In times of “Precision Medicine” it is fundamental to identify the individual disease phenotype in order to provide an individualized therapy for every patient. This concept is also becoming increasingly important for the treatment of allergic diseases. Thanks to the biological engineering of recombinant and native allergens for the assessment of allergen-specific IgE antibodies, it is now possible to easily obtain the individual sensitization profile of a patient. This allows the allergist to precisely identify the primary elicitor of an IgE response and, based on this knowledge, to choose the best treatment option. Several studies have observed the longitudinal evolution of sensitization profiles and identified a phenomenon termed “molecular spreading,” which describes a broadening of the recognized allergen spectrum from a source over time. Additionally, the identification of marker proteins, which can trigger an IgE response or correlate with an increased risk for certain clinical symptoms, helps to establish an individual risk profile. This information may not only affect the decision-making concerning immunotherapy, but also opens up avenues for future investigations with regard to prevention strategies. We provide here an overview on the role of individual sensitization patterns and their predictive value. It is long since no secret that allergic diseases are continuously gaining importance in the industrialized world. While many scientists are trying to get to the bottom of these numbers, the situation for a multitude of practitioners is aggravating as they see themselves confronted not only by affected patients demanding treatment, but also with anxious parents seeking recommendations for effective prevention strategies for their children. In order to provide useful information for the doctors to pass on to their patients, research is being performed restlessly, often with changing results. Nevertheless, all research has one least common denominator: in order to prevent a disease and provide proper treatment, it is fundamental to understand the underlying biological and immunological mechanisms from scratch, including those taking place before and finally leading to the onset of the first symptoms.in-vivo methods like skin tests or standardized allergen provocation. While skin tests can also be performed with raw allergens , allergenic extracts served as a base for serological assessments during the last decades. As these reagents are sometimes poorly standardized and their composition may vary significantly between preparations from different manufacturers, the precision of results left space for improvement. This gap is now progressively being covered by new diagnostic options based on advanced molecular and structural biology. Thanks to fast-placed technological advances, it became possible not only to identify and purify an increasing number of allergenic molecules with their isoforms, but also to produce them in large quantities through sequencing and cloning. This new opportunity goes along with the need for accurate diagnostic tools in times of “Precision Medicine” which requires the detailed knowledge of the patient's disease phenotype in order to provide individualized treatment options. Molecular singleplex and multiplex assays do not only provide detailed information on the patient's sensitization profile, but also on possible cross-reactivity. They also enable us to observe the longitudinal evolution of complex IgE and IgG repertoires in birth cohort studies, which is fundamental for the perception of pre-clinical immunological phenomena. Once these are understood in depth, more efficient long-term therapies, as well as prophylactic measures could possibly be identified. Of course, humoral responses are only one part of this complex journey, to which the following paragraphs will draw a roadmap.Since the discovery of IgE antibodies by Ishizaka, Bennich and Johannson in the late sixties, serological tests have become an integral part of the allergological work-up, combined with the clinical history and In order to evaluate a child's risk of developing an allergic disease as early as possible, ideally already in a pre-clinical stage, it is fundamentally important to have a clear idea of the immunological processes distinguishing the non-atopic from atopic individuals. As IgE antibodies appear during the first year of life and their quantity is minor, especially when compared to IgG levels, early observations should not be limited to allergen-specific IgE antibodies. The production of allergen-specific IgG has been broadly studied , especiaThese results indicate that the route and dose of exposure are of prominent importance for the evoked immune response. In order to analyse the effect of different ways of exposure to almost identical allergenic molecules, we examined the natural evolution of allergen-specific IgG antibodies toward the birch-pollen related PR.10 molecules throughout childhood . This loThe chronological interrelation between the IgG and IgE responses further leads to the hypothesis that both isotypes may have been produced by the same B cell subset passing through an indirect class switch. This hypothesis has also been recently supported by immunoglobulin heavy chain mutational lineage data giving evidence that the primary IgE source in human subjects is the secondary isotype switching of mutated IgG1-expressing B cells . This faIt has to be mentioned though, that independently from the subclass, the assessment of IgG antibodies has no diagnostic value so far, especially not in food allergy. Further research needs to be performed in order to evaluate its diagnostic and/or predictive value.Once a patient is seeking the opinion of a specialized allergist, he or she is usually already suffering from a significant loss of life quality due to his/her allergic symptoms. The serological assessment of IgE antibodies at this time point then typically shows a strong response toward one or, more likely, already various allergen sources. This information is usually obtained through tests based on allergen extracts. These do provide significant information and are a substantial diagnostic tool, however, especially in poly-sensitized patients, they cannot always reliably indicate the eliciting source primarily responsible for the clinical symptoms . The diaAs mentioned before, IgE antibodies are mostly already present in the blood once a patient visits the allergist. But when did this sensitization begin? How long was the pre-clinical phase and what could he observe if the allergist had seen his patient before the onset of symptoms? In order to answer these questions, birth cohorts are an essential pool of resources, which has been used, to assess the longitudinal evolution of IgE responses toward grass pollen molecules in childhood. Their analysis of 177 grass-allergic children revealed a clear structure in the observed sensitization profiles . StrikinPhleum pratense molecules. Nevertheless, Hatzler et al. were able to identify a clear progression in the IgE patterns of 177 participants of the MAS study. While in over 75% of the cases the IgE response was clearly initiated by Phl p 1, further responses mainly appeared according to the following patterns: Phl p 1, then Phl p 4 and Phl p 5; then Phl p 2, Phl p 6, and Phl p 11; and then Phl p 12 and Phl p 7.After the identification of initiator molecules, the subsequent course of sensitization is of fundamental interest. An analysis of the individual IgE profiles among 176 grass-pollen-allergic children in Italy resulted in the identification of 39 different sensitization patterns . This brPhleum pratense molecules Phl p 1, Phl p 2, Phl p 5, and Phl p 6 were associated significantly with moderate to severe symptoms  allergens . Children belonging to group 1 and to the “complete” mite sensitization group presented a significantly increased risk for asthma, eczema and rhinitis, although the “complete” mite sensitization group had the highest odds ratio for asthma. Unlike the children with sIgE to grass pollen, the longitudinal IgE-evolution had no impact on mite-allergic subjects. In summary, individual sensitization patterns toward airborne allergens can be described as diverse but an overall trend toward a broadening of the IgE response with clinical relevance over time is common to most patients.Earlier, Custovic et al. tested the sera of 235 children of the Manchester Asthma and Allergy Study birth cohort for specific IgE toward grass pollen and mite allergens . This stPhleum pratense proteins are related to higher probabilities of certain clinical outcomes, such as e.g., Phl p 7 which has been shown to be associated with a higher risk of developing asthma  had a significantly elevated risk of mite-related allergic rhinitis and asthma, or both, than not sensitized participants. The authors could identify various factors leading to a broadening of the IgE repertoire. Especially an early onset of sensitization, a history of parental hay fever, and higher levels of exposure to mites could be identified as parameters associated with a more complex, polymolecular IgE sensitization pattern. Furthermore, this study also discovered certain marker molecules, as IgE to Der p 1 and/or Der p 23 at the age of 5 or younger predicted a higher risk of developing asthma at school age .In birch pollen allergy, increased risk of incidence and persistence of seasonal allergic rhinitis up to the age of 16 years was shown to correlate with increasing levels of Bet v 1-specific IgE or increasing numbers of IgE-reactive PR-10 proteins at the age of 4 years. This observation has been made among 764 children from the Barn/Children Allergi/Allergy Milieu Stockholm Epidemiologic (BAMSE) study birth cohort who were examined at the age of 4, 8, and 16 years. Additionally to these predictive results, some general observations on the evolution of IgE responses toward birch-related allergens were made. Confirming our previous observations  and Can f 1 (dog), allows a significantly better prediction of cat and dog allergy  than IgE assessed with cat and dog extract. The same could be observed for a polysensitization to either cat or dog allergen molecules, underlining the advantage of CRD in allergy diagnostics.The expanded information obtained through molecular allergy diagnostics raises new questions, not only concerning the decision for or against an immunotherapy, but also on the correct timing for its application as well as the composition of therapeutic agents . AlthougAn important factor influencing the efficacy of any therapy is its suitability for the disease to be treated. As described previously, our theoretical exercise showed that in only 4% of the 176 examined grass-pollen allergic patients, the sensitization profile matched with the composition of a standardized immunotherapy solution . In 29% Another option to increase the success rates of immunotherapy is a good timing. Unfortunately, most of the treatments are currently being initiated only years after the first onset of symptoms and often after a significant loss of life quality. Many patients and attending doctors first observe the natural course of clinical symptoms and especially the former hope for their symptoms to dissolve, without potentially painful treatment with subcutaneous injections. Unfortunately only very few of the clinical manifest allergies tend to dissolve naturally. On the contrary, many mono-sensitized children—especially those with parents affected by hay fever —tend to As mentioned previously in this chapter, a “molecular spreading” phenomenon has been described for various airborne allergens with the same final outcome: patients with a broad spectrum of IgE to many molecules of one source are more likely to suffer from more severe clinical symptoms than those exhibiting a less complex sensitization profile. Reasoning about potential starting points to prevent this late clinical phase ends in two to three options for treatment . CurrentA recent meta-analysis of 32 studies, however, could not depict a statistically significant reduction of the risk to develop a first allergic disease by early allergen-specific immunotherapy (AIT) . Yet, a This means that, albeit the disease prevention is not possible, its onset could be delayed and its symptoms moderated, maybe even prevented with regard to asthma.Both authors have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


T) and pressure (P). Herein, molecular dynamics simulations show evidence of a high-density orthorhombic phase, termed ice χ, forming spontaneously from liquid water at room temperature under high-pressure and high external electric field. Using free-energy computations based on the Einstein molecule approach, we show that ice χ is an additional phase introduced to the state-of-the-art T–P phase diagram. The χ phase is the most stable structure in the high-pressure/low-temperature region, located between ice II and ice VI, and next to ice V exhibiting two triple points at 6.06 kbar/131.23 K and 9.45 kbar/144.24 K, respectively. A possible explanation for the missing ice phase in the T–P phase diagram is that ice χ is a rare polarized ferroelectric phase, whose nucleation/growth occurs only under very high electric fields.Water can freeze into diverse ice polymorphs depending on the external conditions such as temperature ( Water can crystallize in different ice polymorphs according to temperature and pressure conditions. Here the authors predict by molecular dynamics simulations a new ice phase spontaneously forming at room temperature under high pressure and high electric field. T–P phase diagram due to the extraordinary adaptability of water’s hydrogen-bonding networks to different environmental temperatures (T) and pressures (P). To date, at least 17 crystalline ice phases  have been produced in the laboratory3. A number of “computer ice” phases have also been predicted from molecular dynamics (MD) simulations and density functional theory (DFT) computations, including very-low-density porous ices (density ρ < 0.85 g cm−3) such as s-III4, s-IV5, ice ITT6, and sL;7 low-density ices (0.85 g cm−3 ≤ ρ ≤ 1.0 g cm−3) such as silica-like ice polymorphs10, ice 011, ice i, and ice i’;12 high-density ices (1.0 g cm−3 < ρ < 1.4 g cm−3) such as ice B13; and super-high-density ices (ρ > 2.0 g cm−3), which entail partial ionization20.Ice exhibits an exceptionally rich 22 and also has been suggested to exist on Uranus and Neptune24, although the recent theoretical calculations indicated that it would be antiferroelectric ice in nature25. The ice VIII is antiferroelectric, but it is likely to be ferroelectric in an applied electric field26. The ice XV, the hydrogen-ordered form of ice VI phase, is antiferroelectric 1, whereas it is predicted to be a ferroelectric Cc hydrogen-ordered structure based on local density functional approach28. However, Del Ben et al.29 used high-level ab initio computation and predicted that the antiferroelectric phase is indeed the ground state, suggesting that more accurate density-function approaches should be considered (see below). The ice Ic is a metastable ice crystal with hydrogen disordered, but it is predicted to be ferroelectric when all hydrogens are ordered  based on computer simulations31. Indeed, as the water dipole moments can add up to produce a net moment or cancel each other, either ferroelectric ice polymorphs can exist for special crystalline structures. The fabrication of bulk ferroelectric ice, however, is still a challenging task, as without the assistance of dopants as catalysts, the phase-transformation time for a single-phase ferroelectric ice is estimated to be on the order of 104 years23. Hence, pure bulk ferroelectric ice is rare in nature.Among the 17 bulk ice phases observed in the laboratory, ice XI is believed to be ferroelectric37. In addition, several known ice structures have already been determined from computer simulations of electrofreezing under ultra-high electric fields41. Svishchev and Kusalik41 showed from their MD simulations that a polar crystal with the structure of cubic ice Ic can be achieved via electrofreezing of supercooled liquid water. In a later work, they demonstrated a MD simulation of the formation of a quartz-like ice polymorph through electrofreezing10. This quartz-like ice structure was originally proposed by Bernal and Fowler42 as a type of dense ice polymorph. Stutmann40 investigated the effects of an ultra-high electric field (tens of V nm−1) on bulk water. When the electric field magnitude was 30 V nm−1, the MD simulation showed that liquid water transforms into a crystal-like structure40, which can be either defective polar cubic ice or amorphous ice. Recently, Hu et al.38 provided simulation details on the behaviour of glassy water in external electric fields, including the formation of a body-centred-cubic (bcc) ice phase at 77 K. This bcc ice phase is polarized ferroelectric ice VIII, as determined by its lattice constant of 3.19 ± 0.17 Å and oxygen–oxygen (O–O) radial distribution function (RDF)44. However, Saitta et al.45 showed from ab initio MD simulation that the threshold strength of electric field that makes water molecules dissociate is 3.5 V nm−1. As such, the electric fields considered in aforementioned simulations were considerably higher than the threshold.Can a pure bulk ferroelectric ice be produced in the laboratory by other means? The answer to this question is still highly sought today. One possible way could be through the application of an ultrahigh electric field for the electrofreezing of water. Electrofreezing is known to play an important role in many natural processes, ranging from tropospheric dynamics to frost damage in cellsT–P phase diagram of ices and ice χ belongs to the family of high-density ices (1.0 g cm−3 < ρ < 1.4 g cm−3). In the newly obtained phase diagram of ice with the TIP4P/2005 water model, ice χ is located in the high-pressure region between ice II and ice VI at low temperatures, and to the left of ice V at relatively high temperatures. The electric-field-induced crystallization of liquid water may serve as an alternative approach to attain new phase structures of water, particularly the ferroelectric ices.In this study, we report the formation of a previously unreported ice structure, termed ice χ, which can be observed to form spontaneously in the MD simulation of liquid water at room temperature and under an electric field below the threshold strength. The field-direction-dependent result indicates that ice χ is a rare ferroelectric phase. DFT calculations also indicate that the ice χ is dynamically stable even in zero field. Most importantly, our free-energy computation shows that ice χ is not merely a new crystalline structure but a “missing ice” phase in the contemporary E) used in our classical MD simulations (see below) are in the range of 0–3.5 V nm−1. In a previous study, Saitta et al.45 showed from ab initio MD simulation that the threshold strength of electric field that can lead to dissociation of water molecule is 3.5 V nm−1. It is noteworthy that Saitta et al.45 used the Perdew–Burke–Ernzerhof (PBE) functional and the Berry theory approach to the description of an external electric field (implemented in Quantum Expresso package). Here, instead of the PBE functional, we employed the state-of-the-art dispersion-corrected vdW-DF2 exchange-correlation functional  for the ab initio MD simulation , while considering numerous pressures (P) in the range of 0.001–10 kbar (NPT ensemble). At P = 5 kbar and E = 2.3 V nm−1, a previously unreported ice phase emerges spontaneously, as shown in Fig. z-axis 47. The well-separated peaks and valleys suggest that ice χ has long-range crystalline order. Figure −1) when the phase transformation occurs from liquid water to the crystalline ice χ, reflecting a strong first-order transition from liquid to ice χ.We performed numerous MD simulations, each with the same initial configuration of 560 TIP4P/2005 water molecules in the liquid state with the temperature controlled at 270 K  difference is approximately −14.0 kJ mol−1, in which the polarization energy accounts for ~ 73%. Therefore, the high polarization energy from the strong electric field can make the potential energy difference of the system greater than the value of T × ∆S, where S is the entropy. As a result, a phase transition occurs from liquid water to ice χ. Under the electric field, the dipole orientations of water molecules tend to be along the direction of electric field, while the water molecules can still adapt to form the hydrogen-bonding network. Their interplay could lead to a different and yet a more stable solid state.As water molecules have a permanent dipole moment, in an external electric field, water molecules tend to orient along the dipole while maximizing the number of hydrogen bonds with neighbouring water molecules. Hence, in ice χ, the parallel arrangement of water dipoles is the most energetically favourable under high external electric field with the additional driving force of potential energy. The polarization energy is given by ∆z-axis and then reversed against the z-axis, a strong hysteresis loop is observed at pressure of 5 kbar and temperature of 200 K  phase diagram of the TIP4P/2005 water model at 270 K is plotted. At relatively low electric field (E < 1.0 V nm−1), the water remains in the liquid state. When the electric field is high (E > 2.5 V nm−1), a variety of ice polymorphs are observed, depending on the pressure of the system. The P–E phase diagram can be divided into three regions. At P < 3.5 kbar, a polar ice arises, whose topological structure is the same as the previously reported (non-polar) ice B13  ice is the more stable solid phase  with a population ratio of 1:2:2:2. All molecular dipoles are aligned along the z-axis, resulting in polar ice χ. As Supplementary Fig. a = 6.63 Å, b = 12.36 Å, c = 13.68 Å, α = 28.90°, β = 64.36°, and γ = 86.74°. The phonon dispersion and density of states are computed by using the density functional perturbation theory (DFPT) method49, confirming dynamic stability of the ice χ  is given in Supplementary Table −3) is very close to the experimental value (0.93 g cm−3)22 and the calculated O–O distance (2.755 Å) between neighbouring water molecules is slightly longer than the experimental value (2.735 Å)22. The calculated lattice cohesive energy (Elatt) of ice XI differs from the experimental value52 by only 1.67 kJ mol−1. For ice II and ice VI, their mass densities of 1.178 and 1.313 g cm−3, and their average O–O distances of 2.785 and 2.815 Å, obtained from our DFT calculations, are also very close to the corresponding experimental values of 1.180 and 1.310 g cm−3, and 2.770 and 2.800 Å, respectively50,51. Overall, the vdW-DF2/DFT functional reasonably describes the intermolecular hydrogen-bonding interactions of ices, as shown in our previous work4. For ice χ, the mass density of 1.272 g cm−3 is between that of ice II (1.178 g cm−3) and ice VI (1.313 g cm−3). The average O–O distance of ice χ is 2.785 Å, which is comparable to that of ice II (2.785 Å) and ice VI (2.815 Å). As shown in Table −3 and 1.072 g cm−3) than ice II but also lower cohesive energies.To investigate stability competition of ice phases with different mass densities, we took the high-density ice VIP < 1.32 kbar, low-density ice XI is the most stable phase with the lowest enthalpy. Next, the higher-density ice II becomes more favourable at P > 1.32 kbar. The transition pressure is close to the 2 kbar value that was previously obtained by Conde et al.53 based on the TIP4P/2005 model. At P > 6.66 kbar, ferroelectric ice χ replaces ice II as the most stable ice polymorph. For P ≥ 20.28 kbar, ice VI becomes more stable than ice χ. As shown in Fig. P = 9.79 kbar, consistent with the transition pressure of 10 kbar obtained by Conde et al.53. Although neither is the most stable phase, as indicated by the enthalpy curves, ice B and polar ice B have nearly the same stability. Again, our DFT calculations demonstrate that ice χ is one of the most stable high-density ices in the high-pressure region at zero temperature, along with the known phases of ice II and ice VI. In addition, we calculated enthalpies of these ice structures at different pressures and 0 K, using the strongly constrained and appropriately normed54 functional  under different pressures at 0 K without including the zero-point energy correction. Figure T–P phase diagram of water/ice4. Aragones et al.56 also showed that the TIP4P/2005 water model can describe the relative energy, critical temperature, and surface tension of liquid water and ice phases well. The T–P phase diagram of water/ice is plotted in Fig. Ih (or hydrogen-disordered ice XI), ice II, ferroelectric ice χ, and ice VI, arise in sequence with increasing pressure at low temperature. Extrapolation of the phase boundaries to 0 K gives the corresponding transition pressures of 1.92, 5.14, and 29.73 kbar, respectively, compared with 1.32, 6.66, and 20.28 kbar predicted from the above DFT computations. The predicted transition pressure of 1.92 kbar at 0 K for ice Ih to ice II is in excellent agreement with that of the 2 kbar value previously obtained by Conde et al.53. Different from the previous T–P phase diagram, the coexistent line between ice II and ice VI disappears, whereas ferroelectric ice χ occupies a region between ice II and ice VI at low temperature and part of the region of ice V at relatively high temperature. As a result, two new triple points emerge: one for ices II, V, and χ at 6.06 kbar and 131.23 K, and the other for ices χ, V, and VI at 9.45 kbar and 144.24 K. Ice B and polar ice B do not appear in the T–P diagram, as they have higher Gibbs free energies. It is noteworthy that the free-energy calculations show that ice χ has the lowest free energy among ice χ, ice XIII, and ice XV in the low-temperature and high-pressure region, indicating that ice χ is more stable than ice XIII and ice XV. Overall, both MD simulations at finite temperature and DFT calculations at 0 K strongly support the existence of ferroelectric ice χ in the T–P phase diagram at high pressures.Lastly, to examine the stability of ice χ at temperatures much higher than 0 K, we performed free-energy calculations using the Einstein molecule approach method. In our previous study, we confirmed that the TIP4P/2005 water model can reasonably simulate the realistic −3. The ferroelectric ice χ is also proven to be dynamically stable on the basis of phonon-spectrum DFT computation. In the T–P phase diagram of water/ice, ferroelectric ice χ emerges in the high-pressure region, located between ice II and ice VI at low temperatures and occupying some domains of ice V at relatively high temperatures, leading to two triple points at P = 6.06 kbar and T = 131.23 K, and at P = 9.45 kbar and T = 144.24 K, respectively. The appearance of ice χ in the T–P phase diagram of water/ice suggests that the ferroelectric ice χ entails high thermodynamic stability. Identification of this ferroelectric ice phase not only reveals a “missing” ice polymorph in the high-pressure region of the state-of-the-art T–P phase diagram of water but also provides more precise temperature/pressure conditions for seeking the elusive ferroelectric ice. In light of the requirement of ultrahigh electric field, whether this predicted ice We predict a new ferroelectric ice χ in the phase diagram of water. Ferroelectric ice χ has a high mass density of 1.27 g cmNPT) ensemble, with an external electric field applied along the z-axis. The MD simulations are undertaken with the GROMACS  package57. For all MD simulations, the initial supercell is a cubic box containing 560 TIP4P/2005 water molecules48 and the temperature is controlled at 270 K 46. To map out a semi-quantitative P–E phase diagram, we set a series of pressure and electric field values, including P = 0.001, 0.01, 0.05, 0.1, 1.0, 2.0, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 7.0, 8.0, 9.0, 9.5, and 10.0 kbar, whereas the electric field strength is varied from 0 to 3.5 V nm−1 by an increment of 0.1 V nm−1. The external electric field induced an additional force, Fi = qi·E, where Fi is the force induced by the electric field, qi is the charge of each atom, and E is the applied electric field. For all simulations, the time step is set to 2 fs. The equilibration MD run lasted for at least 10 ns and, in some cases, lasted for 200 ns. T and P are controlled by a Nosé–Hoover thermostat58 and a Parrinello–Rahman barostat59, respectively. A cutoff of 1.0 nm is adopted for the L–J interactions and the long-range electrostatic interactions are treated by the slab-adapted Ewald sum method60.All MD simulations are performed in the isothermal-isobaric . We note that the vdw-DF2 functional has been previously shown by Santra et al.61 to be particularly accurate for computing relative energies and transition pressures for known phases of ices. The electron wavefunction is expanded by a plane-wave basis up to 700 eV. The Brillouin zones are sampled by k-point grids with a uniform spacing of 2−1. To confirm dynamic stability of ice χ, the phonon dispersion is computed by using the DFPT method49 as implemented in the VASP 5.4.The relative energies of ice χ and selected ice phases are computed using the DFT methods implemented in the VASP 5.3.5 software packageT–P phase diagram of water and ice polymorphs  was derived based on the Einstein model for crystals and the TIP4P/2005 water potential. First, to obtain reliable configurations of the ice polymorphs, isothermal-isobaric Monte Carlo simulations at temperatures from 1 to 200 K (with 10 K increments) and pressures from 1 to 24 kbar (with 1 kbar increments) are performed using a homemade code. For each candidate phase, the configurations from the Monte Carlo simulations are used to calculate the free energy on the basis of the Einstein molecule approach with the GROMACS programme57. At each T–P condition, the Ewald sum method with a real-space cutoff of 8.5 Å is adopted to treat the electrostatic interactions and the pair potential is truncated at 8.5 Å. For ice Ih, ice V, and ice VI, the effect of hydrogen disorder is considered in the free-energy computation and their Pauling entropies S/NkB are taken as ln(3/2), 0.3817, and ln(3/2), respectively64. Once the free energy at a reference point is determined, the thermodynamic integration method can be used to evaluate the free energy under other thermodynamic conditions. Specifically, an initial coexistent point is located by equating the chemical potentials of two phases at a given temperature and pressure65. Next, the Gibbs–Duhem integration based on the trapezoid predictor-corrector formulas is performed to compute the phase boundaries66.The Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6


Dioscorea sp.) is a nutritious food; however, it is not used very much in industrial food processes. The objective of this study was to develop and to characterize a truly dairy‐free low‐fat ice cream prepared from unfermented and fermented with yam dough.The demand for industrialized foods that contribute to health and well‐being has characterized the new generation of consumers. Yam (Leuconostoc lactic CCMA 0415 remained viable (107 CFU/g) during 90 days of storage. The fermentation process reduced the starch concentration from 26.82% to 22.35% and the protein concentration from 4.68% to 3.99% and increased the concentration of some minerals . The total phenolic contents for fermented and unfermented ice creams were 51 and 54 mg, respectively. The radical scavenging activity were 18% and 10% with the 1,1‐diphenyl‐2‐picrylhydrazyl method and 44% and 26% with the 2,2’‐azino‐bis (3 ethylbenzo‐thiazoline‐6‐sulfonic acid) method for the unfermented and fermented samples, respectively. The fermented and unfermented ice creams were both characterized as non‐Newtonian fluids exhibiting pseudoplastic behaviors.The fermentation was conducted by These results indicated that yam is a suitable substitute for milk, thus making it an option to produce edible lactose‐free ice cream with low fat. It has been commercialized mainly as an in natura product. Approximately 62 million tons of yam is annually produced, and around 6.7 million tons are lost during postharvest treatment  are highlighted for their metabolic versatility. In addition to produce enzymes, this bacterial group can ferment a variety of substrates and produce organic acids, bacteriocins, and exopolysaccharides. In addition, some strains exhibit probiotic properties  is “generally recognized as safe” (GRAS), and many species are efficient producers of mannitol and class II bacteriocin  was washed, peeled, and sliced for ice cream preparation. The yam slices were preheated at 60°C for 10 min . The Leuc. lactis CCMA0415 strain was grown in a De Man, Rogosa, and Sharpe broth medium  at 35°C for 24 hr (108 CFU/ml). After, the cells were inoculated in a yam‐based medium at 107 CFU/ml. The fermentation was performed at 35°C for 6 hr.For fermentation, the The resulting fermented yam dough, as well as the unfermented yam dough, was used for the ice cream preparation. The fermented and unfermented yam dough was added to 0.6% (w/v) stabilizer , 1.11% (w/v) glucose, and 10% (w/v) sucrose. The mixtures were homogenized and then matured for 24 hr. After this period, 0.6% (w/v) emulsifier (Emustab Selecta) was added, and the doughs were beaten in an ice cream maker for 10 min and then frozen at −20°C  (v/v). The minerals  were determined by atomic absorption spectrophotometry and flame photometry, following the method described by Malavolta, Vitti, and Oliveira . The resThe molar ratio of phytate to calcium, zinc, and iron was calculated to evaluate bioavailability of ice cream mineral  and stored in the dark for 2 hr. The absorbance of the samples was evaluated at 750 ηm. The TPC content was obtained through a standard curve of gallic acid. The results were based on wet weight and expressed as mg of gallic acid/100 g of ice cream sample. The analyses were performed in triplicate.The total polyphenol content (TPC) of the fermented and unfermented ice creams was evaluated spectrophotometrically according to the Folin–Ciocalteu method  of the fermented and unfermented ice creams was evaluated using the radicals 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) and 2,2′‐azino‐bis (3 ethylbenzo‐thiazoline‐6‐sulfonic acid) (ABTS) , followed by incubation in the dark for 2 hr. Absorbance was achieved at 515 ηm. The control was evaluated using 0.1 ml methanol as a replacement for the ice cream.Ab is the control absorbance and As is the sample absorbance.The RSA was calculated for all of the samples according to Equation 2.8®) was used to label live bacterial cells (ChS1 detector), and a propidium iodide (PI) solution (Sigma®) was used to mark the dead bacteria  was applied, and the mixture was incubated in the dark for 40 min. Next, 5 µl PI (1 µg/ml) was incubated for 5 min on dark. The images were edited with FIJI ImageJ and Corel Draw software.The fluorescence analyses were made with the Zeiss Axio‐Observer Z1 LSM 780 laser confocal microscope and Zen 2010 software (Carl Zeiss Microscopy MBH) at the Electron Microscopy Laboratory at the Federal University of Lavras. Syto9 fluorochrome  equipped with a HAAKE A10 (Thermo Scientific) thermostatic water bath and a universal temperature control system HAAKE UTM controller (Thermo Scientific). A set of concentric cylindrical geometry sensors (CCB25 DIN cup and CC25 DIN Ti rotor) with a gap of 5.3 mm was used.−1 for 2 min for the upward curve and 2 min for the downward curve to break the thixotropy and eliminate the influence of time on the flow behavior of the treatments. After this procedure, the flow curve was generated for the rheological characterization of each sample by the application of a flow curve varying the deformation rate from 0 to 300 s−1 for 3 min. Newton's law , K is the consistency index (Pa.sn), n is the flow behavior index (dimensionless), and σo is the initial tension (Pa).2.10p < 0.05) using the Statistical Analysis System (SAS) software . The rheological parameter models were fitted to the experimental data of the flow curves using the SAS software, and the graphs were plotted using SigmaPlot 11.0 software .The results were evaluated with analysis of variance (ANOVA) and Tukey's range test . The reason might have been the protease and amylase activity of the Leuc. lactis CCMA0415 starter culture. This strain was previously characterized as an amylase producer (data not shown). It is likely that peptides were used as a source of nitrogen by the microorganism. Amylase hydrolyzes the starch into smaller polymers, thereby being essential enzyme for microorganisms which uses starch as a carbon and energy source. The Leuconostoc genera have been described as protease producers  in the fermented ice cream than in the unfermented product . The increased concentrations of minerals in the cereals, roots, and tubers fermented by the LAB were probably the result of fermentation process  than the dead cells (0.031) because of the high viable bacterial population present in the ice cream. The The observed cell death could likely have resulted from the freezing process, which can damage the cell membrane of the bacteria and lead to injuries that compromise cell function and metabolic activity  as did the titratable acidity (~ 0.2%) and the pH (~ 4.0)  was observed between the ice creams. However, the antioxidant activity with the DPPH and ABTS methods was significantly different (p < 0.05), with lower values being observed for the fermented samples than for the unfermented samples. These results could indicate the possible interaction of plant phenolics with microbial proteins that can form insoluble complexes and reduce ice cream antioxidant activity . The melting rate, approximately 0.5 g/min, was not significantly different for the fermented and unfermented samples. The low melting rate obtained was probably the result of the low concentrations of fats used for the elaboration of the ice creams because the net of fat globules formed during freezing influences the melting rate . Chang and Hartel  stated tr2 = 0.99) and low mean squared error values; however, no significant difference was observed between the fermented and unfermented ice creams.The rheological behavior of the fermented and unfermented yam‐based ice creams was evaluated .The fermented and unfermented ice creams were characterized as non‐Newtonian fluids, and they exhibited pseudoplastic behavior (n values of 0.43–0.66 (Aime, Arntfield, Malcolmson, & Ryland, In general, the ice creams with low‐fat content had The viscosity of the ice cream samples decreased with the rate of deformation (see Figure 4Leuc. lactis CCMA 0415 was successfully used as a starter culture for the yam fermentation. The fermented and unfermented ice creams were characterized as non‐Newtonian with pseudoplastic behavior. Therefore, yam was characterized as a suitable raw material substitute for milk during the elaboration of edible ice cream.Ice creams containing low fat concentrations were successfully elaborated from fermented and unfermented yam dough. According to the obtained data, the fermentation of yam seems to improve mineral availability and decrease starch and protein content of ice cream, and this could lead to higher digestibility. The authors declare no conflict of interest.This study does not involve any human or animal testing.


September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2017). Evidence is presented that three breakpoints (shifts in the mean) occurred in the Pacific sector, with higher amounts of open water starting in 1989, 2002, and 2007. Breakpoints in the Atlantic sector record of open water are evident in 1971 in longer records, and around 2000 and 2011. Multiple breakpoints are also evident in the Canadian and Russian halves. Statistical models that use detected breakpoints of the Pacific and Atlantic sectors, as well as models with breakpoints in the Canadian and Russian halves and the Arctic as a whole, outperform linear trend models in fitting the data. From a physical standpoint, the results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely. From an analysis standpoint, the findings imply that de-meaning the data using the breakpoint means is less likely to cause spurious signals than employing a linear detrend. The confluence of opportunity and risk at the retreating ice edge2 raises critical questions as to how well we observe and simulate Arctic ice area and extent. Conversely, how well do we describe the area covered by ocean within which ice, if present at all, is present in very low concentrations? In this context, the reconstruction of historical ice area records has been an important activity4. However, much of the focus has been upon hemispheric ice extent and concentration, whereas more critical to Arctic industrialization is the area and timing of seasonally navigable open water due to sea ice reductions. Industries such as freight, tourism, mining and commercial fishing may benefit from lower ice area, thickness, and concentration5. These activities have a critical reliance on shipping in the Arctic, which requires reduced or clear ice area over Arctic ocean sectors for transit navigability. However, because of the present low predictability of the navigable season, economic benefits have not accrued at the expected rate7. Low predictability has also led to ongoing safety and reliability concerns8. Indeed, even though summer sea ice is projected to have substantially retreated by as early as 2035, operators will face continued hazards from drift ice, icebergs and perhaps increased storminess10. While seasonally navigable open water is of course dependent on ship type, crew experience, and weather conditions, a reliable record of open water over recent decades provides a first-order understanding of how sea ice will affect Arctic economic development over coming decades.In the most recent decade, summer minimum sea ice extent has retreated to levels not seen since the beginning of the satellite record13. There is a substantial literature that documents structural breaks in the global climate system, including attribution to mechanisms such as phase locking15. Further, steep transitions in the ice cover have been observed in smaller regions using a variety of data sets17. One hypothesis that has emerged is the idea of a low frequency propagating signal from the eastern to the western Arctic18. Despite this literature, to elucidate changes in ice variability, sea ice data continues to be de-trended linearly19. We find that de-trending using a change in means from identified breakpoints better describes the data by a number of measures.Here, we examine the September sea ice area record across the Arctic Ocean and its peripheral seas for structural breaks. September is typically the average period that is used by climate researchers to document the advance of open water area over timeWhile we examine the entire Arctic for breakpoints, a major interest in Arctic open water is the ability to transit from one side to another. Therefore, we look at two different set of halves separately, one set bounded by ocean, the other by land: the Atlantic and Pacific halves, which cover how sea ice might exit the Arctic Ocean and may be of more interest to ice scientists, and the Canadian and Russian halves, which consider possible transit routes from the lower Atlantic to the lower Pacific or vice versa, and so may be of more interest to shipping.21, which performs best with high-concentration multi-year wintertime ice, and the Bootstrap algorithm (data set ID: NSIDC-0079)22, which is considered more reliable in cases of surface melt or when concentrations are lower than about 40%24. Generally, no algorithm detects ice well when at very low concentrations (below 15%) and the low spatial resolution of the sensors limit the ability to distinguish an ice “edge”, however defined25. These records have been combined, accounting for the shortcomings in each, to produce an additional “merged” data product covering satellite era. Finally, the Climate Data Record (data set ID: G02202) uses the merged algorithm and ensures a temporally consistent analysis covering 1988–2017, although it ends in February 201726.An essential first step is to interrogate the reliability of the data. During the satellite era, the data of record has been obtained from the Nimbus Scanning Multichannel Microwave Radiometer (SMMR: 1979–1987), the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I: 1987–2007) and the Special Sensor Microwave Imager/Sounder (SSMIS: 2008-present). Radiances are used to derive 25-km gridded sea ice concentration data products using either the NASA Team algorithm (data set ID: NSIDC-0051)24. For the first half of the 20th century this dataset relies largely on the Arctic and Southern Ocean Sea Ice Concentrations data set27 from the National Snow and Ice Data Center (NSIDC), which in turn makes use of sea ice charts from the Danish Meteorological Institute29. These charts are based on direct observations from the shoreline and ships30, and hence tend to overestimate ice extent in regions inaccessible by sea. Furthermore, these charts lack primary data for the time period 1940–1952, although this has been corrected as part of a substantive recent update13 that does not affect the results shown here. For the time period from 1960, sources expanded to include aerial surveys, observational re-analyses, operational ice charts, and from 1972, OSI-SAF (Ocean and Sea Ice Satellite Applications Facility) passive microwave retrievals, which is produced using SMMR, SSM/I, SSMIS, and (only in Version 1 of the data set) EMSR from 1972–1979. We show this data from 1953 for context, but our analysis focuses on the period from 1960 onwards, and particularly the satellite era. We also examined operational ice charts from the National Ice Center  which are produced on a weekly to biweekly basis using all available forms of remotely sensed sea ice data, including the passive microwave data, visible and near-infrared products such as MODIS, and synthetic aperture radar such as RADARSAT31. Here, we use the ice extent from the 1972–2007 archived product32 for the last chart produced in September of each year. In processing the ice chart data for this study, any grid cell with ice concentration less than 15% was set to open water to be consistent with the satellite retrievals.None of these products can be considered to be independent, as they all derived from the same source measurement. As a result, we augment this record with the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST2.2)9 as well as the largest downward interannual trend19. The records show some substantial differences across the records . September is at the end of the melt season and so tends to have the lowest ice area36 and others have detrended the ice record linearly in order to understand ice retreat and interannual variability19, the data for the Pacific sector suggests there may be a shift in the mean  in the data; if so, a linear trend is not the correct functional form. To investigate this, we apply a breakpoint analysis designed to allow early determination of breakpoints (see Methods) to the sea ice record. We also examine open water, which is determined as the area average of one minus the fraction of ice cover in each grid cell within the ice pack . We apply this analysis to the NASA Team (NSIDC) record in Fig. 2 = 0.09) in the Atlantic sector but a large and significant linear trend  in the Pacific sector (numbers quoted for the NASA Team (NSIDC) record).We start by examining trends in open water percentage using data from 1979 to 2017. It is apparent that there is a weak linear trend . The Pacific sector had three major breakpoints: 1988/89, 2001/02, and 2006/07 (Table 2 for the breakpoint model (0.74) is notably larger than that for the linear model (0.46). All permutations for Canadian half data do better than a linear model for the HAD-Full, HAD-Truncated, and NSIDC-NASA Team datasets. For the Russian half, the HAD-Full results suggest breakpoints at 1970/71, 1989/90, and 2004/05. The all six permutations for the shorter results for NSIDC-NASA Team suggest breakpoints at 1989/1990 and 2004/2005, and all six permutations of the HAD-Truncated strongly suggest a breakpoint at 2004/05. In all cases for all six permutations, the HAD-Full, HAD-Truncated, and NSIDC-NASA Team results for the Russian half suggest a lower MSE, a lower BIC, and a higher adjusted R2 than the linear trend.All six permutations of the breakpoint algorithm on the Canadian half found breakpoints in the HAD-Full record at 1997/98 and 2006/07  – were insignificant in combination, and equally valid when tested separately. The adjusted RThe HadISST record is not temporally uniform, and has a lower spatial resolution than the satellite derived records. In the Pacific sector, the HadISST time series reflects the NSIDC-NASA Team results.39. That said, it is known that there were some errors in the data stream in the first months of the SSM/I instrument40. While this may have some impact on the structural shift detectable in 1988, given that other breakpoints were detected there are likely to be other, physical, factors that are important.These findings suggest caution must be employed when seeking to understand trends in Arctic ice cover over the satellite era and to reproduce these trends in operational and climate models. Further, it calls into question the practice of characterizing the record using linear trends. A model of abrupt shifts in September open water fraction is likely to have a number of drivers that interact in complex ways. First, it is possible that non-physical artifacts may exist in the satellite record. Several authors have noted that small artifacts remain in the satellite-derived records, associated with sensor and orbit changeovers, but all suggest that these differences are below the sensitivity of the instrument42. Shifts in the magnitude of the decreasing trend in Arctic sea ice extent around 2005–2007 were also detected33. Unlike the method employed here, which is designed for lower data frequency as in the satellite record, others have employed algorithms on individual gridpoints assuming a linear trend on LOESS smoothed data, which requires large, densely sampled data and reduces the effect of outliers and thus may understate or miss transition points, and only consider one breakpoint per location43.Structural changes in the ice cover record have been suggested previously. In the eastern Bering Sea, a late 70′s regime shift in the long term sea ice cover record was associated with a 50 to 70 year oscillation in the North Pacific44. In future climate scenarios, abrupt changes in September ice extent and thickness that are characterized by thermodynamically driven doubling of ice loss for periods of around five years, in seven of sixteen models participating in the A1B scenario of the IPCC-AR4, have been detected. Ice age is a useful, though not perfect, proxy for ice thickness. Figure 45 for week 37 , where anomaly is defined relative to a base period of 1984 through 2016. Note that ice age anomalies increase rapidly in the first 6 years of the record–this is likely an artifact of the data product’s initialization procedure. After 1990, multiyear ice with concomitant positive anomalies in both the Pacific and the Atlantic dwindles, particularly after the successive record low summers of 2007 and 2012, with low anomalies in 2008 (Atlantic) and 2010 (Pacific) and falling dramatically lower in 2012 and 2014 in both the Pacific and the Atlantic. Very little multiyear ice remains; in fact, since the algorithm records only the oldest ice in the grid cell, the amount of old ice in recent data may be overestimated. Nevertheless, there is a strong negative relationship between the ice age record and the open water area  than the linear trend model . In the Atlantic sector, the linear trend model  does worse than a model with a breakpoint at 2007/08 , perhaps because the Atlantic provides an escape route for Arctic ice.The hypothesis that the ice volume reaches a critical threshold allowing subsequent rapid melting is one that has been identified in model studiesBecause of the initialization of the ice age record, it is not possible to ascribe a structural shift unambiguously in 1990. However, it appears that an abrupt increase in open water area can be associated with a concomitant abrupt decrease in ice age across the Arctic. Further, it is possible that 2007 and 2012 represents something of a critical point for the sea ice, particularly in the Pacific.46. That said, there is much insight that can be derived from a careful analysis of all of the available data. This assessment of satellite-derived, operational, and climatological time series suggests that Arctic sea ice behavior is highly non-linear, and critical points in ice cover that have been postulated as arising from strong ice-albedo feedback processes may have already occurred. We have demonstrated that, certainly in the Pacific sector and perhaps for the Atlantic sector, as well as for the Canadian and Russian halves, sea ice area is better characterized by a series of structural shifts, in 2007 and in near the turn of the century in 1998 and/or 2002, rather than a linear trend. This model is robust to the algorithm used to derive ice concentration from satellite passive microwave brightness temperatures, although these algorithms demonstrate significant differences in magnitude. The physical nature of these structural shifts is supported by analysis of quasi-independent records, such as the operational ice charts. The concomitant shifts in the ice age product suggests a potential thermodynamic feedback mechanism. That said, the processes associated with these shifts can only be tested independently using a modeling approach, which will be the subject of future work.A long-term, high quality and temporally consistent record of Arctic open water remains an elusive goaloW, 100oE, 66.6oN, and 84.5oN. For the Atlantic sector boundaries are 100oE, 100oW, 66.56oN and 84.5oN. The Russian sector is bounded by 170oW, 0oE, 66.56oN, and 84.5oN. For the Canadian sector boundaries are 0oE, 170oW, 66.56oN and 84.5oN. Within each sector and for each day, the number of grid cells for which the ice concentration is less than 15% is tallied and the area covered by those cells is computed. A monthly average is then computed from these daily areas. HadISST data and National Ice Center sea ice chart data were processed in the same way, although the National Ice Center charts were not available as daily data.For each satellite data record and each year in the record, a mean September sea ice area within a Pacific and an Atlantic sector is determined. The Pacific sector is bounded by 10047 that can find multiple breakpoints and which allows for the choice of different lengths (L) of the number of years and significance levels (p) in determining breakpoints. We vary our number of year (length) inputs from 5 to 10 as inputs into the breakpoint methodology, resulting in six permutations of breakpoints. For the results reported in the paper, we used p = 0.05; for robustness, we also ran the tests using breakpoints based on p = 0.10 and got substantively the same results, and so did not report them here. We then apply this methodology and these six permutations to the Atlantic and Pacific sectors, the Canadian and Russian sectors, and the entire Arctic.We use a sequential algorithmYt is the value of the sea ice concentration or ice age for year t, and t is the time in years (creating a linear trend). For the breakpoint analysis, a model was created using the breakpoints. The generalized form of the model ist = 0 and t = N are the first and last years in the dataset, ti and ti+1 are the break points returned by the sequential algorithm, ti and, and ti+1 , and ti and ti+1 and 0 otherwise.For the statistical tests to compare the accuracy of the breakpoints versus a linear trend, the following models were compared. The linear trend model takes the form:d2, d3, and d4 are defined as in regression (3) above.This form can be translated into a regression, either with or without an intercept. For example, for a model of the Pacific with three breakpoints, such as p = 0.5 and L = 7, where the breakpoints were 1989, 2002, and 2007, a three breakpoint model without an intercept took the form:2 is only well-defined for regression (4). The goodness-of-fit of each of the six breakpoint models was compared with the linear model using MSE, BIC, and adjusted R2. Breakpoints that were found in multiple permutations were considered more robust to parameter estimates. Table 2 breakpoints) of the six examined for each area and dataset; the full set is found in Supplement Table The mean squared error (MSE) and Bayesian information criterion (BIC) were calculated for the linear trend model and each of the six models created by the six permutations of the Rodionov (2004) breakpoint algorithm. Note that both the regression without an intercept (3) and with an intercept (4) will have the same MSE and BIC, but the adjusted RSupplementary Information


Roasting is known to change the allergenic properties of peanuts. To study these observations at a molecular level, the relationship of IgE binding to the structure of Ara h 3 from raw and roasted peanuts was assessed. Ara h 3 (A3) was purified from raw (R), light roast (LR) and dark roast (DR) peanuts, the purity was assessed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and the secondary structures were compared with circular dichroism (CD) spectroscopy. In order to understand the contribution of structure to IgE binding, the R A3 was partially denatured (PD) by heat treatment (65 °C for 2 h), subjected to CD spectroscopy and IgE spot blot analysis with sera from peanut- allergic individuals. While we observed that the secondary structure of purified A3 from R and LR peanut in solution was affected by the reduction of disulfide bonds and heat treatment when purified from the peanut following the roasting process, only small alterations were seen in the secondary structure. The purified LR A3 bound higher levels of IgE than the RA3. CD spectroscopy of PD A3 revealed a reduction in the percentage of alpha helices, and serum IgE binding. Therefore, while A3 purified from roasted peanuts did not show significant changes in secondary structure, it showed higher IgE binding than R A3. Therefore, the higher IgE binding to LR A3 was more likely to be due to chemical modifications than structural changes. However, a decrease in the IgE binding was seen if R A3 was deliberately unfolded, indicating that the structure played an important role in IgE binding to A3. Over 15 million Americans have a food allergy and there is evidence for an 18% increase in prevalence . More thUnderstanding allergens at a molecular level is important for deciphering the immune system-allergen recognition and response, and the cause(s) of allergenicity of foods. The effects of processing on the allergenic potential of food has become increasingly important due to recent studies that have shown that individuals can become more safely desensitized to a food by consuming that food in one processed form versus another ,6,7,8,9.Sera were obtained from the blood of peanut-allergic individuals. The blood was collected after informed consent at Tulane Health Science Center , in accordance with the rules and regulations of the institutional review board. Individual sera from previously well-characterized peanut-allergic patients were used in this study . BrieflyFlorunner peanuts were used either raw, light roasted, or dark roasted as previously described . Raw, sh−1 for Ara h 3 was used for calculating ellipticities. Secondary structural modes were estimated from ellipticities by multiple protein secondary structure prediction and calculation programs such as K2D2 and CDPro.Far UV (185–250 nm) circular dichroism (CD) spectra of Ara h 3 purified from raw (R), light roast (LR) and dark roasted (DR) peanuts were obtained. Partially denatured Ara h 3 was obtained by adding either 10 mM dithiothreitol (DTT) for a few minutes or by incubating for 2 h at 65 °C. Denatured Ara h 3 was obtained by heating the sample for 10 min at 95 °C (data not shown) or incubating with 10 mM DTT overnight. Samples were desalted using disposable gel filtration columns  into Milli-Q water and immediately used in CD measurements or spot blot analysis. A CD spectrum of Milli-Q water was obtained for background purposes. Protein concentrations were 0.1 mg/mL and spectra were obtained at room temperature with a JASCO Model J-815 spectropolarimeter. The mean residue weight of 115.1 g moleRaw and roasted samples or purified proteins were subjected to SDS-PAGE on 4–20% Novex Tris-HCl pre-cast gels  where individual proteins were separated according to size and stained with Gel-Code Blue  for 1 h, and digitally recorded with a CCD camera system Fuji-LAS 4000 .®6  according to manufacturer’s instructions, after any denaturing treatment were spotted onto a nitrocellulose membrane, 0.20 µm pore size . The membrane was blocked with 2% (w/v) non-fat milk in PBS plus 0.5% Tween-20 (PBST) buffer at room temperature for 30 min. The blocked membranes were then incubated overnight at 4 °C with specific human sera from peanut-allergic patients at a 1:10 dilution in PBST. The membranes were washed in PBS and incubated for 30 min at room temperature with a 1:10,000 dilution of rabbit anti-human IgE with 2% non-fat milk in PBST. The membranes were washed with PBS and the signal detected using ECL-plus  according to manufacturer’s instructions.Different amounts of protein from the pure R, LR, PD and D Ara h 3 samples were desalted into water with a Bio-spinThe belief in the field of protein biochemistry is that heating leads to the loss of structure. Therefore, one of the major peanut allergens, Ara h 3 was purified from raw and roasted peanuts and spotted onto membranes and probed with human IgE from peanut-allergic individuals. We previously made the observation that IgE binding for every serum tested was higher for the roasted than the raw. We theorized that Ara h 3, purified from roasted peanuts, was potentially denatured due to the high temperatures of roasting and this led to exposure of IgE epitopes that were inaccessible in the folded protein of the raw peanut. Therefore, Ara h 3 was purified from raw (R), peanuts and two levels of roasted peanut, light roast (LR) and dark roast (DR) were subjected to SDS-PAGE, and the secondary structure was compared with circular dichroism (CD) spectroscopy A. In thiThe effects of roasting on IgE binding and allergenic potency of peanut allergens has been studied for over 20 years. A simulated roasting model of heating crude peanut extract (CPE), and the allergens, Ara h 1 and Ara h 2, purified from raw peanuts, in the presence of reducing sugars at 55 °C for 10 days in solution, enhanced IgE binding . With drWhile model systems can be highly effective in understanding molecular events, it is important to understand the effects of food processing on allergenicity and on the individual allergens within the context of a food in order to develop useful tools for research, diagnosis, detection and preventative or therapeutic treatments. In this study, we demonstrated that the methodology in assessing an allergen’s structural and immunological properties could influence the outcome of a study. We were able to partially denature Ara h 3 at 65 °C and fully denature Ara h 3 at 95 °C (data not shown). Additionally, as expected, disruption of disulfide bonds allowed partial and full denaturation of this protein. However, when Ara h 3 was subjected to the commercial roasting process of 160 °C temperature for 40–60 min, within the context of peanuts, it maintained its structural elements and indeed bound higher levels of IgE then the Ara h 3 purified from raw peanuts. Based on this study, we conclude that increased IgE binding to roasted Ara h 3 is more likely due to roasting-induced chemical modifications of the protein rather than structural changes. Furthermore, given that unfolding the Ara h 3 purified from raw peanuts resulted in significant reductions in IgE binding, we believe that a significant portion of IgE binding to this protein is due to the existence of conformational epitopes.


The half-maximal inhibitory concentration (IC50) values for maltase were as follows: B2-3’-O-gallate (1.73 ± 1.37 μM), ECG (3.64 ± 2.99 μM), and EC (6.25 ± 1.84 μM). Inhibition kinetic assays revealed the inhibition constants (Ki) of the mixed-competitive inhibitors of maltase, as follows: B2-3’-O-gallate (1.99 ± 0.02 μM), ECG (3.14 ± 0.04 μM), and EC (7.02 ± 0.26 μM). These compounds also showed a strong inhibitory activity toward sucrase, and the IC50 values of B2-3’-O-gallate, ECG, and EC were 6.91 ± 3.41, 18.27 ± 3.99, and 18.91 ± 3.66 μM, respectively. Inhibition kinetic assays revealed the inhibition constants (Ki) of the mixed-competitive inhibitors of sucrase as follows: B2-3’-O-gallate (6.05 ± 0.04 μM), ECG (8.58 ± 0.08 μM), and EC (13.72 ± 0.15 μM). Overall, these results suggest that B2-3’-O-gallate, ECG, and EC are potent maltase and sucrase inhibitors.The inhibitory properties of epicatechin-(4 Over the past three decades, the number of people with diabetes mellitus has quadrupled, with the result being that diabetes mellitus is now the ninth leading cause of death worldwide. Approximately 1 in 11 adults have diabetes mellitus, and roughly 90% of those cases are type 2. The most pronounced symptom of diabetes mellitus is an abnormal postprandial increase in blood glucose levels . It is wα-Glucosidases are divided into four hydrolase types, i.e., maltase (Enzyme Commission (EC) 3.2.0.20), sucrase EC 3.2.1.48), glucoamylase (EC 3.2.1.3), and isomaltase EC 3.2.1.10). These four enzymes form two complexes with different substrate specificities, maltase–glucoamylase and sucrase–isomaltase complexes . Maltase. These f8, glucoaRhodiola crenulata (R. crenulata) were shown to significantly reduce the inhibitory activity of α-glucosidase. The genus Rhodiola L. (Crassulaceae) comprises approximately 90 species found throughout the world, and more than 70 species are found in the western and northern regions of Asia. Among these, R. crenulata, is an important member of the Crassulaceae family. For centuries, the roots of R. crenulata have been used as a health supplement. It is possible that these extracts could be used as a supplement for postprandial hyperglycemia associated with diabetes mellitus. In our previously study, we isolated and characterized α-glucosidase inhibitory constituents, including epicatechin--epicatechingallate , epicatechin gallate (ECG), and epicatechin (EC) from R. crenulata [O-gallate, ECG, and EC against α-glucosidase activity.In a study by Kwon et al. , water erenulata . These rO-gallate, ECG, and EC has previously been reported, the inhibitory properties of B2-3’-O-gallate, ECG, and EC toward maltase and sucrase have not been examined. Thus, this paper describes the inhibition kinetics of B2-3’-O-gallate, ECG, and EC toward maltase and sucrase. Our primary objective in this study was to elucidate the inhibitory effects of 2-3’-O-gallate, ECG, and EC on maltase and sucrase.The above potent maltase and sucrase inhibitors were considered for use in treating diabetes mellitus. Although the inhibition of α-glucosidases by B2-3’-R. crenulata were obtained from a traditional Chinese medicine pharmacy in Chiayi in south Taiwan. The authenticity was confirmed by Dr. Hsiang-Wen Tseng  using DNA sequencing technology and an internal transcribed spacer sequence database. Isolates of B2-3’-O-gallate, ECG, and EC were prepared from a crude extract of R. crenulata in accordance with the methods reported by Chu et al. [R. crenulata roots was milled and extracted using distilled water (900 mL). After centrifugation at 12,000 × g for 15 min, the supernatant was collected and freeze-dried to yield 16 g of water extract. The extract that displayed IC50 value for maltase was 5.23 ± 0.18 μg/mL, whereas the extract that displayed IC50 value for sucrase was 21.42 ± 0.45 μg/mL. The water extract underwent column chromatography on a vacuum manifold using solid-phase extraction (SPE) cartridges. Each sample (50 mg) was chromatographed on a SPE cartridge. The samples were then eluted stepwise using 0%, 10%, 20%, 30%, and 40% methanol in distilled water. A total of 5 fractions, one for each methanol elution (20 mL), were collected. The samples were concentrated using a rotary evaporator and then freeze-dried. The total yield of the fractions was 92.4%, whereas the yields of the individual fractions were as follows: 0% elution (67.6%), 10% elution (16.5%), 20% elution (5.1%), 30% (2.3%), and 40% (0.9%). The 30% fraction was redissolved in distilled water and subjected to column chromatography on a HPLC high-performance liquid chromatography (HPLC) system comprising a pump  and a detector  with a C18 packed column . A gradient elution from H2O/acetonitrile (86:14) to H2O/acetonitrile (72:28) was used to isolate B2-3’-O-gallate, ECG, and EC. Elution began with a solvent flow rate of 1 mL/min for the collection of B2-3’-O-gallate, ECG, and EC. The total yields were as follows: B2-3’-O-gallate (8.7 mg), ECG (6.1 mg), and EC (7.6 mg).Dried roots of u et al. . BrieflySaccharomyces cerevisiae (S. cerevisiae) were obtained from Sigma Chemical Co. . A reaction mixture was formulated from 30 μL of maltose (86.3 mM) with 20 μL of maltase (0.33 units/mL) and 50 μL of phosphate buffer . The mixture was incubated at 37 °C for 10 min and then at 100 °C for a further 10 min to stop the reaction. The glucose concentrations released by the reaction mixture were determined via the glucose oxidase method  using a VersaMax microplate reader  at an absorbance wavelength of 540 nm.Maltase activity was characterized using the methods outlined by Adisakwattana et al.  with sliO-gallate, ECG, EC, and quercetin, with quercetin  as positive controls. All of the samples were assessed in terms of maltase inhibitory activity under a range of concentrations (0–30 μM). Maltase inhibitory activity was evaluated by dissolving the samples directly in phosphate buffer solution . Note that each sample was assayed in triplicate. Inhibitory activity was estimated in terms of IC50 values based on percent inhibition, as follows:In this study, we focused on B2-3’-Leuconostoc mesenteroides were obtained from Sigma Chemical Co. . The reaction mixture comprised 40 μL of sucrose (480 mM) with 10 μL of sucrase (0.33 units/mL) in 50 μL of phosphate buffer solution . The mixtures were incubated at 37 °C for 60 min and then at 100 °C for a further 10 min to stop the reaction. The glucose concentrations released from the reaction mixtures were estimated via the glucose oxidase method  using a VersaMax microplate reader  at an absorbance wavelength of 540 nm.Estimates of sucrase activity were obtained using the method outlined by Akkarachiyasit et al.  with sliO-gallate, ECG, EC, and quercetin, with quercetin  as positive controls. All samples were assessed in terms of sucrase inhibitory activity under a range of concentrations (0–50 μM). Inhibitory activity was assessed by dissolving samples directly in phosphate buffer solution . Inhibitory activity was estimated in terms of IC50 values based on percent inhibition, as follows:In this study, we focused on B2-3’-O-gallate, ECG, EC, and quercetin on maltase and sucrase. Kinetics assays were conducted using substrates of various concentrations with and without inhibitors. Maltose was used as a substrate for maltase, where the initial velocity was expressed as the rate of absorbance at 540 nm per min. Sucrose was used as a substrate for sucrase, where the initial velocity was expressed as the rate of absorbance at 540 nm per 18 min. Dixon plot analysis was used to determine the competitive inhibition constant (Ki) and uncompetitive inhibition constant (Ki’). Ki was used as the equilibrium constant for the inhibition of binding to maltase and sucrase, whereas Ki’ was used as the equilibrium constant for the inhibition of binding to maltase–maltose and sucrase–sucrose complexes. The Ki and Ki’ values of the maltase inhibitors were derived from Dixon plots in accordance with the methods described by Cornish-Bowden [Lineweaver–Burk plot analysis was used to identify the mode of inhibition of B2-3’-p < 0.05.Data are expressed as the mean ± standard deviation. All data analysis was performed using Statistical Analysis System . Statistically significant differences between treatment groups were determined using one-way ANOVA followed by Duncan’s multiple range test. Three replicates of each sample were assayed, and the level of significance was set at O-gallate, ECG, and EC derived from R. crenulata. O-gallate, ECG, and EC with that of quercetin, which is a leading candidate for the treatment of diabetes mellitus [50 value of 4.8 ± 0.4 mM against rat maltase. We assessed the inhibitory effects of B2-3’-O-gallate, ECG, EC, and quercetin on maltase at various concentrations. The addition of B2-3’-O-gallate, ECG, EC, and quercetin at a concentration of 10 µM was shown to significantly enhance maltase inhibition, as follows: B2-3’-O-gallate (90.7%), ECG (87.7%), EC (66.3%), and quercetin (56.3%). Potent maltase and sucrase inhibitors have been considered for use for treating diabetes mellitus . Thus, wmellitus . Quercetmellitus  reportedO-gallate, ECG, EC, and quercetin. The IC50 values for maltase were as follows: B2-3’-O-gallate (1.73 ± 1.37 µM), ECG (3.64 ± 2.99 µM), EC (6.25 ± 1.84 µM), and quercetin (8.33 ± 3.91 µM). The strength of the IC50 values was in the following order: B2-3’-O-gallate < ECG < EC < quercetin. We also compared the inhibitory activities of B2-3’-O-gallate, ECG, EC, and quercetin toward sucrase. As shown in O-gallate, ECG, EC, and quercetin at a concentration of 20 µM was shown to significantly enhance sucrase inhibition, as follows: B2-3’-O-gallate (84.6%), ECG (56.1%), EC (51.4%), and quercetin (51.7%).50 values for sucrase were as follows: B2-3’-O-gallate (6.91 ± 3.41 µM), ECG (18.27 ± 3.99 µM), EC (18.91 ± 3.66 µM), and quercetin (18.98 ± 2.53 µM). The strength of the IC50 values was in the following order: B2-3’-O-gallate < ECG < EC < quercetin. We can see that the inhibitory effects of quercetin on sucrase were far less pronounced than those of B2-3’-O-gallate, ECG, and EC (p < 0.05). There has already been considerable research into the use of these polyphenols as maltase and sucrase inhibitors as an alternative to pharmaceutical treatments such as acarbose for the management of type 2 diabetes. Pyner et al. [50 values of polyphenol-rich green tea extract, acarbose, and epigallocatechin gallate for rat maltase were 0.035 ± 0.005 mg/mL, 0.42 ± 0.02 μM, and 14.0 ± 2.0 μM, respectively. Furthermore, the IC50 values of polyphenol-rich green tea extract, acarbose, and epigallocatechin gallate for rat sucrase were 1.8 ± 0.3 mg/mL, 12.3 ± 0.6 μM, and 950 ± 86 μM, respectively. In addition, quercetin showed inhibitory activities with IC50 values of 3.5 ± 0.3 mM against rat sucrose [50 = 12 mM), siamenoside I (IC50 = 10 mM), and mogroside III (IC50 = 1.6 mM) isolated from Siraitia grosvenori also have maltase inhibitory activity [Polyopes lancifolia. That compound had an IC50 value of 1.00 ± 0.03 mM against sucrose in rats. According to the above results, B2-3’-O-gallate, ECG, and EC have the potential to be maltase and sucrase inhibitors.As shown in r et al.  reported sucrose . Furtheractivity . Kim et activity  isolatedO-gallate, ECG, EC, and quercetin toward maltase. In this study, the Michaelis constant (Km) of maltase was 1.01 mM, whereas in the study by Pyner et al. [Km value of maltase was 7.5 mM. The Lineweaver–Burk plots of B2-3’-O-gallate , ECG , and quercetin  were mixed-competitive inhibitors of α-glucosidase [Ki calculated from a Dixon plot was 5.93 ± 0.26 µM [We also evaluated the inhibition kinetics of B2-3’-r et al. , the Km -gallate A, ECG , ECG (6.38 ± 0.04 µM), EC (18.88 ± 0.22 µM), and quercetin (14.97 ± 0.75 µM). Note that all of the Ki values were lower than the Ki’ values. As mentioned previously, Ki is the equilibrium constant for the inhibition of binding to maltase, whereas Ki’ is the equilibrium constant for the inhibition of binding to the maltase–maltose complex. In a previous report by Cortés et al. [Ki values are smaller than Ki’ values in cases of reversible mixed-competitive inhibition. This is an indication that inhibitor–enzyme binding affinity exceeds inhibitor–enzyme–substrate binding affinity, resulting in the mixed-competitive inhibition of maltase. These findings suggest that B2-3’-O-gallate, ECG, EC, and quercetin may bind to either maltase or the maltase–maltose complex. Nonetheless, the binding sites and underlying mechanisms of inhibition have yet to be elucidated.The Dixon plots in s et al. , Ki valuO-gallate, ECG, EC, and quercetin toward sucrose, the Michaelis constant (Km) was 100.4 mM. In a previous report by Pyner et al. [Km value of sucrase activity was 9.5 mM. The Lineweaver–Burk plots of B2-3’-O-gallate  was found in litchi and sweet cherry. Kinetics analysis revealed that sucrase was inhibited by cyanidin-3-rutinoside in a mixed-type manner [Ki calculated from a Dixon plot was 0.77 µM [In our analysis of the inhibition kinetics of B2-3’-r et al. , the Km -gallate A, ECG , ECG (15.74 ± 0.32 µM), EC (21.36 ± 0.30 µM), and quercetin (18.54 ± 0.07 µM). Note that the Ki values of B2-3’-O-gallate, ECG, EC, and quercetin were lower than the Ki’ values, indicating that these compounds were mixed-competitive inhibitors of sucrase. These findings suggest that B2-3’-O-gallate, ECG, EC, and quercetin may bind to either sucrase or the sucrase–sucrose complex. Nonetheless, the binding sites and underlying mechanisms of inhibition have yet to be elucidated.The Dixon plots in O-gallate, ECG, EC, and quercetin). Ki and Ki’ are the dissociation constants of the maltase–inhibitor complex and the maltase–maltose–inhibitor complex, respectively. According to our results, B2-3’-O-gallate , ECG , EC , and quercetin  are mixed-competitive inhibitors of maltase. Note that mixed-competitive inhibitors bind at sites that are distinct from maltose active sites; however, they bind to either maltase or the maltase–maltose complex. Moreover, a similar inhibition scheme was also observed in the results of B2-3’-O-gallate, ECG, EC, and quercetin against sucrase , ECG , EC , and quercetin  are mixed-competitive inhibitors of sucrase. Mixed-competitive inhibitors bind at sites that are distinct from sucrose active sites; however, they bind to either sucrase or the sucrase–sucrose complex. Note that B2-3’-O-gallate, ECG, and EC all had significant inhibitory effects on maltase as well as sucrase. sucrase B. In theO-gallate, ECG, and EC isolated from R. crenulata displayed maltase and sucrase inhibitory properties in a mixed-competitive mode. The IC50 values of these compounds for both maltase and sucrose were in the following order: EC > ECG > B2-3’-O-gallate. Among these compounds, B2-3’-O-gallate had the highest maltase and sucrose inhibitory activities. We observed that the IC50 values of B2-3’-O-gallate, ECG, and EC were lower than those of quercetin, which means that they have greater potential for the prevention of hyperglycemia associated with maltase or sucrase. Our findings clearly identify B2-3’-O-gallate, ECG, and EC as likely candidates for the treatment of diabetes mellitus, warranting further in vivo studies.B2-3’-


Using genetic and pharmacological techniques, we examined whether BTK activation in Ship1−/− MCs is mandatory for the manifestation of the well-known hyperactive phenotype of Ship1−/− MCs. We demonstrate the prominence of BTK for the Ship1−/− phenotype in a manner strictly dependent on the strength of the initial Ag stimulus; particular importance for BTK was identified in Ship1−/− bone marrow-derived MCs in response to stimulation with suboptimal Ag concentrations. With respect to MAPK activation, BTK showed particular importance at suboptimal Ag concentrations, allowing for an analogous-to-digital switch resulting in full activation of ERK1/2 already at low Ag concentrations. Our data allow for a more precise definition of the role of BTK in FcεRI-mediated signal transduction and effector function in MCs. Moreover, they suggest that reduced activation or curtate expression of SHIP1 can be compensated by pharmacological inhibition of BTK and vice versa.Antigen (Ag)-mediated crosslinking of IgE-loaded high-affinity receptors for IgE (FcεRI) on mast cells (MCs) triggers activation of proinflammatory effector functions relevant for IgE-associated allergic disorders. The cytosolic tyrosine kinase BTK and the SH2-containing inositol-5′-phosphatase SHIP1 are central positive and negative regulators of Ag-triggered MC activation, respectively, contrarily controlling Ca Antigen (Ag)-mediated crosslinking of IgE-loaded high-affinity receptors for IgE (FcεRI) on MCs drives immediate secretion of preformed mediators (e.g. biogenic amines and proteases) and de-novo synthesis of proinflammatory mediators (e.g. leukotrienes and cytokines)1. MC activation is regulated by complex signalling networks2. One crucial signalling element downstream of the FcεRI is the phosphatidylinositol-3-kinase (PI3K) phosphorylating phosphatidylinositol-4,5-bisphosphate  to yield the second messenger phosphatidylinositol-3,4,5-trisphosphate (PIP3). Several central signalling proteins contain pleckstrin homology (PH)-domains allowing them to bind to PIP3. Amongst these are PKB and Bruton’s tyrosine kinase (BTK)3.Mast cells (MCs) are critical effector cells in IgE-associated allergic disorders3 to its N-terminal PH-domain appears to release BTK from an auto-inhibited conformation, allowing transphosphorylation by LYN at Y551 in BTK’s kinase domain5. This results in activation of BTK, which then autophosphorylates at Y223 within its SH3-domain6. Though Y223 phosphorylation has only a minor role in regulating BTK catalytic activity7, it serves as an early substrate for activated BTK and thus, it can serve as indirect measure for BTK activity. By phosphorylating phospholipase C (PLC)γ, BTK controls Ag-triggered Ca2+ mobilisation, and hence, Btk−/− MCs hardly mobilise Ca2+ in response to Ag9. Moreover, BTK positively regulates Ag-induced cytokine production in MCs10.Binding of PIP3 generating PI-3,4-P211. Ship1−/− bone marrow-derived MCs (BMMCs) degranulate much stronger in response to Ag than wild-type (WT) BMMCs, which goes along with augmented store-operated Ca2+ entry13. Moreover, SHIP1 suppresses Ag-triggered degranulation in response to high, so-called supra-optimal Ag concentrations, contributing to the well-known bell-shaped dose-response curve of MC activation15. Furthermore, SHIP1 represses Ag-induced proinflammatory cytokine production17.In MCs, a crucial negative regulator of the PI3K pathway is the SH2-containing inositol-5′-phosphatase 1 (SHIP1), which dephosphorylates PIPShip1−/− BMMCs 17 are suppressed in Btk−/− BMMCs18. Moreover, BTK activation appears to depend on the SHIP1 substrate PIP3. Hence, BTK often is discussed as a central mediator of the hyperactive Ship1−/− effector phenotype. However, there are also studies challenging the strict PI3K-dependence of BTK activation19. As mentioned earlier, SHIP1 is activated in particular after stimulation with supra-optimal Ag concentrations resulting in reduced PI3K-dependent PKB phosphorylation compared to stimulation with optimal Ag concentrations15. Phosphorylation of BTK at Y223, however, was not found to be attenuated after stimulation with supra-optimal Ag doses15. Moreover, Suzuki et al. showed in B-lymphocytes that activation of the IgM B-cell Ag receptor resulted in BTK phosphorylation that was not reduced in the presence of PI3K inhibitors19. In addition, BTK phosphorylation was even enhanced in B-lymphocytes deficient for the regulatory PI3K subunit, p85α19. These data suggested that the functional relationship between PI3K and BTK might depend on the receptor/ligand system studied and the cell type investigated.Interestingly, MC functions upregulated in Ship1−/− BMMCs is mandatory for the manifestation of the various hyperactive phenotypes of Ship1−/− BMMCs. We show that BTK is phosphorylated at Y223 and Y551 in MCs independent of PI3K activation. Moreover, using DKO BMMCs we demonstrate the importance of BTK for the Ship1−/− phenotype in a manner strictly dependent on the strength of the initial Ag stimulus; particular importance for BTK was identified in response to stimulation with low  Ag concentrations. Furthermore, our data indicate that the digital nature of MAPK ERK1/2 activation in BMMCs is dependent on BTK. Our data allow for a more precise definition of the role of BTK in FcεRI-mediated signal transduction and effector functions in MCs.In the present study, we analysed the PI3K-dependence of BTK function in more detail. Using BMMCs double-deficient for SHIP1 and BTK , we examined whether BTK activation in 3 is particularly important in regulating responses to FcεRI crosslinking by supra-optimal Ag concentrations20. This also suggested that BTK, due to its reported PI3K-dependence, is involved in controlling the hyperactive Ship1−/− phenotype. However, BTK activation was also shown to be independent on PI3K, using genetic and pharmacological approaches in B cells19. Thus, we re-addressed the PI3K-dependence of BTK phosphorylation in Ag-stimulated MCs. IgE-loaded BMMCs were stimulated with two doses of the Ag DNP-HSA  in the presence or absence of the pan-specific PI3K inhibitor Wortmannin and activating phosphorylations of BTK (at Y223) and PKB (at S473) were analysed. Whereas PKB phosphorylation was completely inhibited by Wortmannin, BTK phosphorylation appeared unaltered  in BMMCs pre-treated with DMSO or 100 nM Wortmannin. Cells were left unstimulated or stimulated for 1, 2, and 5 min. In addition, DMSO-pre-treated Btk−/− BMMCs were stimulated with Ag. Control Western blots with BTK-, p85-, and P-PKB (Ser473)-specific antibodies corroborated lack of BTK in Btk−/− BMMCs, equal loading, and efficiency of PI3K inhibition, respectively  with respect to the amino acid sequence surrounding Y551 of BTK   Fig. . At high25. Titrating Ibrutinib against Ag-triggered phosphorylation of PLCγ1, 0.3 µM Ibrutinib were determined as sufficient to block BTK-mediated PLCγ1 phosphorylation. WT and Ship1−/− BMMCs were then pre-treated with vehicle or 0.3 µM Ibrutinib , stimulated with suboptimal and optimal Ag concentrations, and degranulation was measured. Indeed, Ibrutinib-treated WT and Ship1−/− BMMCs reacted comparably to Btk−/− and DKO BMMCs, respectively  is considered to be a weakly FcεRI-crosslinking agent20, we expected IgE-triggered degranulation of Ship1−/− BMMCs to be strongly suppressed by inhibition of BTK, which was indeed the case  of PLCγ1 and PLCγ2 was measured. In addition, Y-P of the transmembrane adaptor LAT1 was determined, which organises membrane-recruitment of PLCγ. Ag-triggered Y-P of LAT1 appeared to be comparable between the cell types under study  enhanced phosphorylation of p38, IKKα/β, and IκBα in Ship1−/− cells in response to suboptimal Ag concentration were reduced by Ibrutinib to levels found in WT BMMCs; and (iii) augmented PKB phosphorylation in Ag-stimulated Ship1−/− BMMCs remained unchanged in the presence of Ibrutinib  P-ERK1/2 was significantly reduced in suboptimally stimulated Ibrutinib-treated WT cells, whereas no such effect was observed in Ibrutinib-treated nib Fig. . Thus, oShip1−/− BMMCs. All the more it was interesting that BTK deficiency predominantly impacted on suboptimal stimulation, which was compensated for by additional SHIP1 deficiency, indicating functional interplay between BTK- and SHIP1-controlled signals in regulating ERK1/2 phosphorylation. Such response pattern is reminiscent of a conversion of an analogous input to a digital output as reported for activation of the small GTPase RAS by the cooperation between the different exchange factors, SOS and RASGRP29. Hence, we analysed this regulation in more detail and asked first, whether this type of regulation was specifically found in BMMCs or could also be observed in another MC type as a more general mechanism. Therefore, peritoneal MCs (PMCs) from WT and Btk−/− mice were investigated. Activation of ERK1/2 in PMCs was also particularly dependent on BTK after suboptimal Ag stimulation  should recover a WT-like phenotype in Btk−/− cells. To address the role of PLCγ in Ag-triggered ERK1/2 phosphorylation, the PLCγ inhibitor U73122 and its inactive control compound U73343 were used. IgE-preloaded WT BMMCs were pretreated with DMSO (vehicle control), U73122, or U73343 (control substance) and subsequently stimulated with a suboptimal and an optimal dose of Ag. As shown in Fig. Btk−/− BMMCs . Subsequently, phosphorylation of ERK1/2 was analysed. Of importance, the relevant PMA concentration (1 µM) did not induce ERK1/2 phosphorylation by itself , BTK inhibitor Ibrutinib , indicating that BTK activation is not necessarily dependent on PI3K in every signalling situation. Nevertheless, the question remains of how BTK can be activated in a largely PI3K-independent manner. Particular compelling in this respect is the recent finding that BTK can be activated by soluble inositol hexakisphosphate (IP6)33. IP6 induced transient BTK dimerization via the PH-TH domains and thus promoted transphosphorylation of the kinase domains even in the absence of a membrane33. Of note, IP6-BTK interaction is independent of the PIP3-binding pocket of the BTK PH-domain33. Moreover, generation of IP6 emanates from the production of IP3, which in MCs is catalysed by PLCγ in a PI3K-independent manner, potentially constituting a BTK-driven positive feedback loop33. Those data and our thoroughly controlled results show that activation/phosphorylation of BTK can be independent of PI3K; this, most likely, is dependent on cell type, stimulus, and environmental factors34.The opposing phenotypes of Ag-stimulated Btk−/− and Ship1−/− mice were crossed and eventually, WT, Btk−/−, Ship1−/−, and DKO BMMCs were differentiated and analysed. Our experiments revealed that BTK-dependence of the SHIP1-deficient phenotype varied depending on Ag concentration used and endpoint analysed  might be excluded. However, an extended titration analysis in WT and Ship1−/− BMMCs revealed that complete inhibition of degranulation in optimally stimulated Ship1−/− BMMCs required about 30-times higher concentrations of Ibrutinib compared to WT cells as well as suboptimally stimulated SHIP1-deficient cells 47. Mating of these mice resulted in Btk+/− Ship1+/− F1 progeny. F1 mice were mated to obtain WT, Btk−/−, Ship1−/−, and Btk−/−Ship1−/− mice (referred to as DKO). All mice were on a mixed C57BL/6 × 129/Sv background. Experiments were performed in accordance with German legislation governing animal studies and following the Principles of Laboratory Animal Care. Mice are held in the Institute of Laboratory Animal Science, Medical Faculty of RWTH Aachen University. The Institute holds a license for husbandry and breeding of laboratory animals from the Veterinary Office of the Städteregion Aachen (Administrative District). The Institute follows a Quality Management System, which is certified according to DIN ISO 9001/2008. Every step in this project involving mice was reviewed by the animal welfare officer. All experiments were approved by the Landesamt für Natur, Umwelt und Verbraucherschutz NRW (LANUV), Recklinghausen (AZ 84-02.04.2016.A496).The following mutant mice were used: et al.48, bone marrow cells (1 × 106/ml) from 6- to 8-week old mice were cultured at 37 °C and 5% CO2 as single cell suspensions in growth medium , 2 mM L-glutamine, 10 mM HEPES, 1 × 10−5 M 2-mercaptoethanol, 100 units/ml penicillin, and 0.1 mg/ml streptomycin). At weekly intervals, the non-adherent cells were reseeded at 1 × 106 cells/ml in fresh medium. By 4–6 weeks in culture, greater than 95% of the living cells were positive for KIT and FcεRI as assessed by PE-labelled anti-KIT  and FITC-labelled anti-FcεRI alpha antibodies , respectively. Expression of ST2 was detected by FITC-conjugated anti-mouse T1/ST2  and of CD13 by PE-conjugated rat anti-mouse CD13 . Peritoneal MCs (PMCs) were prepared and cultivated using IL-3 and SCF according to Meurer et al.50.For the generation of BMMCs according to the procedures by Razin Antibodies directed against P-PKB , P-ERK1/2 , P-IKKα/β , P-IκBα , P-LAT , P-p38 , P-PLCγ2 , and PI3K p85 (#4292) were purchased at Cell Signaling Technologies. Anti-GAPDH (sc-32233), anti-BTK (sc-1696), and anti-P-PLCγ1  antibodies were from Santa Cruz Biotechnology and anti-PLCγ1 (#2112-1) antibody wase obtained from Epitomics. The antibody against P-BTK  was purchased at R&D Systems. The following inhibitors were used: for BTK, Ibrutinib , for PLC, U73122  as well as its inactive compound U73343 , and for PI3K, Wortmannin . Dinitrophenyl (DNP)-human serum albumin (HSA) containing 30–40 mol DNP per mol albumin (A6661) and anti-DNP-specific monoclonal IgE  were purchased at Sigma-Aldrich. DMSO (A3672) was obtained from PanReac Applichem and Phorbol-12-myristate-13-acetate  from Merck Millipore.6 cells/ml in stimulation medium  for signal transduction studies and cytokine production or in Tyrode’s buffer  for degranulation studies and adapted to 37 °C for 20 min. If necessary, the cells were treated at 37 °C with the respective inhibitor for the mentioned times before stimulation or were directly stimulated 20 min for degranulation, 4 h for the production of cytokines (IL-6 and TNF-α), or as indicated for the signal transduction analyses.MCs were pre-loaded overnight with 0.15 µg/ml IgE in starvation medium (without IL-3) or in growth medium (without SCF) for BMMCs or PMCs, respectively. The next day, the cells were washed with PBS and resuspended at a density of 1–2 × 1051.For degranulation studies, the percentage of degranulation was determined by measuring the release of β-hexosaminidase52 containing 0.5% IGEPAL and 0.5% Na-deoxycholate. To prevent degradation during lysis, the PMC pellets were directly lysed in 95 °C hot 1 x Laemmli buffer for 10 min. Whole-cell lysates of BMMCs, after centrifugation at 4 °C with 16,000 × g for 15 min, were subjected to SDS-10%-PAGE. For Western Blot analysis, the proteins were blotted for 1–2 hours with 350 mA onto a PVDF membrane  using a 25 mM Tris, 192 mM glycine, and 20% methanol solution. The membrane was blocked with 2% w/v milk powder in wash buffer (PBS 0.1% Tween) for 1 h at RT before the incubation with the respective primary antibody overnight. Particularly for detection of P-BTK (Tyr551), the PVDF membrane was dried overnight after protein transfer, reactivated by methanol treatment, and blocked with 10% w/v BSA in wash buffer before incubation with primary antibody overnight at 4 °C. The next day, following incubation with HRP-coupled secondary antibody, the membrane was developed with enhanced chemiluminescence solution using a LAS 4000 .Following stimulation, cells were pelleted and the pellets were solubilised at 4 °C in phosphorylation solubilisation bufferThe supernatants of stimulated BMMCs that were centrifuged for 10 min at 1,000 × g, were used for the detection of secreted cytokines. Mouse IL-6 (BD Pharmingen) and mouse TNF-α ELISAs (R&D Systems) were performed according to the manufacturer’s instructions. Concentrations of cytokines varied between experiments and/or cell cultures due to age of the cells. Qualitative differences or similarities between the genotypes, however, were consistent throughout the study.7 cells/ml in RPMI 1640 containing 1% FCS and stained with 50 µM Indo-1 AM  and 0.5% pluronic F-127  for 45 min at 37 °C. Afterwards, cells were resuspended in RPMI/1% FCS and analysed with the indicated stimulations using a LSR II (BD). FACS profiles were converted to line graph data using the FlowJo analysis software (Treestar).IgE-preloaded BMMCs were resuspended at 1 × 10Statistical data were analysed from n independent experiments (with n indicated in the respective figure legend) using PRISM version 7.0c . P-values were calculated by a two-tailed Student’s t-test for WT vs. DKO cells and by a one-tailed Student’s t-test for BTK−/− or SHIP1−/− vs. DKO cells. P-values smaller than 0.05 with (*)p < 0.05, (**)p < 0.005, and (***)p < 0.0005 were considered statistically significant and p-values bigger than 0.05 were non significant (n.s.). No adjustment for multiple testing was performed since all analyses were explorative.Supplementary Information