Whether the sensitivity of seasonal climate to insolation matches theoretical predictions has not been assessed over long timescales. Here, we analyse a continuous record of water-isotope ratios from the West Antarctic Ice Sheet Divide ice core to reveal summer and winter temperature changes through the last 11,000 years. Summer temperatures in West Antarctica increased through the early-to-mid-Holocene, reached a peak 4,100 years ago and then decreased to the present. Climate model simulations show that these variations primarily reflect changes in maximum summer insolation, confirming the general connection between seasonal insolation and warming and demonstrating the importance of insolation intensity rather than seasonally integrated insolation or season duration3. Winter temperatures varied less overall, consistent with predictions from insolation forcing, but also fluctuated in the early Holocene, probably owing to changes in meridional heat transport. The magnitudes of summer and winter temperature changes constrain the lowering of the West Antarctic Ice Sheet surface since the early Holocene to less than 162 m and probably less than 58 m, consistent with geological constraints elsewhere in West Antarctica7.The recovery of long-term climate proxy records with seasonal resolution is rare because of natural smoothing processes, discontinuities and limitations in measurement resolution. Yet insolation forcing, a primary driver of multimillennial-scale climate change, acts through seasonal variations with direct impacts on seasonal climate Analysis of a continuous record of water-isotope ratios from the West Antarctic Ice Sheet Divide ice core reveals a dominant role for annual maximum insolation in determining West Antarctic summer temperature during the Holocene. By controlling summer temperatures and ice ablation, summer insolation in the northern high latitudes is thought to drive global ice volume changes over glacial–interglacial timescales8. Although modelling studies support this idea10, empirical evidence of the specific climate response to insolation changes derives almost entirely from mean annual temperature reconstructions12 or from indirect effects on, for example, trapped gases and melt layers in polar ice14 and marine aeolian deposits15. The absence of seasonal temperature reconstructions has precluded direct evidence of insolation forcing on seasonal climate, a relationship that may vary geographically. In Antarctica, long records of multiple glacial–interglacial cycles have supported different claims about whether the effects of summer insolation relate most strongly to its maximum intensity, its seasonal integral or to duration above a threshold17. Site-specific empirical determinations would provide valuable tests of such competing ideas.Milankovitch famously postulated that variations of Earth’s orbit and axis drive climate changes over tens of thousands of years by altering the seasonal cycle of insolation18. Our reconstruction  and performed model experiments to understand its physical controls. The Holocene offers a window of time for assessing the influence of orbital forcing without the complicating effects of Northern Hemisphere deglaciationon Figs.  uses theFigs. 20 , obtaine24),whereas only climate model simulations are available for older periods10. For Antarctica, before the present study, the longest records spanned only a few centuries25. A combination of three factors accounts for the considerably greater scope of our reconstruction: exceptional depth resolution of measurements, conditions at WAIS Divide  which allow for preservation of subannual information through the entire Holocene26 and an analysis strategy which circumvents interannual noise by evaluating millennial averages of the seasonal parameters.Records of seasonal temperatures from ice cores are limited by measurement resolution and information loss from water-isotope diffusion. In Greenland, the longest records separating summer and winter variability extend to only 2 thousand years ago (ka)  rather than insolation anywhere in the subtropical through subpolar latitudes . Indeed, models indicate heat export from WAIS Divide in summer   concentrations and two different ice-sheet histories, GLAC1D and ICE-6G, which entail net surface lowerings of about 83 m and about 208 m, respectively, from 11 to 7 ka at the WDC site Fig. . These eσ) from 10 to 3.5 ka or 54 m increase to 162 m decrease (2σ) if the time interval is narrowed to 10 to 6.5 ka  water isotopes using continuous-flow analysis 48. All other Holocene data are previously published31 and available online20. Data are reported in 5-mm increments in delta-notation  relative to Vienna standard mean ocean water (δ18O = δD = 0‰), normalized to standard light Antarctic precipitation . WDC is annually dated, with accuracy better than 0.5% of the age between 0 and 12 ka (ref. 22). For the Holocene, the temporal spacing of consecutive 5 mm samples is <0.1 yr and the average <0.05 yr, ranging from about 2.6 weeks at 10 ka to 0.5 week at 1 to 0 ka (ref. 18).WDC water isotopes , z the depth (m) and −1) at a given depth. The diffusion-corrected spectrum takes the form of a series of complex numbers Diffusion in the firn and deeper ice attenuates high-frequency water-isotope information in ice cores26 calculated in each 140-yr window. Before spectral analysis, the isotope data are linearly interpolated at a uniform time interval of 0.05 yr. Our determination of diffusive attenuation and correction arises from the observed frequency spectra themselves and therefore is entirely independent of firn diffusion and densification models.Uncertainties on To select extrema (summers and winters) in the diffusion-corrected δD signal Fig. , we used29. Finally, to find summer and winter temperatures we added the individual seasonal temperature amplitudes to the year’s mean temperature obtained previously30 by calibrating the water-isotope record against borehole temperatures and δ15N constraints on firn thickness.A linear scaling converted seasonal isotopic amplitudes to seasonal temperature amplitudes, using a sensitivity of isotopes to surface temperatures determined by the simple water-isotope model (SWIM)54, which simulate the transport and distillation of moisture down climatological temperature gradients. As moist air is transported towards the poles and cools, the saturated vapour pressure decreases nonlinearly and moisture above saturation is removed by precipitation. The model keeps track of the isotopic fractionations at each step along this distillation process. In most previous simple models, there is an inconsistency in the calculation of the supersaturation that determines the point of condensation and that drives kinetic isotope fractionation. Modifications to these earlier models, used in SWIM, ensure consistency in the calculation, which results in a smoother relationship between temperature and the δ-values of precipitation and better agreement with observed spatial patterns of δD and δ18O. Given input of both δD and δ18O data, SWIM calculates distributions of source temperatures, the temperature gradients of pseudo-adiabatic pathways and condensation temperature. We used SWIM to derive sensitivities for surface isotope-temperature scalings using diffusion-corrected WDC data to obtain a surface scaling of 6.96‰ δD °C−1. Using raw data, the surface scaling is 7.07‰ δD °C−1. In comparison to other isotope-temperature scalings, ref. 55 obtain about 6.56‰ δD °C−1 and ref. 30 about 7.10‰ δD °C−1 (both converted from δ18O to δD using a factor of 8).SWIM is based on earlier numerical Rayleigh-type distillation modelsσ) (ref. 21). The ‘diffusion-correction uncertainty’ is described in ref. 26. The uncertainty of the mean-temperature reconstruction, calculated previously30, accounts for most uncertainty in the early Holocene but a small fraction in the late Holocene. Sections ‘σ uncertainties for summer and winter temperature 57, a firn-evolution model with coupled firn temperature, firn densification and water-isotope modules, to test how seasonally weighted accumulation affects the diffusion of specified, hypothetical isotope records progressing from surface snow (δDsnow), to consolidated snowpack in the firn (δDfirn), to solid ice beneath the pore close-off depth (δDice). We applied the back-diffusion calculation  as in summer (October to March). The five scenarios are as follows: (1) ‘constant’: identical accumulation for all months ; (2) ‘cycle’: monthly accumulation equal to MAR monthly means; (3) ‘noise’: using the ‘cycle’ time series, we add noise to each time step in the ‘cycle’ series in the form of a normal random variable of zero mean and the standard deviation for the month from MAR; (4) ‘random’: for each month, the accumulation is a normal random variable with mean and standard deviation equal to MAR monthly values; and (5) ‘loop’: the entire 39-yr MAR accumulation time series is repeated over and over again. For the temperature boundary condition, we used the mean temperature at a height of 2 m for 1979–2017 for each month predicted by MAR to create an annual temperature cycle. We repeated this 12-month time series for the duration of the model runs. This method ensures that model runs, which are designed to test accumulation seasonality, are not affected by interannual temperature variability, while also providing an estimate of the annual temperature cycle, which affects the rate of isotope diffusion in the upper firn.We performed five CFM runs using monthly time steps for accumulation, temperature and isotopes  could arise from large changes in seasonal-accumulation weighting, whereas multimillennial trends ≫2.6‰ are unlikely to be caused by seasonal accumulation and can therefore be interpreted as climate signals of a different origin. For 1,000-yr averaging . Seasonal-fire regimes in South America dominate BC concentrations at WDC, causing BC maxima and minima in autumn and spring, respectively59  concentrations, the only age-scale-independent impurity. BC data are available from 0–2.5 ka and 6–11 ka  to temperature yields three curves for summer and three for winter: R2 values for summer and winter versus the mean. We then subtracted the 1,000-yr averages from the 300-yr averages to obtain residuals and then determined R2 values again for summer and winter versus the mean  or whether they fluctuate at high-frequency.We randomly generated a large number of alternative seasonal temperature histories governed by the uncertainties on 1,000-yr averages , following bulk aerodynamic formulae. We calculated the annual TOA insolation-cycle at every latitude in 500-yr time slices from 0 to 11 ka. For each time slice the model is run at 2 hour time resolution for 30 model-years to reach equilibrium.We used a simple global, zonal mean MEBM to calculate surface temperatures  at the WDC site to the summer maximum and winter minimum site temperature and insolation. Although Holocene changes in ∇·F at the WDC site correlate with insolation forcing, the magnitude of changes in maximum direct insolation are much larger than those in atmospheric-heat divergence. Further, the Holocene changes in summertime heat divergence are of the wrong sign to cause net heating at the WDC site (positive divergence is an export of heat by the atmosphere from the site). Heat transport in the Antarctic is convergent in the annual mean but divergent in mid-summer, as intense incoming insolation exceeds longwave emissions from the cold surface.Extended Data Fig. 65), v.HadCM3BM2.1, which well simulates tropical Pacific climate and its response to glacial forcing66. Our simulations are snapshots at 1 kyr intervals over the last 11 ka (ref. 35), with time-specific boundary conditions of orbital forcing67, GHG concentration69, ice-sheet topography and sea level74. We used three simulations: (1) only orbital forcing changes (ORBIT), with all other boundary conditions set to the pre-industrial; (2) orbital/GHG forcing with GLAC1D ice-sheet elevation history; and (3) orbital/GHG forcing with ICE-6G ice-sheet elevation history. Elevation histories are shown in Fig. We used the fully coupled ocean-atmosphere model HadCM3 (refs. d) and consequent cooling. Downward longwave radiation (LWd) also decreases, probably because of atmospheric cooling. Sensible heat flux (SHd) to the surface is increased, indicating that the atmosphere and surface do not equally cool; one cause of this is the increased meridional heat convergence (−∇·F).We examined the zonal mean at 80° S in simulations for hypothetical 10 ka worlds, by changing the boundary conditions to compare to the pre-industrial/late Holocene. These simulations are ‘10 ka ORBIT-only’; two runs with only ice sheets at 10 ka and pre-industrial settings otherwise, called ’10 ka GLAC1D-only’ and ‘10 ka ICE-6G-only’; and two runs with all 10 ka forcings, called ‘10 ka GLAC1D-all’ and ‘10 ka ICE-6G-all’. In ‘10 ka ORBIT-only’, reduced TOA shortwave radiation causes a large reduction in shortwave radiation at the surface . Both ice-sheet scenarios also cause an increase in −∇·F, partly counteracting summertime cooling.Changed ice sheets cause summertime cooling in both ‘10 ka GLAC1D-only’ and ‘10 ka ICE-6G-only’, primarily via reduced LWd and SWd. The decrease in SWd is similar in ‘10 ka GLAC1D-all’ and ‘10 ka ICE-6G-all’ and slightly smaller than in ‘10 ka ORBIT-only’, probably because the thinner atmospheric column reduces absorption. The decrease in LWd in ‘10 ka GLAC1D-all’ and ‘10 ka ICE-6G-all’ is larger than in ‘10 ka ORBIT-only’, ‘10 ka GLAC1D-only’ and ‘10 ka ICE-6G-only’. This indicates the importance of feedbacks in the atmosphere. Heat convergence −∇·F increases in both simulations indicating remote feedbacks, in addition to local feedbacks related to the amount of water vapour in the atmosphere.Using all 10 ka forcings causes cooling through both LWd and LWd by roughly the same magnitude. With full forcing (including ice sheets), the reduction in LWd is roughly three times the reduction in SWd. Considering an energy budget over the WDC site , mechanisms are the same as for the zonal mean. Magnitudes of forcings change but reduced SWd still cools the surface, amplified by an LWd feedback dependent on ice-sheet size.The preceding description of changes in the zonal mean in the 10 ka simulations compared to pre-industrial period holds for the entire Holocene epoch. Orbital forcing alone reduces SWd is no longer a factor as the sun is below the horizon, yet there is still surface warming caused by an increase in LWd. An increase in −∇·F in ‘10 ka ORBIT-only’ warms the atmospheric temperature, increasing LWd and SHd. With an ice sheet imposed, the surface temperature cools. In both ‘10 ka GLAC1D-only’ and ‘10 ka ICE-6G-only’ there is a reduction in −∇·F, reducing LWd and SHd. When all 10 ka forcings are introduced, the change in temperature is smaller than for ice sheet-only runs. In ‘10 ka GLAC1D-all’ we found no change in −∇·F, LWd or surface temperature. This suggests that the increase in −∇·F from orbital forcing is almost perfectly balanced by the change in −∇·F from the ice-sheet configuration.During winter, SW75. This cannot, however, explain the prominent millennial-scale changes at about 9.2 and about 7.9 ka  and individually for three subset simulations , from 0 to 11 ka. We projected the model-simulated sea ice for each individual time-slice simulation onto these patterns to compute the amplitude of sea ice variability in each simulation. The amplitude was compared to temperature at 80° S to understand how large-scale changes in the sea ice affect temperature for the months of December (summer) and July (winter) . This suggests that winter sea ice is not an important factor in determining the temperature at 80° S. In summer, only the ORBIT simulation has meaningful correlations between temperature and sea ice variability . The sign of the correlation changes between simulations despite the sea ice change pattern being the same in all simulations. From this we concluded that sea ice is not a dominant control on temperature at 80° S in summer. The correlation in the ORBIT simulations suggests that there may be some relationship between sea ice and temperature; we investigated this with atmosphere-only simulations.In atmosphere-only simulations at 0 ka and 10 ka, we specified the top of the atmosphere insolation, sea surface temperature (SST) and sea ice from the ORBIT simulations. Over land areas and sea ice regions, the model calculates the surface temperature using the land-surface scheme in the model. The atmosphere model is identical to the model used within the coupled model. We ran a series of experiments varying the orbital configuration, SST or sea ice . We found that the atmosphere-only model does not compare well with the coupled model, simulating very little change in the surface temperature. Doing a term-by-term decomposition of the atmosphere model is not, therefore, particularly useful as it tells us more about the model rather than the physical climate. The failure of the atmosphere-only model to capture the changes at 10 ka suggests that the importance of the SST and sea ice is in their day-to-day coupling with the atmosphere and not in any long-term mean change in this season.TR exceeds that of ORBIT by an amount ΔTR − ΔTO. This can be compared to the effective lapse rate (ΔTM − ΔTO)/ΔZM defined by a HadCM3 simulation including topographic change (GLAC1D or ICE-6G models) and all forcings, for model warming ΔTM and model elevation decrease ΔZM. Specifically, the estimated elevation decrease is ΔZR = ΔZM((ΔTR − ΔTO)/(ΔTM − ΔTO)). Summer and winter reconstructions offer two separate assessments, for which we calculate the algebraic average.Temperatures simulated by HadCM3 for ORBIT provide a control scenario against which observations can be compared to identify the signal of elevation change. For a chosen time interval, the net reconstructed warming ΔTR requires recognizing that uncertainties of summer and winter reconstructions are not independent, while also recognizing that they emerge from two independent sources: uncertainty in the mean annual temperature history  and uncertainty in the seasonal amplitude . In general, the uncertainty of seasonal temperature at a specified time is the quadrature sum of annual and amplitude uncertainties, which gives 1σ and 2σ uncertainties for a given season and time. However, if the true value of annual temperature is shifted by an amount ασ from the nominal reconstruction, this must be true for both summer and winter. And if the true value of amplitude is shifted by an amount βσ from the nominal reconstruction, the temperature shift must be +βσ in one season but −βσ in the other.Accounting for uncertainties in ΔTR for a season by differencing the upper (or lower) limit at one end of the interval with the lower (or upper) limit at the other end and also calculating the corresponding ΔTR for the opposite season required by the correlated errors. The elevation decreases ΔZR were then calculated by comparison to HadCM3 simulations, as specified previously and the summer and winter values averaged. This process was completed four times for each time interval and HadCM3 model, corresponding to four different initial ΔTR (maximum and minimum ΔTR for summer and maximum and minimum ΔTR for winter) and the most extreme case taken as the result (this proved to be the one starting with maximum summer ΔTR). Table To define bounding cases on elevation change in a specified time interval, we calculated the maximal  temperature change ΔAny methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05411-8.


The fractions with the highest inhibition effect were separated and purified using ultrafiltration and cation exchange chromatography. Four novel α-glucosidase inhibitory peptides  were identified by nano-HPLC-MS/MS and molecular docking. Molecular docking showed that peptides could occupy the active pocket of α-glucosidase through hydrogen bonding, hydrophobic interaction, salt bridges, and π-stacking, thus preventing the formation of complexes between α-glucosidase and the substrate. In addition, the α-glucosidase inhibitory activity of PMHs was stable against hot, pH treatment and in vitro gastrointestinal digestion. The study demonstrated that PMHs might be used as a natural anti-diabetic material with the potential to inhibit α-glucosidase.Hot-pressed peanut meal protein hydrolysates are rich in Arg residue, but there is a lack of research on their α-glucosidase inhibitory activity. In this study, different proteases were used to produce hot-pressed peanut meal protein hydrolysates (PMHs) to evaluate the α-glucosidase inhibitory activity. All PMHs showed good α-glucosidase inhibitory activity with the best inhibition effect coming from the dual enzyme system of Alcalase and Neutrase with an IC Diabetes mellitus (DM) is an endocrine and metabolic disease characterized by hyperglycemia worldwide. As reported in the 2019 International Diabetes Federation (IDF) atlas, 463 million adults between the ages of 20 and 79 have DM, and this number is expected to rise to 702.2 million by 2045. Ninety percent of DM is Type 2 diabetes mellitus (T2DM) ,9.Peanut meals are by-products of the peanut oil processing industry . Peanut Therefore, in this research, we explored the influence of various proteases on the α-glucosidase inhibitory activity of PMHs and selected the enzyme with the strongest inhibitory activity to prepare α-glucosidase inhibitory peptides. Peptides with the best α-glucosidase inhibitory were isolated by ultrafiltration and ion exchange chromatography and further identified by nano-HPLC-MS/MS. Possible α-glucosidase inhibition mechanisms of PMHs were investigated by molecular docking. At last, the inhibitory stability of PMHs against pH, hot, and in vitro gastrointestinal digestion was evaluated.Defatted hot-pressed peanut meals were gifts of Shandong Jinsheng Grain Oil and Food Co., Ltd. . Alcalase, Protamex, Papain, Flavourzyme, and Neutrase were purchased from Novozymes . α-glucosidase and its substrate p-nitrophenyl-α-D-glucopyranoside (pNPG) were bought from Shanghai Yuanye Biotechnology Co., Ltd. . Other reagents were analytically pure.The PMHs were produced by Alcalase , Protamex , Neutrase , Flavourzyme , Papain  and a double enzyme system , respectively. The peanut meals were added into deionized water with a concentration of 10% and stirred for 30 min. Then, the pH and temperature of the dispersions were adjusted to the optimum working conditions of each enzyme. One percent of the protease was added to initiate the hydrolysis which lasted for 4 h. The hydrolysis time was 2 h for each protease in the double enzyme system. The proteases were inactivated by boiling for 15 min. After adjusting to pH 7.0, the hydrolysates were centrifuged for 20 min at 4000 rpm. The resulting supernatant was freeze-dried and stored at −20 °C until use.toth recognized as 7.55 meqv/g.The degree of hydrolysis (DH) was measured by the pH-stat method as described by Rios-Herrera et al.  with theN is the nitrogen content of the supernatant; N0 is the nitrogen content of the initial hydrolysate solution.The TCA-NSI was determined according to the method of Kong  with minhN is the nitrogen content of the hydrolysates (g) and mN is the nitrogen content in the hot-pressed peanut meals (g).The nitrogen yields of the hydrolysis were calculated as follows:2CO3 (0.2 M) was added to terminate the reaction. The absorbance was detected and recorded at 405 nm using a microplate reader. In the sample blank, the same amount of PBS was used to replace α-glucosidase; in the blank control the same amount of PBS was used to replace the hydrolysate solution, and only PBS and Na2CO3 were added to the blank. The α-glucosidase inhibitory rate was calculated as follows:The determination method of α-glucosidase inhibitory activity described by previous studies ,22,23 waXL (7.8 × 300 mm), eluate being ultra-pure water: acetonitrile: Trifluoroacetic acid = 55:45:0.1 (v/v/v), detection wavelength is 220 nm, flow rate being 0.5 mL/min. The temperature of the column was controlled at 30 °C.The molecular weight distribution of PMHs was determined using size exclusion high-performance liquid chromatography (SEC-HPLC). The detection conditions were as follows: the chromatographic column being TSK gel G2000 SW50 was calculated.The PMHs with strongest activity were separated by ultrafiltration membranes with molecular weight cut-offs (MWCO) of 3 kDa and 10 kDa, respectively. Three fractions with molecular weights <3 kDa, 3~10 kDa, and >10 kDa were collected and lyophilized. The inhibitory rate against α-glucosidase was determined and ICThe ultrafiltration fraction with the strongest α-glucosidase inhibitory activity was dissolved in deionized water (20 mg/mL) and further separated via a 732-cation exchange resin column (26 × 300 mm) equilibrated with deionized water. The loading quality was 5 mL. The column was eluted with different concentrations of ammoniac solutions  at a flow rate of 4 mL/min, and the eluate was detected at 220 nm. The peaks of the eluates were collected, concentrated, dialyzed, and freeze-dried. The α-glucosidase inhibitory rate of each separated fraction was determined at a concentration of 0.27 mg/mL.TM RSLC, Thermo Fisher Scientific, Waltham, MA, USA) and high-resolution mass spectrometry  was employed. Desalting was performed using a Waters SEP-PAK C18 solid phase extraction column. The desalted sample was dissolved in 0.1% (v/v) formic acid solution to a concentration of 1 mg/mL. The injection volume was 1.0 μL. The flow rate was 300 nL/min, and the column temperature was 30 °C. Mobile phase A was ultrapure water with 0.1% (v/v) formic acid, and mobile phase B was acetonitrile with 0.1% (v/v) formic acid. The chromatographic separation was carried out as follows: 4–28% B (0–98 min); 28–37% B (98–113 min); 37–100% B (113–117 min); 100% B (117–120 min). The mass spectral conditions were as follows: (1) detection mode: positive ions; (2) MS: scan rage (m/z) = 400–1800; resolution = 60,000; automatic gain control (AGC) target = 3 × 106; maximum injection time 50 ms; (3) MS/MS: resolution = 15,000; isolation window = 1.6; AGC target = 2 × 105; maximum injection time = 20 ms; (4) collision energy = 28 eV.The separated fraction with the strongest α-glucosidase inhibitory activity was identified. A nano upgraded high-performance liquid chromatography  equipped with a C18 column  [http://www.pymol.org/, accessed on 18 August 2022).2QMJ, the crystal structure of α-glucosidase, came from the Research Collaboratory for Structural Biology Protein Data Bank (er 2022) . Chem-Bier 2022) . The seaThe determination of inhibitory stability against α-glucosidase of PMHs under pH, hot, and in vitro gastrointestinal digestion was modified according to the method of Ketnawa et al.  and BrodAll the tests were carried out at least three times, and data were presented as mean values ± standard deviation. The significance was analyzed by one-way analysis of variance (ANOVA) through SPSS 25.0 .Each enzyme has its specific catalytic site(s) for the hydrolysis of proteins, releasing peptides with different molecular weights, amino acid sequences, and bioactivities . To evalp < 0.05) than that of PMH-Alc. Therefore, a double enzyme system of Neutrase and Alcalase was further applied to increase the DH, yield and TCA-NSI. The resulting hydrolysate was designated as PMH-NA. Compared with the single-enzyme system, the dual-enzyme system significantly increased the yield and DH. As shown in p < 0.05) than that of PMH-Alc and PMH-Neu. Although the TCA-NSI of PMH-NA was smaller than that of PMH-Alc, it was significantly higher (p < 0.05) than that of PMH-Neu. PMH-NA showed similar α-glucosidase inhibitory activity to PMH-Neu, with an IC50 value of 5.63 ± 0.19 mg/mL. This IC50 value was lower than that of Ginkgo biloba seed protein hydrolysate produced by Alcalase with an IC50 value of 12.94 ± 0.37 mg/mL [50 value of 6.21 ± 0.41 mg/mL [Although PMH-Neu showed the strongest α-glucosidase inhibitory activity, DH, yield, and TCA-NSI were significantly smaller  were collected and desalted with a 100 Da dialysis membrane. The α-glucosidase inhibitory rate at a concentration of 0.27 mg/mL is shown in In order to identify the peptides with α-glucosidase inhibitory activity in the isolated components, the amino acid sequences of C-I were identified by nano-HPLC-MS/MS and a total of 643 peptides were obtained A. The prThe binding energy of peptides and α-glucosidase were predicted using molecular docking. 2QMJ was chosen as the crystal structure of α-glucosidase because of the homo sapiens attribute and the high resolution of 1.9 Å. The docking of acarbose and α-glucosidase was produced firstly to validate the docking method. It could be observed that acarbose bonded to residues Asp203, Thr205, Asp327, Arg526, Asp542, and His600 of 2QMJ and eight hydrogen bonds were formed. The binding sites were the same as those of a previous study [8-barrel structure (residues 270–651), a loop belonging to the N-terminal domain (residues 200–217), and parts of catalytic Insert 1 (residues 367–416) and Insert 2 (residues 447–492). Asp443 and Asp542 play crucial roles in α-glucosidase catalytic reactions [The active site of the α-glucosidase applied in this study is a pocket formed by four parts, namely C-terminal β-strand residues originating from the (β/α) residues 7–492. Asp < 0.05) lower than other pH levels, which may be due to the inactivation of some fragments of bioactive peptides under extremely acidic and alkaline pH conditions [Bioactive peptides will inevitably undergo processing, at variable pH and temperature, and gastrointestinal digestion before reaching the active pocket of α-glucosidase. So, it is practical to evaluate the bioactivity stability of PMHs under these conditions to ensure PMHs inhibit α-glucosidase in the body. As shown in nditions . Therefonditions . The rel50 of which decreased significantly after simulated gastrointestinal digestion [According to Brodkorb et al. , althougigestion . These rIn this study, four types of novel α-glucosidase inhibitory peptides, FYNPAAGR, PGVLPVAS, FFVPPSQQ, and FSYNPQAG, were identified by virtual screening from hot-pressed peanut meal protein hydrolysates. The molecular docking results indicated that four interactions  formed between peptides and α-glucosidase. Hydrogen bonding and hydrophobic interaction played significant roles. Moreover, the α-glucosidase inhibitory peptides of PMHs remained stable after pH treatment, heat treatment, and in vitro gastrointestinal digestion. Our results not only shed light on current knowledge about hypoglycemic peptides but also provide an option for value-added processing of hot-pressed peanut meals.


In this review, we have summarized classical post-translational modifications (PTMs) such as phosphorylation, ubiquitylation, and SUMOylation of the different components of one of the most studied NLRP3, and other emerging inflammasomes. We will highlight how the discovery of these modifications have provided mechanistic insight into the biology, function, and regulation of these multiprotein complexes not only in the context of the innate immune system but also in adaptive immunity, hematopoiesis, bone marrow transplantation, as well and their role in human diseases. We have also collected available information concerning less-studied modifications such as acetylation, ADP-ribosylation, nitrosylation, prenylation, citrullination, and emphasized their relevance in the regulation of inflammasome complex formation. We have described disease-associated mutations affecting PTMs of inflammasome components. Finally, we have discussed how a deeper understanding of different PTMs can help the development of biomarkers and identification of novel drug targets to treat diseases caused by the malfunctioning of inflammasomes. Microbial detection is achieved through germline-encoded pattern-recognition receptors (PRRs) that survey both the extracellular and intracellular spaces for pathogens-associated molecular patterns (PAMPs) . These PRecognition of the canonical inflammasome agonists results in sensor activation, its oligomerization, and in most of the cases recruitment of an adaptor protein ASC , which consists of two death-fold domains: a pyrin domain (PYD) and a caspase recruitment domain (CARD). These domains allow ASC to bridge the upstream inflammasome sensor molecule to Caspase-1. Formation of the inflammasome complex turns an inactive Caspase-1 into an active protease, which cleaves pro-interleukin (IL) 1 and pro-IL18 into their bioactive form . The nonTo treat diseases caused by the overactivation of inflammasome and achieve specificity, elucidation of detailed inflammasome signaling is necessary. Despite the involvement of inflammasome signaling in various disease pathologies, not much is known about the biochemical and molecular basis of inflammasome signaling. As an example, the NLRP3 inflammasome activation requires two signals, signal 1 activated by the engagement of a TLR agonist, such as LPS (a TLR4 agonist), and stimulation of signal 2 can be achieved by a wide variety of molecules including extracellular ATP, nigericin, and various components of bacteria and viruses , but sigThe canonical inflammasomes are composed of three key components, a sensor, in some cases an adaptor, and the effector caspase proteases. There are mainly three classes of sensor molecules which include the NOD-like receptors (NLRs), the AIM2-like receptors (ALRs), and the pyrin sensor. Although the human genome encodes 23 NLRs , only NLShigella effector E3-ligase IpaH7.8 resulting in proteasomal degradation of NLRP1, releasing the bioactive C-terminus to bind and activate caspase 1  and a CARD domain at its carboxy-terminus, in addition to an amino-terminal PYD, a NOD, and LRR domains. There is only one human NLRP1, while multiple paralogs of NLRP1  sharing similar domain organization are found in mice, with the exception that mouse NLRP1 lack PYD. NLRP1 undergoes autolytic proteolysis within the FIIND domain to generate two non-covalently associated fragments that remain in an auto-inhibited state. To explain full activation of NLRP1, a “functional degradation” model has been proposed, which involves either cleavage of the N-terminus of NLRP1 by Anthrax lethal factor or ubiquitylation induced by aspase 1 . NLRP1 aListeria , intraceListeria , and virListeria , but theAIM2 and IFI16 (interferon gamma-inducible protein) belong to the ALRs family and comprise an N-terminal PYD that interacts with ASC, and a C-terminal HIN  domain for the recognition of double-stranded DNA (dsDNA) . A diffeClassical inflammasome complex formation has not been demonstrated for the non-canonical inflammasomes. Instead, caspase oligomerization and autoactivation are shown to be mediated by direct binding of the CARD domain of caspase-11 with cell wall components of bacteria such as LPS. This binding induces caspase oligomerization and activation, with the subsequent cleavage of gasdermin D (GSDMD) and cell death by pyroptosis .The most widely studied PTM in the context of inflammasome signaling is protein phosphorylation. In recent years, several comprehensive reviews have been published on this topic and we refer to them for more detailed information . They ofKinases carry out the phosphorylation reactions by transferring the gamma phosphate of ATP onto hydroxyl groups of various substrates including lipids, sugars, or amino acids . This prProtein kinases of eukaryotes mainly phosphorylate either tyrosine , serine/threonine  or both tyrosine and threonine  . For theThe best studied phosphorylation sites will be discussed in this chapter according to their location within or outside the characteristic domains of NLRs, pyrin ASC and the two inflammatory caspases . summariThe pyrin domain (PYD) is found in the N-terminus of inflammasome sensors and the adaptor protein ASC, enables protein-protein interaction between the inflammasome sensor and the adaptor protein, a critical step in inflammasome activation. So far, functionally characterized phosphorylation sites in this domain for NLRP3 have generally been demonstrated to have an inhibitory effect on inflammasome activation.in vitro and in vivo  inhibitor phenylarsine oxide (PAO), which prevented AIM2 and NLRP3 inflammasome activation in human and mouse macrophages. Moreover, site-directed mutagenesis of ASC tyrosine residue Tyr60 to phenylalanine, a non-phosphorylatable tyrosine mimic led to a significant reduction in inflammasome activity measured by IL-1β release , highligSubcellular location of ASC is dependent on its phosphorylation status. The protein kinase IKKα phosphorylates murine ASC in the PYD domain at serine 16 (Thr16 in human) and at Ser193 within the CARD domain (human Ser195) . PhosphoGiven this evidence that phosphorylation events in this domain negatively impact the interaction between the inflammasome sensor and ASC, one can speculate that phosphorylation events for other NLRPs might have negative effects on their inflammasome activation. NLR proteins contain a central NACHT domain, which possess nucleoside-triphosphatase (NTPase) activity. In a recent review, Sandall and colleagues highlight the importance of ATP binding and hydrolysis for inflammasome activation .The NLRP3 ATPase activity is sensitive to phosphorylation at Ser295 by protein kinase PKA, as demonstrated by Despite the identification of other putative phosphorylation sites in various inflammasome sensor proteins , little NLRs (apart from NLRP10) contain leucine-rich repeat (LRR) domain, usually located towards the C-terminus. The role of this domain includes auto-inhibition and ligand sensing. Several phosphorylation sites were identified in various LRR domains and can be found summarized in .in vitro and in vivo. Full-length, membrane-associated NLRP3 possess a double-ring cage structure, which is facilitated by LRR-LRR and PYD-PYD interactions  has been identified to be important for inflammasome assembly and activation . NLRP3-aractions . Ser803 via ubiquitination at Lys380 (Phosphorylation at Ser891/895 in mouse macrophages (corresponding to Ser894/898 in human) enables the binding of the E3-ligase TrCP1 in (Ubiquitination chapter), which in turns catalyzes the proteasomal degradation of NLRP3 t Lys380 . While tNLRP3 has been shown to be phosphorylated at Tyr861 by an unknown kinase, allowing its interaction with the autophagy machinery Sequestosome 1 (SQSTM1) to target to the phagophore, and thereby control inflammasome activation . ProteinThe C-terminal HIN-200 domain binds to dsDNA through electrostatic attraction between the positively charged HIN domain residues and the dsDNA sugar-phosphate backbone . AIM2 isThree phosphorylation sites in the HIN-200 domain of AIM2 have been identified : Thr254,The CARD domain, through protein-protein interactions, enables the formation of inflammasome complexes and unsurprisingly, protein phosphorylation has been identified as a mechanism controlling this process. Two phosphorylated tyrosine residues (Tyr137 and Tyr146) in ASC have been functionally characterized. When Mambwe and colleagues established that the pharmacological PTPase inhibitor PAO had the profound inhibitory effect on the AIM2 and NLRP3 inflammasomes respectively, in human and murine macrophages . They idVery little is known how Caspase activity is regulated by CARD domain phosphorylation despite the identification of several putative phosphorylation sites . One couCaspase-1 and Caspase-4 are directly inflammasome-associated proteases in human cells, whereas Caspase-11, is the Caspase-4 orthologue in mice. All three caspases have in common that the proteolytic cleavage of their respective substrates is catalyzed by their peptidase domains. Several phosphorylation sites have been found in this domain , but so Helicobacter pylori LPS treatment of THP-1 cells (Caspase-1 is phosphorylated by the protein kinase p21-activated kinase 1 (PAK1) at Ser376 upon -1 cells . The LPSMany phosphorylation sites are situated in between the distinct protein domains of NLRs see . The NLRin vitro and in vivo inflammasome activation. These sites are Tyr136, Tyr140, Tyr143, and Tyr164, that are situated in the polybasic linker between PYD and NACHT domains, which is critical for interaction with negatively charged phosphatidylinositol phosphates (PIPs) at organelles, such as the Golgi. Ablation of BTK kinase activity or mutation of the respective tyrosine residues decreased the formation of cytosolic NLRP3 oligomers, complexes with ASC and IL-1β release (BTK kinase activity inhibits PP2A function and thereby lead to inhibition of NLRP3 de-phosphorylation of Ser5 of NLRP3 in PYD domain preventing its oligomerization . Recentl release . This st release . The BTK release . The conPhosphorylation of mouse NLRP3 at Ser198 (mouse Ser194) is catalyzed by c-Jun N-terminal kinase 1 (JNK1) in response to chronic LPS treatment or other TLR ligands , in a trEscherichia coli infection model system, PAK1 phosphorylates Thr659, which in turn facilitates NLRP3-NEK7 interactions, inflammasome formation and IL-1β cytokine maturation. PAK1 appears to be key regulator in the anti-bacterial host-responses by phosphorylating Thr659 on NLRP3 and Ser376 of Caspase-1.As discussed for Ser803 phosphorylation (LRR domain), the interaction between NEK7 and NLRP3 is crucial for its inflammasome activation, the NLRP3-NEK7 binding induces NLRP3 deubiquitylation by BRCC3 and subsequently inflammasome assembly, with NLRP3 phosphomimetic mutants showing enhanced ubiquitination and degradation than wildtype NLRP3 . While SSalmonella typhimurium, indicating that S533 phosphorylation is critical for NLRC4 inflammasome function. Conversely, phosphomimetic NLRC4 S533D caused rapid macrophage pyroptosis without infection. The phosphorylation site Ser533 is conserved among the NLRC4 proteins from different species, which suggests that it is potentially important for the structure or function of NLRC4. A screen of kinase inhibitors identified PRKCD (PKCδ) as a candidate kinase upstream of Ser533 phosphorylation . Both studies demonstrated that the phosphorylation at Ser533 had no to very subtle effect for NLRC4 inflammasome activation and the response to S. Tryphimurium infection. While Qu and colleagues observed a delayed, but not completely blocked response and a potential crosstalk with the NLRP3 inflammasome  upstream kinases and corresponding protein phosphatases2) redundancies between kinases for phosphorylation events3) temporal and spatial control of phosphorylation events4) functional characterization of identified phosphorylation sites, in particular beyond the NLRP3 inflammasome.Taken together, phosphorylation of inflammasome components occur at various stages of activation or inhibition of these multimeric protein complexes. From controlling the priming step , to inflammasome complex formation , the subcellular localization  and to protein stability . The validated residues that are phosphorylated in inflammasome components so far are represented in . Future From its discovery in the late 1970’s and early 1980’s by Aaron Ciechanover, Avram Hershko, and Irwin A, the ubiquitin system has gained importance, and this PTM is currently investigated intensely, and it is crucial in regulating several biological processes . In receThe importance of ubiquitylation in the regulation of the inflammasome activation has been well recognized, and several comprehensive reviews on this topic have been published , but sinShigella flexneri ubiquitin ligase secreted effector, can induce NLRP1b activation and degradation by ubiquitylation  inhibits NLRP3 activation. The authors demonstrated that TRIM65 binds to the NACHT domain of NLRP3, promotes Ly48 and Lys63 ubiquitination of NLRP3 to prevent NEK7-NLRP3 interaction, and therefore inhibits NLRP3 inflammasome activation . Other mof NLRP3 , and thevia proteasome. Indirectly, in this work, they also demonstrated that another E3-ligase FBXO3-mediated ubiquitination and degradation of SCF-FBXL2, which increases the NLRP3 levels and activity , a RING-finger E3 ubiquitin ligase, inhibits NLRP3 activation, is intricate. Cbl-b is recruited via its interaction with Lys63 chains that are synthesized by RNF125 E3-ligase that are attached to the NLRP3 protein. Cbl-b then mediates Lys48 poly-ubiquitylation of NLRP3 at Lys496 in the NBD domain and induces its proteasomal degradation . It is lFew E3-ligases have been linked to activation NLRP3; amongst them, Pellino2 was described to induce Lys63 ubiquitylation of NLRP3 following chronic LPS stimulation. The ubiquitylation activity of Pellino2 occurs through FHA and RING-like domains where Pellino2 promotes the ubiquitination of NLRP3. In this paper the authors postulated that there is counter regulatory relationship between Pellino2 and IL-1 receptor associated kinase 1(IRAK1), which explains the negative regulatory function of IRAK1 in NLRP3 inflammasome activation . In contThe NLRP6 inflammasome is crucial in regulating inflammation and host defense against microorganisms in the intestine. In this context, it has been described that Cyld is able to de-ubiquitylates NLRP6 in a Lys63 poly-ubiquitin chain dependent manner and this has an impact on NLRP6-ASC complex formation and production and maturation of IL-18. .The function of NLRP7 is less understood than other NLRs. It has been linked to innate immune signaling, but its precise function is still controversial. Only 3 potential lysines have been identified by massive screening efforts, that potentially could be ubiquitylated in NLRP7 although none of these sites have been validated  Table 2Table 2. NLRP12 plays an essential role as a potent suppressor of inflammation. NLRP12 has been linked to the E3-ubiquitin ligase TRIM25, this TRIM25 ubiquitination by poly- ubiquitin Lys63 chains, and the activation of RIG-I. NLRP12 is also responsible for the Lys48 poly ubiquitylation of RIG-I through the E3-ligase RNF125 .NLRP14 has been described to have a role in spermatogenesis and inflammation . NLRP14 The BH3 domain of the E3 ubiquitin ligase HUWE1 was found to interact with NLRC4 through the NACHT domain. HUWE1 mediated the Lys27-linked polyubiquitination of NLRC4 which activates this inflammasome .Recently, it has been demonstrated that HERC2 another E3-ligase is able to interact with NLRC4 and mediates its ubiquitylation and this is possibly regulated by the Long non-coding RNA (LncRNA-Fendrr) .NLRP2 has been attributed in the literature to different roles, including an interaction with TBK1, which negatively impacts antiviral immunity . DespiteNLRP5 has a role in oocyte implantation  and femaIn 2011 Moreover, Lys39 and Lys235 were identified as a new ubiquitylation sites in a mass spectrometry screening (PhosphositePlus). The identification of E3-ligase(s), which mediates ubiquitylation of Lys39 and Lys235 could shed light on the AIM2 inflammasome regulation .Finally, the E3-ligase HUWE1 has been described as an interactor of AIM2. The BH3 domain of HUWE1 was important for its interaction with the HIN domain of AIM2. This interaction leads to the Lys27-linked polyubiquitination of AIM2 leading to the assembly of this inflammasome .ASC is post-translationally modified by phosphorylation and ubiquitylation, although for the latter fewer information are available. On the other hand, USP50 has been described as a DUB able to deubiquitylate Lys-63 ubiquitin chains from ASC . Apart oDespite its tremendous importance in the inflammasome complex no ubiquitin PTMs have been demonstrated on Caspase-1 protein. On the PhosphositePlus database several lysines in the Caspase-1 sequence have been postulated to be ubiquitylated, including Lys37, Lys44, Lys53, Lys134, Lys148, Lys158, Lys204, Lys268, Lys274, Lys278, Lys319, Lys320, and Lys325 see . All of Reported as an inflammatory caspase, , the onlvia proteasome  to active GSDMD amino (N)-terminus to bind to the cell membrane to induce a highly inflammatory form of programmed cell death called pyroptosis . PhosphoShigella ubiquitin ligase IpaH7.8 is able to ubiquitylate GSDMD and promotes its degradation via proteasome in humans but not in rodents . Their data showed reduced GSDMD Lys48 and Lys63 polyubiquitylation inhibiting its degradation diabetes .1) upstream E3-ligases and corresponding DUBs2) temporal and spatial control of ubiquitylation events.3) functional characterization of identified ubiquitylation sites, in particular beyond the NLRP3 inflammasome.The validated residues that are ubiquitylated in inflammasome components so far are represented in . Future Little is known about the ubiquitylation process regarding inflammasome components, even less data is available for SUMOylation. SUMOylation is similar to the ubiquitin cascade with 3 coupled enzymes E1, E2, and E3 SUMOylation enzymes where a small ubiquitin-related modifier (SUMO) protein is transferred to the substrate . The majvia proteasome.In particular, NLRP3 is SUMOylated by the SUMO E3-ligase MAPL which impairs its activation . In the The only validated residue that is SUMOylated in NLRP3 so far are represented in . Future α-tubulin due to sirtuin 2 (SIRT2) deacetylase inactivation was reported (in vitro and in vivo. SIRT3 interacts with and deacetylates NLRC4 at Lys71 or Lys272 to promote its activation (Lysine acetylation is a PTM mediated by lysine acetyltransferases (KATs). This is a reversible process catalysed by lysine deacetylases (KDACs)  Figure . Inflammreported , but acereported . A recenreported . SIRT3 dtivation . HoweverMycoplasma pneumonia-derived toxin and designated community-acquired respiratory distress syndrome (CARDS) toxin, which interact with and activates NLRP3 inflammasome, dependent on its ADP-ribosyltransferase activity. Upon stimulation by these toxins, NLRP3 was found to be ADP-ribosylated, suggesting that ADP-ribosylation of NLRP3 is a functional PTM for its positive regulation, but further investigation is required to understand the molecular basis of inflammasome activation  group to a thiol (S-H) group of a protein  Figure . EndogenPrenylation is a post-translational modification involving transfer of farnesyl or geranylgeranyl lipid groups on characteristic C-terminal CAAX motifs of proteins . PrenylaCitrullination of proteins is a process in which arginine residue of a protein is converted to citrulline by deamination, which is catalyzed by peptidyl arginine deiminases (PADs) . This prSince the discovery of the NLRP3 inflammasome by the team of Jürg Tschopp at the University of Lausanne in 2002 , much prin vitro and in vivo experimentation, with multiple diseases   .A well-established causal relationship between gain of function mutations of NLRP3, resulting in excessive inflammasome activation with subsequent overproduction of IL-1β, and disease can be found for autoinflammatory conditions called cryopyrin-associated periodic syndromes (CAPS) . CAPS beVery little is known how gain- or loss-of function mutations affect PTMs of inflammasome components. A few examples how mutations identified in CAPS patients’ crosstalk with PTMs of NLRP3 have been published:PKA phosphorylation of NLRP3 Ser295 inhibited the NLRP3 ATPase activity, which is required for assembly of NLRP3-ASC complexes, resulting in NLRP3 inflammasome inhibition . A clustThe Tyr861Cys mutation in NLRP3 has been reported in patients with CINCA. Phosphorylation of this site has been identified as negative regulator of NLRP3 . MechaniThe INFEVERS (INternet periodic FEVERS) database is a registry of hereditary auto-inflammatory disorders mutations . Mutatioin vitro and in vivo.While end products of inflammasomes such as circulating ASC specks and mediators such as IL-1β and IL-18 are strong signals of inflammasome activity, they don’t provide any mechanistic insights where and which of the many NLRs are activated. Furthermore, antibody-based methods used to detect IL-1β and IL-18 might not be able to distinguish between the cleaved and fully active proteins and their uncleaved, less active precursors. Therefore, inflammasome activity might not correlate well with the expression levels of these two mediators Post-translational modifications can regulate all aspects of inflammasome biology, from activation and inhibition, subcellular localization, their interactome and function. Monitoring PTM status could allow an assessment of inflammasomes activation status and of the upstream catalyzing enzymes. In the future, a well validated PTM has the potential to enable a precision medicine approach to identify a specific patient population that would benefit from selective inhibition of one respective inflammasome, if a pan inflammasome inhibition is not desirable. Likewise, a well characterized PTM might be an option for a target engagement biomarker for an inflammasome inhibitor.PTMs have the potential to be used to control inflammasome activation in a specific manner. For instance, modulation of protein ubiquitylation could modulate protein stability, whereas some phosphorylation sites have been implicated in the subcellular localization .An intriguing, yet unproven application of well characterized inflammasome PTMs could be as diagnostic tools. Because CAPS is an extremely rare disease and patients show a heterogeneous multi-system clinical presentation, a significant delay exists between symptom onset and definitive diagnosis . ScreeniAll PTMs are catalyzed by upstream enzymes, which could be drug targets by their own rights due to involvement in diseases not driven by excessive inflammasome activity. For some of these enzymes, a direct measurement of their activity, either in pre-clinical drug development or as target engagement or patient identification biomarker might prove to be too challenging. If a well characterized inflammasome PTM is indeed robustly predictive of upstream enzyme activity, this PTM could be tested as a more accessible, surrogate biomarker for the upstream drug target.• Development of PTM detection tools (for example PTM site-specific antibodies) that provide sufficient sensitivity and robustness to enable pre-clinical research• Establish a deep understanding of temporal and spatial expression of PTMs• Functional characterization of PTMs, ideally utilizing CRISPR/Cas9 gene editing to generate knock-in models• Systematic exploration of upstream enzymes controlling PTMs, for example through validated small molecule inhibitor libraries, siRNA libraries or CRISPR/Cas9 genome editing approaches• Translational of PTMs across species and in appropriate human biological samples from patients.The identification of PTMs through proteomics approaches led to the discovery of a plethora of modified sites within inflammasome sensors, adaptor protein ASC and the effector caspases in recent years. Lagging behind is the functional characterization, in particular the contribution of individual PTMs and concerted action of multiple PTMs for inflammasomes. To further build on past discoveries and improve our knowledge in the future we propose:


Arctic sea ice has decreased substantially and is projected to reach a seasonally ice-free state in the coming decades. Little is known about whether dwindling Arctic sea ice is capable of influencing the occurrence of strong El Niño, a prominent mode of climate variability with global impacts. Based on time slice coupled model experiments, here we show that no significant change in the occurrence of strong El Niño is found in response to moderate Arctic sea-ice loss that is consistent with satellite observations to date. However, as the ice loss continues and the Arctic becomes seasonally ice-free, the frequency of strong El Niño events increases by more than one third, as defined by gradient-based indices that remove mean tropical Pacific warming induced by the seasonally ice-free Arctic. By comparing our time slice experiments with greenhouse warming experiments, we conclude that at least 37–48% of the increase of strong El Niño near the end of the 21st century is associated specifically with Arctic sea-ice loss. Further separation of Arctic sea-ice loss and greenhouse gas forcing only experiments implies that the seasonally ice-free Arctic might play a key role in driving significantly more frequent strong El Niño events. With an ice-free Arctic, strong El Niño frequency increases 1/3 + . Almost half the increase by 2100 links to Arctic ice loss, not other greenhouse forcing. The Arctic’s influence on ENSO may represent a novel driver of changing climate extremes globally. This alters exchanges of heat and moisture between the ocean and the atmosphere and contributes to Arctic amplification4. Most current climate models project a seasonally ice-free Arctic before the mid-century under scenarios with future cumulative emissions of ~270 GtC beyond the emissions through 20198. A recent modeling effort found a complete loss of summer Arctic sea ice under forcings consistent with the last interglacial9. The rapid decline of Arctic sea ice is an integral part of the Arctic response to natural variability and greenhouse gas forcing. A large body of evidence in observations and model simulations has shown that Arctic sea-ice variability is strongly influenced by the El Niño-Southern Oscillation (ENSO) via teleconnections, i.e., the Rossby wave train initiated through tropical convection, the shift of jet streams in response to tropical sea-surface temperature (SST) anomalies, changes in meridional and zonal circulations and associated heat transports, and anomalous transient eddy activity11. However, it is unclear how much Arctic sea-ice loss can influence El Niño characteristics and whether such an influence might depend on the magnitude and pattern of Arctic sea- ice loss, though a few recent studies have indicated that the effect of decreasing Arctic sea ice might reach deep into the tropics14.Arctic sea-ice cover has decreased in all months since the early 1950s and by nearly half during summer18. That type of model experiment neglects potential feedbacks from interactions of ocean dynamics with the atmosphere induced by Arctic sea-ice change. Recently, coupled climate models have been used to investigate the impacts of Arctic sea-ice loss, but the ice cover is altered indirectly by imposing a “ghost flux” on the Arctic energy balance . Such an indirect approach is based on the assumption that change in Arctic sea ice cover has an approximately linear relationship with additional downward longwave radiative flux, which is clearly deficient, especially in summer. This makes it difficult to isolate and directly assess the role of Arctic sea-ice loss specifically within a coupled model framework. Here we conduct coupled model experiments by directly altering sea- ice cover in Community Earth System Model version 1.2 (CESM1.2) to investigate whether different amounts of Arctic sea-ice loss have detectable impacts on the occurrence of El Niño events (see Methods for details). In brief, our reference simulation (hereafter ICEhist) is constrained by the climatology of Arctic sea ice during 1980–1999, as a representation of the observed sea-ice state in the late 20th century. Two sensitivity simulations are otherwise identical, except they are constrained with Arctic sea-ice climatology during 2020–2039 (hereafter ICEp1) and 2080–2099  based on the large ensemble RCP8.5 ‘high emissions’ projections. Consistent with rapid recent sea-ice loss, the seasonal evolution of the ice extent in ICEp1 is close to the observations averaged during 2007–2020, which are the lowest 14 records in the satellite era , particularly on the south side of the equator  using a nonparametric bootstrap statistical test (see “Methods” for details). We first use the widely used Oceanic Niño Index (ONI) to track El Niño. Compared to ICEhist, ICEp1 does not have a significant change in the occurrence of strong El Niño events. By contrast, the seasonally ice-free condition of ICEp2 yields a ~50% increase in strong El Niño, due to a spike in extremely strong El Niño events . Consistently, ICEp1 has little effect on the frequency of strong El Niño events  analysis on the simulated SST in the tropical Pacific, as an alternate way of removing the mean tropical Pacific SST warming, while cognizant that El Niño occurs across a range of spatial scales and can be described by more than one EOF mode. The first EOF mode of ICEhist, ICEp1, and ICEp2 shows an ENSO-like pattern, though the center of action is more towards the central-to-eastern equatorial Pacific compared to the classic ENSO pattern , which has numerous differences in physics as well as different climate sensitivity compared to CESM1.2 (see “Methods” for details). The results of the CCSM4 experiments showed that for the ONI index, the seasonally ice-free Arctic yields a ~80% increase in the occurrence of strong El Niño . Consistent with ICEhist and ICEp2, the reference and sensitivity experiments of CAM5 are forced with a specified seasonal cycle of Arctic sea- ice cover during 1980–1999 and 2080–2099, respectively . Compared to ICEp2, ICEp2_CAM5 produces much weaker SLP response in mid- and high-latitudes in winter . Moreover, the significant SLP anomalies in spring, summer and autumn in ICEp2 are almost entirely absent in ICEp2_CAM5 Fig. . Another21. Their result demonstrates a SST warming with maxima in the eastern equatorial Pacific in the full ocean model configuration, which is completely absent in the slab-ocean model configuration. These experiments together highlight the key role of ocean feedbacks in persistent/lagged atmospheric response to Arctic sea-ice changes.A recent coupled model study examined the response of the mean tropical Pacific SST to Arctic sea-ice loss in two different ocean configurations (full ocean model vs. slab-ocean model)30. ICEp2 does result in a significant positive change in the interhemispheric gradient in the Pacific (~0.37 °C), favoring enhanced zonal SST gradient in the tropical Pacific.Some studies have suggested that the mean tropical Pacific climate is sensitive to the change of the thermal gradient between the two hemispheres and the equatorial zonal SST gradient increases with an enhanced northward interhemispheric thermal gradient11, further feeding back on mid- and high-latitudes and reinforcing the Arctic atmospheric response , especially in winter. Here, we calculate the response of the eddy geopotential height and associated wave activity flux at 200 hPa. It appears that a Rossby wave propagates from the tropical/subtropical Pacific to the mid- and high-latitude Pacific, which is connected to the deepening of the Aleutian Low ; subsequently, heat is recharged in the tropical Pacific. Oceanic heat transport (OHT) is key to the recharge and discharge, which thus modulates the level of El Niño activity. However, the effect the decreasing Arctic sea ice on the Pacific OHT in the Pacific is largely unstudied. The poleward OHT in the tropical and subtropical north and south Pacific is greatly reduced in ICEp2 , 2020–2039 (RCP85p1), and 2080–2099 (RCP85p2), respectively, using the RCP8.5 CESM large ensemble experiments, and compare these results to our time-slice experiments. The CESM large ensemble simulations use the same model employed in our time-slice model simulations and these three periods are consistent with the three time slices we defined based on sea-ice changes. Not surprisingly, relative to HIST, there is a huge increase in strong El Niño events as defined by ENSO indices based on actual SSTs  in RCP85p2, in conjunction with the strong mean global warming in the CESM large ensemble experiments , so that ENSO variability is only influenced by increased atmospheric CO2 forcing. The ONI index in ICE1%CO2, as expected, produces a large increase in the occurrence of extremely strong El Niño  can explain more than two third of the frequency change of strong El Niño in RCP85p2  and central Pacific (CP), respectively–produce different global impacts. The CP El Niño has been suggested to be related to extratropical atmospheric forcing37. The seasonally ice-free Arctic also induces changes in ENSO diversity, in favor of CP El Niño events and ENSO events of mixed type with SST anomalies spanning both CP and EP regions. Mixed-type events are an example of a vexing compound extreme event, a class of extreme events that are gaining increasing attention among scientists and decision-makers38 in part due to their potential for novel behavior and societal impacts. Should mixed-type events become more common and more extreme, they could lead to large, yet poorly understood, teleconnections and impacts. Our results indicate that change in strong El Niño events may depend on the magnitude and pattern of Arctic sea-ice loss. Although the findings presented here are supported by two different coupled models (CESM1.2 and CCSM4), coordinated experiments, including those that utilize different coupled climate models, different sea-ice constraints, and different model configurations are needed to further quantify relationships between El Niño, Arctic sea-ice loss, and other aspects of climate change. Research is also needed to determine the extent to which changes in mid-latitude climate, including variability and extremes, could be linked to changes in ENSO that are themselves partially driven by Arctic sea-ice loss. It is becoming clearer though that climate models need to simulate decreasing Arctic sea ice realistically in order to correctly simulate ENSO variability.In summary, a seasonally ice-free Arctic induces marked changes in the tropical Pacific with an El Niño-like warming pattern. Strong El Niño events become more frequent, presumably with continued devastating impacts around the globe39. For the atmospheric component, CAM5 is employed, which includes many improvements in the representation of atmospheric processes  as compared to CAM4 employed in the previous Community Climate System Model (CCSM) to investigate the impacts of Arctic sea-ice loss. For the ocean and sea-ice components, POP2 and CICE5 are used, and CICE5 includes a range of improved sea-ice physics as compared to CICE4 used in the previous CCSM. The CESM1.2 is run at a spatial resolution of 1.9° × 2.5° for atmosphere and land models and ~1° for ocean and sea-ice models. Detailed information of each model components and their coupling can be found at http://www.cesm.ucar.edu/models/cesm1.2. Previous studies showed that the CESM1.2 can simulate the mean state of the tropics and Arctic reasonably well41.To investigate the impacts of different amounts of Arctic sea-ice loss on El Niño events, we perform numerical experiments utilizing CESM1.2, a fully coupled atmosphere, land, ocean, and sea-ice model40, including 40 ensemble members from different initial conditions. The CESM large ensemble mean during 1979–2005 from the historical simulation shows that sea ice has decreasing trends for almost the entire Arctic with pronounced trends in an arc around the periphery of the central Arctic Basin during 1979–2005, which is in good agreement with observations. In the reference experiment (ICEhist), the climatological Arctic sea-ice cover is specified based on the ensemble mean of historical simulations during 1980–1999. In the two sensitivity experiments, Arctic sea-ice cover is specified using the ensemble mean of the RCP8.5 projection simulations during 2020–2039 (ICEp1) and 2080–2099 (ICEp2), respectively. Our model setting allows ocean-atmosphere interactions and ocean dynamics outside the region with prescribed Arctic sea-ice cover as well as ocean dynamics below prescribed Arctic sea-ice cover, but neglects the potential feedbacks from interactions with interactive Arctic sea ice. Sea-ice cover in the Southern Hemisphere is computed by the sea-ice model component of the CESM1.2 model.We conduct three main experiments using CESM1.2. The fixed seasonal cycle of Arctic sea-ice concentration is employed in the sea-ice model component of CESM1.2, generated from the average of historical and projection simulations of the CESM large ensemble projectCompared to ICEhist, Arctic sea-ice extent in both ICEp1 and ICEp2 shows a year-round reduction, peaking in magnitude at the end of the melting season , CCSM4 (different fully coupled model), and CAM5 .2 experiment, we fix Arctic sea-ice cover using the ensemble mean of historical simulations during 1980–1999, but allowed a 1% per-year increase in atmospheric CO2 for 100 years starting from the level of the year 2000. Thus the ENSO variability is only influenced by increased greenhouse gas forcing in this experiment. This experiment is run for 100 years.(1) In the ICEp2NP experiment, we only fix the sea-ice cover in the North Pacific sector using the ensemble mean of the RCP8.5 projection simulation during 2080–2099. Outside the North Pacific sector, sea ice is allowed to evolve dynamically and thermodynamically, as computed by the sea-ice model in CESM1.2. This experiment is run for 450 years. (2) In the ICE1%CO43. Each experiment is run for 450 years with the same spatial resolution as that of CESM1.2.We repeat the ICEhist and ICEp2 experiments discussed above using a different coupled model—CCSM4. There are numerous differences in physic packages between CCSM4 and CESM1.2. Using the atmospheric model component as an example, CAM5 of CESM1.2 has a range of enhancements and improvements in the representation of physical processes compared to CAM4 of CCSM4 , such as a new moist turbulence scheme, shallow convection scheme, and 3-mode modal aerosol scheme, improved cloud macro and microphysical scheme, and radiation scheme42. ICEhist_CAM5 (ICEp2_CAM5) is forced with the prescribed Arctic sea-ice cover using the ensemble mean of the historical simulation (RCP8.5 projection) during 1980–1999 (2080–2099). Each experiment is run for 150 years with a horizontal resolution of 1.9° × 2.5°.We conduct two numerical experiments, named ICEhist_CAM5 and ICEp2_CAM5, using a stand-alone atmospheric model—CAM540 are used to assess to what extent the projected increase in strong El Niño in response to increasing greenhouse gases can be attributed to Arctic sea-ice loss by comparing the frequency change of strong El Niño events between our time-slice experiments as described above and the CESM large ensemble experiments.A 40-member ensemble of the CESM simulation for the historical simulation and the projection under the RCP8.5 emission scenario44 is employed for statistical significance testing of strong El Nino events. Using the index based on the zonal SST gradient as an example, first, we extract samples from time series of the zonal SST gradient calculated from ICEhist using random sampling to generate two new time series. Each time series has 300 samples with different numbers of strong El Niño events. We then find the difference in the number of strong El Niño events between the two new time series. Secondly, we repeat this bootstrap resampling ten thousand times and obtain ten thousand differences in the number of strong El Niño events. We then generate the PDF of ten thousand differences and used the value of the 95th percentile as the criteria. Thirdly, if the number of strong El Niño events based on the zonal SST gradient between the sensitivity experiment  and ICEhist is greater than the above criteria, we conclude that the change in strong El Niño occurrence is significant at the 95% confidence level.A nonparametric bootstrap statistical testWe define two indices to characterize El Niño, in order to facilitate comparison of simulated El Niño events in different steady climate states. The first index is calculated as the SST difference between the Niño3.4 region  and the Maritime Continent region  where fingerprints of oceanic and atmospheric anomalies have the strongest ENSO-related variability. A comparison of this index  and the NOAA Climate Prediction Center’s operational Oceanic Niño Index show very good agreement in interannual variability associated with ENSO. The second index is calculated as the SST difference between 2.5°S–2.5°N, 150°W–90°W and 5°N–10°N, 150°W–90°W. A positive index used here physically means that the SST gradient has reversed, thereby favoring tropical convection over the central and eastern Pacific.Supplementary InformationPeer Review File


In pelvic reconstruction surgery, the hemipelvic prosthesis can cause significant changes in stress distribution due to its high stiffness, and its solid structure is not suitable for osseointegration. The purpose of this study was to identify a novel bone mineral density screw channel and design the structure of the prosthesis so as to improve the distribution of stress, promote bone growth, and enhance the biomechanical properties of the prosthesis. The mechanical characteristics of bone mineral density screw and traditional screw were compared by finite element analysis method, and redesigned by topology optimization. The direction of the newly proposed screw channel was the posterolateral entrance of the auricular surface, ending at the contralateral sacral cape. Compared to the original group, the maximum stress of the optimized prosthesis was decreased by 24.39%, the maximum stress of the sacrum in the optimized group was decreased by 27.23%, and the average strain energy density of the sacrum in the optimized group was increased by 8.43%. On the surface of screw and connecting plate, the area with micromotion more than 28 μm is reduced by 12.17%. On the screw surface, the area with micromotion more than 28 μm is reduced by 22.9%. The newly determined screw channel and optimized prosthesis design can effectively improve the biomechanical properties of a prosthesis and the microenvironment of osseointegration. This method can provide a reference for the fixation of prostheses in clinical pelvic reconstruction. Primary pelvic sarcoma seriously impacts the survival and quality of life of patients. Limb-salvage surgery through prosthesis placement or biological reconstruction is more popular than traditional hemipelectomy . With thDuring sacral pelvic reconstruction and repair, a screw–rod system  and cancThe huge mismatch in mechanical properties between the sacrum and prosthesis materials can easily lead to a stress-shielding effect , and sigIn this study, we aimed to measure the sacral BMD and determine the position of the screw according to the BMD distribution, then conduct FEA to verify its mechanical stability. Combined with a TO design, a porous structure was added on the surface to obtain good stability, promote bone growth, and reduce complications.Two researchers collected computed tomography (CT) data from 40 patients, including 20 men and 20 women, with an average age of 67.23 ± 4.26 years . The CT data were imported into the medical image–processing software OsiriX Lite , which was used to measure the BMD in the sagittal plane of the patient’s sacrum. Seven layers of sacral sagittal plane were scanned for BMD measurement, including the BMD of the sagittal sections of the bilateral auricular surface, bilateral sacral ala midline, bilateral sacral canal edge, and sacral midline. A sectional diagram of the seven layers of the sacral sagittal plane and the measurement method were shown in The geometry of the sacral model was based on CT data of the pelvis of a female volunteer. Pelvic CT data were acquired using the iCT 256 scanner with an X-ray tube  with the following parameters: current, 232 mA; voltage, 120 kV; pixel size, 0.52 mm; section thickness, 0.9 mm; and spacing between sections, 0.45 mm. The CT data were imported into the Mimics version 21.0 software program , and the sacrum was reconstructed into a 3D model. The 3D model of the sacrum was then imported into Magics version 24.0 , and its left auricular surface was intercepted. On this basis, three S1 screw channels  were designed, and fixed with a long screw at the S2 level. BMD screw placement is a way to implant screws near a high BMD area. A hemipelvic prosthesis with acetabulum was designed and reconstructed using Magics version 24.0. The hemipelvic prosthesis was mainly composed of three parts: a connecting plate in contact with the auricular surface, an acetabular cup, and the connecting device present between the connecting plate and the acetabular cup. The screws were tightly connected with the prosthesis through the connecting plate and fixed on the auricular surface. These screws were simplified to cylinders for later application of FEA. The design of the three parts of the prosthesis and screws is shown in We imported the 3D model of the prosthesis into the HyperMesh 2020 software program . The triangle-based surface mesh in the prosthesis model is set to 1 mm using self-animation mesh technology. After generating the two-dimensional mesh, it is transformed into a 4-node linear tetrahedral element (C3D4). Using Mimics, a 3D sacral model with inhomogeneous material properties was defined according to the gray value of the CT scan. The material properties of inhomogeneous sacrum are shown in t to 0.3 . The matt to 0.3 . The matThe load conditions of the static analysis carried out in this study were the same as those used by . A load According to the stress distribution of the prosthesis, the BMD screw and connecting plate were topologically optimized. TO of the connecting plate and screws was performed in HyperMesh 2020. Referring to previous literature , the minObjective function: to minimize (Uc).i < 1 c is the compliance, ηi represents the internal pseudodensity assigned to each finite element (i) in the optimization equation, V is the computed volume, V0 is the original volume, V* represents the amount of volume to be removed, Vi is the volume of element i, Ei is the elasticity tensor for each element, E represents the elasticity tensor, σi is the stress vector of element i, and εi represents the strain vector of element. η, as the density index, ranged from 0 to 1. An η value close to 0 indicates the material to be removed, and an η value close to 1 indicates the material to be retained. The program was set to reduce the volume by up to 50% and iterate 30 times at most.Constraint: 0 < ηNext, we input the results of TO into Magics version 21.0. The parts to be removed were obtained by performing Boolean operations between the intact model and the optimized parts obtained by TO. In this study, the newly designed prosthesis retained the same shape as the original prosthesis. The removed parts and optimized parts were designed to have a high-strength body-centered cubic structure. The removable part was designed with an optimal porosity of 70% and an aperture of 200 μm to allow for early and extensive bone ingrowth . The porThe FEA results were processed using HyperView 2020. Von Mises stress values of the sacrum and prosthesis were recorded. The prosthesis consists of screws, a connecting plate, and the hemipelvic prosthesis. The strain energy density (SED) can be used as an index of shielding stress . Insertit-test was used to statistically analyze the data of topology optimized prosthesis. Statistical significance was set as p < 0.05.Using the SPSS software , the BMD data were statistically analyzed by one-way ANOVA. Paired p < 0.01). In the middle of the left sacral ala, the first quadrant had the highest BMD, with an average value of 103.90 ± 39.04 HU (p < 0.01). In the left margin of the sacral canal, the BMD in the third quadrant was the highest, and the average value was 264.51 ± 88.75 HU (p < 0.01). In the middle sacral line, the BMD in the second quadrant was the highest, with an average value of 240.81 ± 65.30 HU (p < 0.01). In the right margin of sacral canal, the BMD in the third quadrant was the highest, and the average value was 247.19 ± 67.47 HU (p < 0.01). In the middle of the right sacral ala, the first quadrant had the highest BMD, with an average value of 107.02 ± 39.09 HU (p < 0.01). Finally, at the junction of the right auricular surface and the dorsal side, the BMD in the fourth quadrant was the highest, with an average value of 187.21 ± 52.91 HU (p < 0.01).In the measurement, it was found that the BMD distribution on both sides of the sacrum was symmetrical. The mean and standard deviation values of BMD were determined in Hounsfield units . The meaThe stress distribution of the prosthesis varies according to the three different fixation methods. As shown in The TO program was iterated six times. The algorithm mainly removed the middle two portions of the connecting plate; about 84% of the connecting plate was retained. As shown in p < 0.05).As shown in The SEDs of the sacrum in the original group and the optimization group are shown in 2, and there is no area where the micromotion is more than 40 μm. In long screw, the area of the screw surface with micromotion more than 28 μm is 441.26 mm2, and the area of the screw surface with micromotion more than 40 μm is 66.59 mm2. In short screw, the area of the screw surface with a micromotion more than 28 μm is 180.93 mm2, and the area of the screw surface with a micromotion more than 40 μm is 17.54 mm2. The maximum micromotion of the three screws is at the top of the connecting plate. The maximum micromotion of BMD screw is 229 μm, the maximum micromotion of long screw is 191 μm, and the maximum micromotion of short screw is 173 μm.The micromotion distribution of prosthesis varies with three different fixation methods. As shown in 2 to 601.13 mm2, a decrease of 12.17%. On the screw surface, area with micromotion more than 28 μm in the optimization group was reduced from 368.85 to 284.38 mm2, a decrease of 22.9%. However, the maximum micromotion increased from 229 to 252 μm. p < 0.05), and the micromotion on the connecting plate was increased compared with that of the original group.As shown in As one of the most commonly used options for pelvic ring reconstruction, a hemipelvic prosthesis is usually a solid structure with high stiffness, which leads to the non-physiological distribution of stress, and is not suitable for bone integration . The mosin vitro mechanical experiments. Their study showed that the BMD screw can significantly improve the mechanical distribution on the screw, reduce the peak stress on the screw, and decrease the incidence of postoperative complications, consistent with our expected results. Compared to the other two known fixation methods, the area of high-stress concentration at the connection between the screw and the connecting plate can also be eliminated. The position of the BMD screw is similar to the mechanical conduction path of the pelvis (In order to reduce the stress-shielding effect caused by the mismatch between vertebral cancellous BMD and screw stiffness, this study divided the sacral sagittal plane into several regions and measured the average BMD of each so as to obtain a screw channel with high BMD distribution. The measurement results of sacral BMD clearly showed the distribution pattern of sacral BMD. The BMD in the sacral vertebral body was higher than that in other areas, and the anterior and upper sides of vertebral body was the area with the most highly concentrated BMD. The BMD near the auricular surface was also high, and the posterolateral area was an area with concentrated BMD. Meanwhile, in the sacral wing, the BMD was low. Overall, there were significant differences in BMD distribution at the S1 level, consistent with the results reported by Darius et al. .Lu et ale pelvis , so it ce pelvis  and 4.In this study, an inhomogeneous sacral model was reconstructed to simulate the real sacrum. The material properties of the sacrum were determined according to the gray values of CT images. The inhomogeneous sacral model used in this study can improve the accuracy of FEA. Ahmet et al. previously explored the correlation between bone inhomogeneity and reported that inhomogeneous bones can lead to significant differences in stress values compared to rigid bones . We desiProsthesis micromotion is an important assessment related to prosthesis stability and postoperative pain. Previous studies indicated that 28 μm was considered to be the micromotion boundary value most conducive to bone growth, and the interface micromotion of about 40 μm could lead to partial inward growth, while 100–150 μm was considered to prevent inward bone growth, more than 150 μm would completely inhibit the inward growth of bone . As showSED is another index for evaluating stress shielding. The difference between the optimized group and the original group can be used to evaluate bone resorption. As shown in In this new study of prosthesis fixation, we preliminarily verified the initial stability of screw and prosthesis by finite element analysis, and obtained satisfactory results. However, there are some limitations in this study. First, we did not consider the effect of postoperative stress-dependent BMD change on prosthesis fixation. This study exists in our next plan. We will study this phenomenon through animal experiments and postoperative follow-up of patients. Second, we only considered the mechanical interaction between bone tissue and a prosthesis and did not introduce the supporting effect of muscle and ligament on bone or the prosthesis, although the FEA results support the effectiveness in reducing stress shielding. Such operation simplified the finite element model and may cause some deviation in the results. In the future research, we will use a more accurate and more physiological model. Finally, this study only includes a single hemipelvic pelvic model, which may have an impact on the applicability of the prosthesis. Future research will further improve upon the content of this study.In this study, we proposed an improved scheme to reduce the probability of stress shielding after pelvic reconstruction, determine a new screw channel, and optimize the structure and porous surface design of the prosthesis. We also used FEA to verify the biomechanical properties of the prosthesis. The maximum stress of the optimized prosthesis was 24.39% lower than that of the original prosthesis, the maximum stress of the sacrum in the optimization group decreased by 27.23%, and the average SED of the sacrum in the optimization group rose by 8.43%. On the surface of screw and connecting plate, the area with micromotion more than 28 μm was reduced by 12.17%. On the screw surface, the area with micromotion more than 28 μm was reduced by 22.9%. The newly determined screw channel and optimized prosthesis design can effectively improve the biomechanical properties of the relationship between a prosthesis and bone. Some limitations of this study, such as the impact of postoperative stress-dependent BMD change on the stability of prosthesis and the failure to use complete pelvis, muscle and ligament structures, may affect the accuracy of the experiment. These limitations have been included in our next experimental plan. This series of studies will provide reference for the stable fixation of prosthesis in clinical pelvic reconstruction.