounted on glass slides and subsequently deparaffinized and rehydrated in alcohol series. Next, the sections were subjected to endogenous peroxidase inactivation and antigen retrieval as described before . SectionRNA was isolated from harvested AF using the QIAamp viral RNA Mini Kit  according to manufacturer’s protocol. Reverse transcription (RT) was performed using the Transcriptor First Strand cDNA Synthesis Kit  according to manufacturer’s protocol, with random hexamers for standard PCR, or with specific primers for sequencing and cloning purposes. PCR was performed with recombinant Taq DNA polymerase (Thermo Fisher Scientific) for plasmid characterization or with Phusion Hot Start II High-Fidelity DNA Polymerase (Thermo Fisher Scientific) for sequencing and cloning purposes.One-step RT-qPCR was used to semi-quantitatively assess virus load in AF. Forward primer IBV.RdRp.F41 (3′-CATGCAGTTTGTTGGAGATCCT-5′) and reverse primer IBV.RdRp.R41 (3′-GTGACCTGGTTTTACCGTTTGA-5′) targeting the conserved region of gene 1b  coding for the RNA-dependent RNA polymerase protein. Primers were obtained from Biolegio  and used at a final concentration of 300 nM each with the iTaq universal SYBR Green one-step kit . The RT-qPCR reaction was carried out in a Bio-Rad CFX Connect real-time PCR system, starting with 10 min at 50 °C and 1 min at 95 °C, followed by 40 cycles of 10 s at 95 °C and 30 s at 60 °C, and ending with a dissociation step for the determination of the melting point of the obtained PCR fragment.The complete genome sequence of IBV H52 BI was determined by Sanger sequencing using primers as described by Zhou et al. . The 5′-The design of the donor plasmids principally followed the strategy previously described by Kuo et al. . The finBstBI site between p-IBV-5 and p-IBV-1b, which is only partly present in the 5′-UTR, and the XhoI site between p-IBV-1b and p-IBV-S, which was introduced without changing the amino acid sequence (silent mutation). Restriction enzyme sites were made unique by silently removing these RES from other parts of the genome included in the donor plasmid  located in the viral cDNA were used, except for the XhoI overhang IBV and p-IBV-N, respectively. After 1 h of incubation at 37 °C, production of RNA was verified by analyzing 1 μl of the reaction volume by gel electrophoreses. After an incubation of 2 h the reaction was stopped by transferring the reaction tubes to ice.Capped, run-off donor transcripts were synthesized from p-IBV, p-mIBV and p-IBV-N using the mMessage mMachine T7 kit (Ambion by Thermo Fisher Scientific). In brief, p-mIBV was The IBV spike gene was replaced by a chimeric MHV-IBV spike gene in the IBV genome by targeted RNA recombination between p-mIBV generated donor RNA and recipient virus (IBV) RNA, as described before . IBV H52Two days after seeding, when syncytia in the LR7 monolayer were observed, the cell culture supernatant was harvested and rescued viruses were purified by two rounds of plaque purification on LR7 cells. Characterization of the last one-third of the genome of candidate recombinants was performed by RT-PCR and subsequent Sanger sequencing of the obtained cDNA-fragments, using the primer sets specified in Table −3) were prepared. Two hundred microliters of LR7 cell suspensions were inoculated into the allantoic cavity of 10-day-old ECEs, using 5 eggs per dilution. The eggs were candled twice daily and scored for embryonic death. Upon death, or at 7 days p.i., the eggs were transferred to 4 °C. Sixteen to twenty-four hrs later the AF was collected aseptically for RT-qPCR, and the CAMs were fixed in 10% formalin for immunohistochemistry (IHC). The AF from eggs inoculated with the highest dilution of electroporated LR7 cells, in which virus was detected by RT-qPCR and IHC, was subjected to two additional rounds of end-point dilution in 8-day-old ECE. Genetic characterization of candidate recombinants was performed by RT-PCR and subsequent Sanger sequencing of the region encoding the structural and accessory genes using the primer sets specified in Table Recombinant IBV (rIBV) was generated by substituting the IBV spike ectodomain back into the mIBV genome by targeted RNA recombination between p-IBV-generated donor RNA and recipient virus mIBV. LR7 cells were infected with mIBV at a multiplicity of infection (MOI) of 0.4 for 4 h. Capped, run-off donor transcripts from p-IBV were transfected into the mIBV-infected LR7 cells by electroporation with two pulses at 850 V and 50 μF. Electroporated LR7 cells were resuspended in 2 ml DMEM at 37 °C) and tenfold dilutions  double staining for IBV and MHV. BHK-21 and LR7 cells were grown on coverslips and inoculated with IBV Beaudette, and MHV A59 and mIBV #1B3-IIA, respectively, at an MOI of 1.0. Cells were fixed with PBS 4% paraformaldehyde  for 20 min at room temperature after 5¼, 8, and 10 hpi for MHV, IBV, and mIBV, respectively. Subsequently, cells were permeabilized with PBS containing 0.1% Triton X-100, blocked with goat serum (Gibco by Life Technologies), and incubated for 45–60 min with a combination of two primary antibodies in NGS; rabbit anti-MHV polyserum k134 diluted 1:400 and chicken anti-IBV-H120 serum diluted 1:400, or rabbit anti-MHV polyserum k134 diluted 1:400 and mouse MAb Ch/IBV 26.1 diluted 1:200. Cells were washed three times with PBS/0.05% Tween-20 and incubated in the dark for 45 min with a combination of two fluorescently labelled secondary antibodies diluted 1:200 in NGS: Alexa Fluor 488 goat anti-chicken IgY and Alexa Fluor 568 goat anti-rabbit IgG, or Alexa Fluor 488 goat anti-mouse IgG and Alexa Fluor 568 goat anti-rabbit IgG. Cells were washed three times and nuclei were stained with 300 nM DAPI in PBS for 5–10 min in the dark. Cells were washed once with milliQ and mounted with Fluorsave . Slides were viewed using an Olympus BX60 microscope with filters I3, A and N2.1 with a Leica DFC425C color CCD and Leica LAS-AF software .2 EID50 of IBV H52 BI or recombinant IBV wild-type (rIBV-wt). Eggs were candled twice daily and 6, 12, 24, 36, and 48 hpi. Five previously selected eggs per virus strain were transferred to 4 °C for 16–24 h, and AF was aseptically harvested and stored at −80 °C. For analysis, AF samples were thawed and tenfold diluted in PBS without Ca and Mg, and nucleic acids were extracted with the QIAamp DNA Blood Mini kit  and the addition of carrier RNA, using the Hamilton Starlet pipet robot . Extracted nucleic acids were analyzed by RT-qPCR for the amount of IBV RNA following the protocol from Callison et al. [Eight-day-old ECEs were inoculated with 10n et al.  with smaSPF layer-type chickens were used in the experimental infection study at Boehringer Ingelheim Animal Health Operations . The chickens were hatched from SPF eggs at BIAHO from eggs obtained from Charles River . One-day-old chickens were kept in separate isolators under controlled housing conditions, including filtered supply and exhaust air.3 EID50 in 0.1 ml of IBV H52 BI (n = 5), rIBV-wt (n = 5), or not inoculated . Clinical symptoms monitored included ruffled feathers, decreased consciousness, depression, gasping, coughing, tracheal rales, and nasal discharge. Seven days p.i. animals were euthanized, and evaluated for their tracheal ciliary activity. To this end, the trachea was sliced into 10 transversal sections: 3 from the upper part, 4 from the middle part, and 3 from the lower part. Ciliary activity was examined by low-magnification microscopy within 2 h after sampling. Ciliostasis of each tracheal section was scored on a scale from 0 (100% ciliary activity) to 4 , with the maximum score for each trachea being 40. Finally, the mean ciliostasis score for each group of animals was calculated. A unpaired t-test was performed to analyse whether there were differences in ciliostasis scores between IBV H52 BI and rIBV-wt.Animals were housed in separate groups and inoculated via eye-drop with 10Viral RNA of IBV H52 BI and RNAs transcribed from plasmids p-mIBV and p-IBV-N were co-transfected into BHK-21 cells and seeded onto monolayers of LR7 cells. At 2 days post transfection, syncytia were observed in the LR7 monolayers, suggesting the successful generation of recombinant mIBV. After two rounds of plaque purification on LR7 cells, candidate recombinants were characterized antigenically and genetically.IF staining of LR7 cells infected with mIBV showed positive staining with both anti-IBV and anti-MHV sera, indicating the chimeric nature of mIBV Fig. . IF staiLR7 cells infected with mIBV and subsequently transfected with RNA transcribed from plasmid p-IBV were inoculated in tenfold dilution series into the allantoic cavity of 10-day-old ECEs. No embryonic death was observed up to 7 days p.i., but embryos in the lowest dilutions showed signs of stunting and curling typical for embryos infected with IBV. The presence of replicating recombinant IBV was demonstrated by RT-qPCR on viral RNA extracted from the AF (data not shown), and by IHC on CAM tissue Fig. . In contUsing specifically located primers Fig. , the intBstBI RES in gene 1b, which marks the start of p-mIBV) confirmed the expected genetic identity of mIBV as observed after RT-PCR analysis .ses Fig. . The virP > 0.05), indicating that rIBV maintained the ability to infect one-day-old chickens and induced lesions to a similar extent as the parental virus strain.The pathogenicity of rIBV-wt was compared to that of the parental IBV H52 BI strain by inoculating one-day-old SPF chickens. During the course of the infection, no clinical symptoms were observed in any of the groups (data not shown). At 7 days p.i. the animals were euthanized and the mean ciliostasis scores were determined as a readout for the ability of the respective viruses to infect and cause lesions in the primary target organ, the trachea. As expected, the negative control animals scored very low  based on targeted RNA recombination that allowed manipulation of the genome of virulent IBV was developed. This system makes use of an interspecies chimeric coronavirus, which is created by replacing the ectodomain of the IBV spike protein by that of MHV. The spike ectodomain exchange results in a host species tropism switch, which enables replication of mIBV in cell culture. Upon recombination of mIBV with synthetic donor RNA carrying the IBV spike gene, rIBV could be rescued in ECEs. Additional file 1: Table S1.Silent mutations introduced in rIBV. Nucleotide positions and sequences refer to the IBV H52 BI genome  and recombinant (r)IBV wild-type (wt) P4 with IBV H52 BI. Numbers refer to nucleotide positions in the IBV H52 BI genome. Restriction enzyme sites are highlighted in yellow, with the corresponding enzyme indicated above the sequences. An additional thymidine residue to keep the MHV spike gene ectodomain sequence in frame with the IBV spike gene signal sequence at position 20,385 is highlighted in green and marked with a # above the sequence. A spontaneous T to C silent substitution in the spike of rIBV-wt at position 22,644 is highlighted in red. (DOCX 41 kb)


Immunoglobulin E (IgE) is the antibody that plays a central role in the mechanisms of allergic diseases such as asthma. Interactions with its receptors, FcεRI on mast cells and CD23 on B cells, are mediated by the Fc region, a dimer of the Cε2, Cε3 and Cε4 domains. A sub-fragment lacking the Cε2 domains, Fcε3–4, also binds to both receptors, although receptor binding almost exclusively involves the Cε3 domains. This domain also contains the N-linked glycosylation site conserved in other isotypes. We report here the crystal structures of IgE-Fc and Fcε3–4 at the highest resolutions yet determined, 1.75 Å and 2.0 Å respectively, revealing unprecedented detail regarding the carbohydrate and its interactions with protein domains. Analysis of the crystallographic B-factors of these, together with all earlier IgE-Fc and Fcε3–4 structures, shows that the Cε3 domains exhibit the greatest intrinsic flexibility and quaternary structural variation within IgE-Fc. Intriguingly, both well-ordered carbohydrate and disordered polypeptide can be seen within the same Cε3 domain. A simplified method for comparing the quaternary structures of the Cε3 domains in free and receptor-bound IgE-Fc structures is presented, which clearly delineates the FcεRI and CD23 bound states. Importantly, differential scanning fluorimetric analysis of IgE-Fc and Fcε3–4 identifies Cε3 as the domain most susceptible to thermally-induced unfolding, and responsible for the characteristically low melting temperature of IgE. •The Cε3 domains of IgE are most susceptible to thermally induced unfolding determined by differential scanning fluorimetry.•The Cε3 domains are responsible for the characteristically low melting temperature of IgE.•The Cε3 domains exhibit the greatest intrinsic flexibility.•Quaternary structural diversity of Cε3 domains is compared across all known structures using a simplified single parameter.•Human IgE-Fc and Fcε3–4 domain structures are determined at the highest resolutions yet reported (1.75 Å and 2.0 Å). It exerts its effects through its two principal receptors, FcεRI on mast cells, basophils and antigen-presenting cells, and CD23/FcεRII on B cells. The former mediates allergic hypersensitivity, the latter regulates IgE levels, and both contribute to allergen presentation to the immune system koff = 1.9 × 10− 4 s− 1), and this is reproduced with the IgE-Fc fragment containing the dimer of Cε2, Cε3 and Cε4 domains koff = 3.2 × 10− 3 s− 1) a ≈ 1010 M− 1). The CD23 binding site lies principally within the Cε3 domain but with a small contribution from Cε4 a ≈ 107 M− 1), with one binding site in each chain. The Cε3 domain is thus the critical domain for receptor binding.IgE causes long term sensitisation of mast cells due to the uniquely slow off-rate for the antibody/receptor complex , CD23 (sCD23) and various inhibitors. The first crystal structure was of Fcε3–4 bound to sFcεRIα (1F6A) In FcεRI-bound structures, the Cε3 domains move apart into an “open” conformation to accommodate receptor binding, compared with the uncomplexed fragments. However, binding to CD23 between the Cε3 and Cε4 domains causes the Cε3 domains to adopt a more “closed” conformation, and in this way the binding to the two receptors, while occurring at distant sites at either end of the Cε3 domains, is mutually exclusive m values are typically ~ 72 °C), was confirmed The thermal lability of IgE has been recognised for a long time. Even before the recognition of IgE as an antibody, the loss of “reaginic” activity in atopic sera by heating to 56 °C was reported Here we report two new crystal forms of Fcε3–4 (at 2.26 Å and 2.2 Å resolution) together with one of the earlier forms now solved at the highest resolution yet reported (2.0 Å) and IgE-Fc also solved at the highest resolution yet reported (1.75 Å); new details concerning carbohydrate/protein interactions are revealed. We compare these with all previous structures, and propose a simplified, single-parameter descriptor for the quaternary structure of the Cε3 and Cε4 domains. An analysis of B-factors for all IgE-Fc and Fcε3–4 structures is also reported, and correlated with experimental data from differential scanning fluorimetry (DSF), which reveals the Cε3 domains to be responsible for the thermal sensitivity of IgE.22.11 ‘large’ form were grown in 0.1 M SPG  at pH 6.0 (Hampton Research) and 25% (w/v) PEG1500 using a reservoir volume of 70 μL and drops comprising 100 nL protein solution (10 mg/mL) and 100 nL reservoir. The Fcε3–4 P21 ‘small’ form was grown in 0.1 M MES at pH 5.5, 25% (w/v) PEG 4000 (Hampton Research) and 0.15 M ammonium sulphate using a reservoir volume of 80 μL and drops comprising 100 nL protein solution (7.7 mg/mL) and 200 nL reservoir. IgE-Fc crystals were grown in 0.1 M Tris HCl at pH 8.5 and 25% (v/v) PEG 550 MME (Hampton Research), using a reservoir volume of 80 μL and drops comprising 250 nL protein solution (6 mg/mL) and 250 nL reservoir.IgE-Fc and Fcε3–4 were prepared as reported earlier 1 ‘large’ crystals in 0.1 M sodium di‑hydrogen phosphate at pH 4.0 and 30% (w/v) PEG 3350, Fcε3–4 P21 ‘small’ crystals in 25% (w/v) PEG 4000 and 18% (v/v) PEG 400, and IgE-Fc crystals in 0.1 M Tris-HCl at pH 8.5, 25% (v/v) PEG 550 MME and 10% (v/v) PEG 400, before flash-cooling in liquid nitrogen.Crystals were cryoprotected as follows: Fcε3–4 P1 crystals were cryoprotected in 0.1 M SPG at pH 4.4 and 30% (w/v) PEG 1500, Fcε3–4 P22.21 ‘large’ crystal form (beamline I04), data were collected using an ADSC Q315r detector . For the Fcε3–4 P21 ‘small’ crystal form (beamline I02) data were collected using a Pilatus 6M-F detector . For the IgE-Fc crystal (beamline I04-1), data were collected using a Pilatus 6M-F detector . Data were integrated with DIALS using the Xia2 package Data were collected at the Diamond Light Source . For the Fcε3–4 P1 crystals (beamline I02), data were collected using an ADSC Q315r detector . For the Fcε3–4 P21 ‘large’ and P21 ‘small’ structures, respectively. For the IgE-Fc structure, PDB entry 2WQR 1 ‘large’ structure is the same crystal form as 3H9Y, and the IgE-Fc structure is the same crystal form as 2WQR, but both structures represent an improvement in terms of resolution. Refinement was carried out in iterative cycles with PHENIX The number of molecules in each asymmetric unit  was corr1 ‘small’ structure: chain A 336–365, 370–424, 427–544; chain B 336–362, 369–422, 429–544. In the Fcε3–4 P21 ‘large’ structure: chain A 336–424, 428–546; chain B 336–363, 369–545; chain C 335–362, 369–421, 428–544; chain D 336–366, 369–422, 428–544. In the Fcε3–4 P1 structure: chain A 339–360, 372–389, 398–419, 430–544; chain B 338–362, 369–393, 398–421, 428–544; chain C 336–544; chain D 335–362, 369–421, 429–498, 503–544; chain E 336–362, 369–423, 425–454, 457–544; chain F: 339–361, 372–376, 379–381, 383–389, 394–418, 431–454, 456–544. Carbohydrate was built at Asn394 as follows: IgE-Fc structure chain A GlcNAc2Man3; chain B GlcNAc2Man5. In the Fcε3–4 P21 ‘small’ structure: chain A GlcNAc2Man3; chain B GlcNAc2Man3. In the Fcε3–4 P21 ‘large’ structure: chain A GlcNAc2Man2; chain B GlcNAc2Man5; chain C GlcNAc1Man3; chain D GlcNAc 2Man5. In the Fcε3–4 P1 structure: chain A GlcNAc1Man3; chain B GlcNAc2Man3; chain C GlcNAc2Man3; chain D GlcNAc2Man5; chain E GlcNAc2Man3; chain F GlcNAc0Man3. For the quaternary structural comparisons, the angle defined by the three Cα atoms X337-X497-Y337 or Y337-Y497-X337 (where X and Y refer to two paired chains) was measured using the PyMOL Molecular Graphics System . NB: where residues 337 or 497 were not modelled in the structure , the measurement was not taken.In the IgE-Fc structure, residues were built as follows: chain A 228–544; chain B 225–545. In the Fcε3–4 P22.3All chains of the four new structures presented here, together with all published Fcε3–4 and IgE-Fc structures, were analysed. To compare B-factors derived from different structure determinations, each was normalised by division by the average B-factor for the whole Cε3–4 chain (for comparison of Cε3 and Cε4) or Cε3 domain . The Cε3/Cε4 boundary was defined as residues 437/438; the Cε4-distal region of Cε3 was defined as residues 336–339, 359–372, 387–400 and 419–431; the Cε4-proximal region of Cε3 was defined as residues 340–358, 373–386, 401–418 and 432–437.2.4Thermal stability assays were carried out according to a published protocol 2.52O, 10% acetonitrile and 0.1% formic acid prior to mass spectrometric analyses. Samples at concentrations between 8 μg/mL and 80 μg/mL were directly infused at 25 μL/min into a Bruker MaXis ultrahigh-resolution electrospray ionization quadrupole time-of-flight mass spectrometer. The multiple charge states were deconvoluted using the manufacturer's maximum entropy algorithm . Due to glycosylation heterogeneity, a series of masses were observed with well-defined mass differences of 162 Da, corresponding to variations in the number of mannose residues present in the sample The two protein samples, either IgE-Fc or Fcε3–4, were dialysed into a solution of H33.11 ‘small’ contain three molecules (six chains) in the asymmetric unit (solved at 2.2 Å resolution), and one molecule (two chains) in the asymmetric unit (solved at 2.26 Å) respectively. They therefore offer four additional and unique packing environments for analysis of the intrinsic flexibility of this molecule  in the asymmetric unit, has been reported earlier 1212, with one molecule (two chains) in the asymmetric unit has been determined to 1.75 Å resolution n) is N-linked to Asn394 in each Cε3 domain, as seen in previous crystal structures of IgE-Fc and Fcε3–4 at different resolutions, and with varying degrees of disorder. There is heterogeneity in the overall mannose content and in the length of the branches in any population of IgE-Fc or Fcε3–4 molecules (2Man3 (chain A) (2Man5 (chain B)   (2Man2 (chain A) (1Man3 (chain C) (2Man3 (chains C and E) (2Man5 (chain D) (2Man3 (chain B) (1Man3 (chain A) (0Man3 (chain F) olecules  [12], [1chain A) Ciii and chain B) Ci; Fcε3–A and B) Cii; Fcε3B and D) Ci, GlcNAchain A) Civ and Gchain C) Cv; Fcε3–C and E) Cii, GlcNchain D) Ci, GlcNAchain B) Ciii, Glcchain A) Cv and Glchain F) Cvi.Fig. 1 ‘large’ crystal form and chain F in the Fcε3–4 P1 crystal form is that while the terminal units are well defined, there is poor (or absent) electron density at a contour level of 1σ in the 2FoFc map for the first GlcNAc (P21 ‘large’) or first two GlcNAc units (P1), as well as for the covalent connection between these and Asn394 . While this has been seen in other Fcε3–4  and the second at 64 °C (Tm2). The latter is taken to be the unfolding of the Cε2 domains, occurring after that of Cε3 and Cε4. Both Tm1 and Tm2 decrease with increasing urea concentration , which may be due to differences in the aggregation properties of the two proteins as denaturation occurs. Aggregation reduces the hydrophobic surface area to which Sypro-orange molecules can bind, thus reducing the signal. The Cε3 domains, and in particular the N-terminal regions, are more flexible  and perhaps inherently more prone to unfolding in Fcε3–4 compared with IgE-Fc, thus promoting aggregation of the former. However, as seen in the B-factor analysis A, the stnditions .4The Cε3 domains are central to the function of IgE since they contain the receptor-binding sites for both FcεRI and CD23. These two sites are located at opposite ends of the Cε3 domain, yet their binding is mutually incompatible Analysis of the B-factors from all the published crystal structures together with the new structures presented here confirms the enhanced intrinsic flexibility of the Cε3 domains and demonstrates that this is most pronounced in their Cε4-distal regions. The ability to average over so many different structures in different crystal forms, overcomes any local effects of crystal packing constraints. The Cε3 domains are stabilised by FcεRI binding , consistm values are typically ~ 72 °C), was confirmed i.e. in the Cε3–4 domains m values similar to that of intact IgE, and the second involving the Cε2 domains. Since the Tm1 and Tm values for IgE-Fc and Fcε3–4 respectively are very similar, the Cε2 domains have only a marginal influence on the thermal stability of the Cε3 domains.The thermal stability data that we present here implicate the Cε3 domains as the most susceptible to denaturation within IgE-Fc. The thermal lability of IgE has been recognised for a long time. Even before the recognition of IgE as an antibody, the loss of “reaginic” activity in atopic sera by heating to 56 °C was reported m values: glycosylated, 71 °C and 82 °C for Cγ2 and Cγ3 respectively; fully deglycosylated, 66 °C and 82 °C m by 4 °C 1 “large” form and chain F of the P1 form, the flexibility and/or disorder is so great that there is no visible electron density (in vitro and in vivoIn IgG-Fc, glycosylation of the complex type, N-linked at the structurally homologous Asn297 in each Cγ2, contributes to the thermal stability of this domain, and the two domains have distinct T density B&D; this density . In IgG-5MOI; Fcε3–4 P21 ‘small’, PDB ID: 5MOJ; Fcε3–4 P21 ‘large’, PDB ID: 5MOK; IgE-Fc, PDB ID: 5MOL.Coordinates and structure factors have been deposited in the Protein Data Bank with accession numbers: Fcε3–4 P1, PDB ID: Transparency document.Image 2


The loss of Arctic sea ice is a conspicuous example of climate change. Climate models project ice-free conditions during summer this century under realistic emission scenarios, reflecting the increase in seasonality in ice cover. To quantify the increased seasonality in the Arctic-Subarctic sea ice system, we define a non-dimensional seasonality number for sea ice extent, area, and volume from satellite data and realistic coupled climate models. We show that the Arctic-Subarctic, i.e. the northern hemisphere, sea ice now exhibits similar levels of seasonality to the Antarctic, which is in a seasonal regime without significant change since satellite observations began in 1979. Realistic climate models suggest that this transition to the seasonal regime is being accompanied by a maximum in Arctic amplification, which is the faster warming of Arctic latitudes compared to the global mean, in the 2010s. The strong link points to a peak in sea-ice-related feedbacks that occurs long before the Arctic becomes ice-free in summer. For example, the linear trend in northern hemisphere monthly-mean sea ice extent in summer and autumn is −6.6 ± 1.2% per decade on average , to 1.43 m . As a consequence of decreasing extent and thickness, Arctic sea ice volume is dropping too5, by 20% for the mean volume of the 2000s compared to 1980–2000  demonstrate realistic sea ice declines  ice cover to seasonal cover (mainly in winter) in the 21st century14. For example, sea ice projections in the fourth assessment of the Intergovernmental Panel on Climate Change (IPCC) suggest that a large part of the Arctic Ocean will be seasonally ice-covered by the end of the twenty-first century7.The rate of Arctic sea ice decline projected by coupled climate models in the 21st century varies greatlynes Figs  and SI5.6 km2  for at least five consecutive years16. For example, analysis of sea ice projections in the fifth IPCC assessment climate models16 suggests that this threshold will be reached in 2054–2058 under a high emission scenario . Under a medium-mitigation scenario (RCP4.5) September sea ice extent is projected to reach 1.7 × 106 km2 in the early 2060s, followed by a leveling off16. Nevertheless, model-based estimates of when the Arctic will be ice-free in September are associated with substantial uncertainty14.To quantify this change in seasonality, attention has focused on when the Arctic will be ice-free in summer. Typically, this question is addressed by estimating when September sea ice extent is less than 106 km2 threshold on summer ice extent. The date at which the Arctic is declared to be ice free depends on this threshold in an obvious way.Although an important issue, this threshold approach emphasizes the final stages of the transition, when the summer sea ice is about 85% gone. It does not address the earlier stages of the transition which involve dramatic changes to the Arctic climate system. Furthermore, the ice-free-summer threshold is a binary criterion that cannot measure the continuous transition in the sea ice seasonal cycle. It relies on a somewhat arbitrary , of a variable x(t), such as sea ice extent, is the seasonal change (the range) in x for a given year T divided by its mean value over the season,t1 and t2 are the times of consecutive extreme values of x, typically separated by about six months. By definition, |x(t2) − x(t1)| equals the seasonal range between the monthly-mean winter maximum sea ice extent x(t1) and the summer minimum x(t2), and Se(T) is plotted against time T for each year. Further discussion of the seasonality number, including pedagogical examples  satellite sea ice index18. They show the well-known decline in summer-time sea ice extent in recent years, from annual minima around 7 × 106 km2 in the early 1980s to record-breaking lows of about 4.3 × 106 km2 and 3.6 × 106 km2 in 2007 and 2012. In contrast, the winter-time sea ice maxima losses are smaller. Prior to the satellite observations, sea ice estimates are taken from a recent product19 of gridded historical ice observations (see Methods), which show weaker trends.Observations and CMIP5 model simulations of the Arctic-Subarctic, i.e. the total northern hemisphere, sea ice extent and the corresponding seasonality numbers are shown in Fig. 20). The PIOMAS estimates cover the same time period as the NSIDC data, but the seasonal range is less; mainly because they underestimate the winter-time maxima in sea ice extent. The summer-time PIOMAS minima are also typically greater than from the NSIDC data, but they nearly coincide since 2007.Figure Se < 1; Supplementary Information), but are lower, around 0.55. Although the PIOMAS sea ice extent means are smaller than those from the NSIDC data, the NSIDC seasonal ranges are larger . The Antarctic sea ice system is well-known to be in the seasonal regime22, although other studies have not quantified the seasonality with an objective metric like Se. The Arctic-Subarctic sea ice extent is now approaching the same degree of seasonality, with fluctuations, and in 2012 it peaked at Se = 1.16, overlapping the envelope of Antarctic values. The seasonality number for NSIDC sea ice area  for the northern and southern hemispheres is shown in Fig. Se is very well correlated with Se for sea ice extent. It is greater, however, by about 0.19 on average. The reason is that the mean sea ice area is less than the mean sea ice extent in general, so the denominator in (1) is smaller for sea ice area. Still, the northern hemisphere sea ice area seasonality now overlaps the Antarctic values, as it does for sea ice extent.For comparison, Fig. 16. Specifically, these models meet criteria on realism of their September ice extent, their trend in September ice extent, their monthly climatology and the magnitude of their seasonal cycle. For the 20th century, the multi-model mean shows realistic sea ice extent cycles between 8 and 16 × 106 km2  declines are smaller than those in the summer-time (September mean). The rates of decline are smaller than the NSIDC data in the summers of the 2010s.Results from fifteen CMIP5 coupled climate models are also shown in Fig. Se passes one . Indeed the linear correlation coefficient between these variables is −0.97 for the NSIDC data. The linear correlation fails for multi-year sea ice extent less than about 2 × 106 km2 (for the CMIP5 models), however, and for the Antarctic data in Fig. The dominant factor in controlling 23 and using assimilation24, essentially similar to the PIOMAS. For most of the last decade, the PIOMAS seasonality numbers have been in the same range as the Antarctic estimates. The PIOMAS Arctic-Subarctic sea ice volume seasonality exceeds the highest Antarctic Se in 2012, however. The CMIP5 multi-model mean sea ice volume seasonality numbers agree well with the PIOMAS estimates although they under-estimate the rate of increase in the 2010s. The RCP8.5 sea ice volume projections enter the seasonally-episodic regime  after about 2060.Corresponding sea ice volume estimates from PIOMAS and the CMIP5 models are in Figs 26 with implications for sea ice melt27, atmospheric moisture and cloud cover29, momentum transfer into the ocean31, primary production32, and ecological dynamics33. We illustrate the impact of the transitioning of the Arctic sea ice cover to the seasonal regime on the climate system by a simple example: the role of the ice cover in Arctic amplification. Arctic amplification is the faster rate of Arctic warming compared to the global average  during autumn (September to November mean).Seasonality in the Arctic-Subarctic sea ice system affects diverse physical, chemical, and biological processes such as surface ocean warmingage Fig. , which i41.Sea ice influences, among other variables, Arctic SAT by moderating both absorption of insolation at the surface and property exchange between the ocean and atmosphere. As sea ice retreats during the melt season it exposes the darker ocean to direct insolation, which then warms locally over the summer, causing further ice retreat. Seasonally, the warmer ocean accelerates ice melt and delays re-freezing. Inter-annually, less sea ice survives summer melt and the shorter freezing season promotes overall thinner ice which is more prone to retreat during the next melt season. This positive ice-albedo feedback is an important process under global warming42. In conjunction with advection from lower latitudes of warm and moist air, this was a key factor leading to unusual high surface temperatures in the Arctic in autumn 201643. Moreover, it shows that Arctic amplification is influenced by non-local processes. Sea ice retreat, and its consequences, may cause it to vary in time, however. For example, the positive ice-albedo feedback is most effective when a thin ice cover exists because it requires the contrast between dark open water that warms and bright sea ice that rapidly melts. Due to the topographic enclosure of the Arctic Ocean by land, sea ice retreat is limited and the ice-albedo feedback will disappear once the ocean becomes ice-free in the sunlit season. To the extent that the ice-albedo feedback influences Arctic amplification, Arctic amplification should be greatest during the time of fastest year-to-year ice retreat.The sea ice cover also impedes heat and moisture fluxes from the ocean to the atmosphere. In autumn and winter the open ocean can significantly warm the atmosphere in the absence of sea iceTo investigate this idea further we focus on two quantitative diagnostics. We study the decadal increase in autumn mean surface air temperature (SAT) north of 70°N Fig. . By limiWe quantify the peak effectiveness of sea ice feedbacks using the year of greatest combined summer sea ice retreat and subsequent autumn warming . This finding suggests that the seasonality number is closely related to the effectiveness of sea ice feedbacks in modulating Arctic amplification. The multi-model mean Arctic amplification factor decreases significantly after 2015 reaching a value of 2.4 toward the end of the study period in 2085 . They also facilitate an inter-annual decrease of the mean ice thickness and area, thus decreasing the denominator of (1). Both effects increase the seasonality number, with greatest impact when sea ice processes are most effective, i.e. when there is still a contrast between ice-covered and open ocean in the Arctic. Thus it is reasonable to hypothesize that the change from the perennial to the seasonal regime occurs when sea ice feedbacks are most effective. For these reasons, we speculate that the decisive transition of the Arctic climate and eco-systems will occur during the next decade, long before the threshold of an ice-free Arctic is reached.44. Nevertheless, it coexists with other (partly-dependent) feedback mechanisms known to cause Arctic amplification. They include sea ice processes unrelated to albedo (involving modified sea surface temperature), the atmospheric lapse rate feedback45, temperature effects46, and surface albedo change due to adaption processes in clouds, snow cover and vegetation34.We attribute the reduction of autumn Arctic surface warming and the reduction in surface-based Arctic amplification to summer sea ice loss. The processes involved include, among others, a reduced ice-albedo feedback and reduced capability of sea ice to moderate ocean-atmosphere exchange. This is a plausible explanation because the timing of peaks in Arctic SAT growth rate, Arctic SIA loss rate, and Arctic amplification coincide Fig. . The ice47 describes the link between sea ice loss and surface ocean warming, emphasizing the importance of the timing of ice retreat. In the 2010s ice retreat occurs earlier in the marginal shelf seas of the Arctic compared to the 1980s, which enables greater warming there. In the central Arctic Ocean, ice retreat commences late in summer, after August 1, when the net heat flux into the ocean is weaker and increasing winds stir the ocean more strongly. For a significant impact on Arctic amplification, these regions need to become ice-free at least six weeks earlier to substantially warm over the summer47. Since the Arctic has already transitioned into a state of predominantly first-year ice48 we hypothesize that this timing sensitivity is most effective in the 2010s, adding to the current peak in Arctic amplification.Future research should investigate the causal link between the loss of summer Arctic sea ice, concomitant albedo decrease, ocean heat uptake, ocean surface warming, and long-wave radiation, and the reduction in Arctic amplification seen in Fig. 16. We select these models because our focus is on the presently-occurring transition from the perennial to the seasonal ice regime, and the associated peak in Arctic amplification. Clearly, uncertainty grows projecting into the future. Indeed, the set of CMIP5 models selected here may not have the most realistic projections49.The CMIP5 models considered here exhibit realistic sea ice properties compared to the NSIDC dataRegardless, the real Arctic sea ice system passed into the seasonal regime recently Figs  and 2, aet al.19 gridded monthly sea ice data which synthesizes the (sparse) records of sea ice extent, primarily from hand-drawn charts50. Similarly, we use HadISST data21 for the Antarctic before the satellite era. The PIOMAS product20 blends ice concentration and sea-surface temperature data with a dynamical sea ice and ocean circulation model.The NSIDC data product comprises merged satellite passive microwave measurements from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder, the older DMSP Special Sensor Microwave/Imager, and the Nimbus-7 Scanning Multichannel Microwave Radiometer. The NASA Team algorithm is used to derive sea ice concentrations. These data  are available since November 1978. Prior to that time, we use the Walsh Sea ice extent is defined as the area of ocean enclosed by the 15% sea ice concentration contour, which is computed from the sea ice distribution. Sea ice area is the integrated area covered by ice. It is a metric that avoids the non-linearity inherent in sea ice extent, but has a larger observational uncertainty. The correlation coefficient for northern hemisphere sea ice extent and sea ice area is 0.94. Sea ice volume is the area-integrated product of sea ice concentration (ice-covered area fraction) and thickness and is harder to estimate than either sea ice extent or area because of the uncertainty in ice thickness.4 km2 , which gives an uncertainty in Se of a few parts in a thousand. Finally, different algorithms exist to process the satellite data, but they do not cause large uncertainty in the seasonality number: for example, the NASA Bootstrap sea ice product (version 2.1)51 gives an Se timeseries that differs from that of the NSIDC data in Fig. The NSIDC satellite data contain various uncertainties, but they all cause small errors compared to the inter-annual changes and the decadal trend seen in the seasonality number in Fig. d from the origin for each year T, as follows  sea ice area (SIA) and autumn (September through November mean) surface air temperature (SAT) anomalies (with respect to the 1960–1980 mean) averaged over the Arctic Ocean  and globally are computed from CMIP5 model output which is smoothed with a 30-year boxcar filter. Year-to-year differences are displayed in Fig. ows Fig. :2\documeThe algorithm is applied to the CMIP5 model realizations combining the historical runs and RCP8.5 climate projections and to the multi-model mean  and to reduce the impact of interannual to decadal internal variability. This procedure does not fully eliminate internal variability, however, which differs between modelsSupplementary information pdf