Despite some progress in the reduction of the prevalence of child wasting in Ethiopia, the pace of progress has been slow. Despite millions of dollars being spent on the treatment of wasting every year, the increased frequency and magnitude of environmental and anthropogenic shocks has halted progress. This study aimed to present the trends of child wasting in Ethiopia and estimate the economic losses related to the slow progress towards meeting the sustainable development goal (SDG) targets. Weather shocks and civil unrest between 2015 and 2018 have halted progress. We used a “consequence model” to apply the coefficient risk–deficit on economic losses established in the global scientific literature to the Ethiopian health, demographic, and economic data to estimate economic losses related to child wasting. The impact of wasting on the national economy of Ethiopia is estimated to be 157.8–230.2 million United States dollars (USD), annually. The greatest contributor to the economic burden (43.5–63.5% of the burden depending on the discount rate) is the cost of supplies and human resources to treat wasting. To reach the 2030 SDGs, Ethiopia should increase its annual average reduction rate (AARR) in the numbers of child (<59 months) wasting from 0.1% to 5.4%. This will avert the wasting in 7.9 million cases and prevent additional economic costs of up to 803.7 million USD over the next decade. Increasing the reach of therapeutic interventions, but also identifying and implementing wasting prevention interventions, will be critical if the SDG targets are to be met and the opportunity of the children to thrive is not to be wasted. Both ponderal and linear growth faltering are major threats to child survival and development. Wasting, which reflects inadequate weight gain or weight loss, is measured as weight-for-height <−2 SD of the median weight-for-height in the WHO’s child growth standards. In 2019, 47 million children were wasted globally [Ethiopia, home to more than 16 million children under 5 years old, is one of the countries that have high levels of wasting. A time-series analyses of the various rounds of demographic health surveys (DHS) illustrated that some progress was made in reducing the prevalence of wasting: the prevalence was 12.2% in the 2000 DHS  and dropAchieving the World Health Assembly (WHA) nutrition targets can substantially reduce this national economic burden, as well as generate human and social capital to fuel economic development. Over the years, several countries  quantifies the rate of change of the prevalence from baseline to the current year. Using the different Ethiopian Demographic Health Surveys ,5,6,12, The assessment explored the economic losses associated with the wasting prevalence reported in the 2016 Ethiopian Demographic Health Survey  ,15, LaosMalnutrition is a distinct, measurable, and often significant contributing factor to child mortality, and is globally recognized as the underlying cause of up to 45% of all child deaths . EstimatThose relative risks used in the global scientific literature were tailored to the local Ethiopian context by using data available in the latest 2016 and 2019 Ethiopian Mini Demographic Health Survey to paint a general picture of child mortality, as done in previously published papers.Population attributable risk: The population attributable risk (PAR) is a function of the prevalence of the nutrition indicator along with the severity of the mortality risk, as expressed by the relative risk RR: see . It is cMortality in risk group affected: number of deaths per year based on national dataFor pathway #1, to estimate the number of deaths attributable to maternal and child malnutrition, the following algorithm (1) was used :Number Labor force participation: All: 91.2%, Male: 88.4%, and Female: 73.4% Average earnings: 790/year USD, based on World Bank estimatesNet present value: Net present value (NPV) is a subjective factor used to define the value of future goods or services and expresses that value in current currency. To calculate this NPV of lost future earnings due to the various indicators of malnutrition, we used a 3% discount rate, recommended by the World Bank for social investments . As a seThe estimated value of workforce lost to child mortality was then estimated ). This value was simply derived as a lost workforce, by taking a discounted net present value (NPV) of future lost earnings. The NPV includes a delayed earnings stream that presumes entry into the workforce at an average of 15 years of age. Furthermore, this economic perspective attributes an economic value only to the 81.2% of children  who woulChildren, even mildly or moderately undernourished, score poorly on tests of cognitive functions, psychomotor developments, and fine motor skills. With lower activity levels, they interact less frequently with their environment, and thus fail to acquire physical and intellectual skills at normal rates. These early childhood deficits determine to a large extent the ability to capitalize on educational opportunities, and later, employment opportunities, resulting in an adult productivity deficit.There is substantial evidence that after correction for poverty, nutrition has independent and additive impacts on child growth, cognition, and development . The genFor stunting, a 19.8% deficit  in earniThe cost of lost productive time of caregivers while in a treatment program was calculated based on their occupational status and the total time spent on caring for the child during treatment. Assuming, an average wage of 790 USD/year (2.16 USD/day), 7 days lost during severe acute malnutrition (SAM) hospitalization and a half of a day during follow-up visit (8 for MAM and 12 for SAM OTP), we have estimated that a family loses in current productivity: (i) 5.76 USD for MAM treatment, (ii) 8.64 USD for SAM OTP, and (iii) 23.76 USD for SAM SC. This data is comparable with an analysis done and published in 2012 . Conside10% of the SAM children will go through a stabilization center (UNICEF Ethiopia estimates)Cost, including supply, training, and monitoring: 40.27 USD/child for moderately wasted children (MAM), 80 USD/child for a severely wasted child (SAM) in out-patient treatment (OPT), 120 USD/child for a SAM in a stabilization center (SC) and then OPTCost of labor: to estimate the cost of the health extension worker (HEW) during the following visits, we have estimated that he works 196 h/month and receives a salary of 150 USD/month. Therefore, his cost per hour of work is equal to 0.76 USD. A similar calculation was done for a health worker (HW) who works 196 h/month and receives a salary of 198 USD/month. His cost per hour will be equal to 1.01 USD. If we consider that each contact with a wasted child is 20 min, a MAM child with eight visits will cost 1.8 USD, a SAM child (OTP) with 12 visits will each cost 2.7 USD, while a SAM child (SC+OTP) with 19 visits will costs 4.9 USD.Based on the number of children being treated in 2019 , we analyzed in algorithm (4) the cost due to treatment (manpower and supplies):The estimated averted SAM cases were calculated based on (5):Assumption: the number of cases was calculated using the total population of children × prevalence (estimated according to the AARR) × incidence of 2.6.For Pathway 1 , we have integrated in the yearly calculation a 1% increase of the number children under 5 years of age in Ethiopia and estimated the current burden over the year with the actual AARR for stunting and wasting versus the estimated AARR as described in formula (6):Assumption:1 AARR for wasting of 0.1% and 0.5% for stunting2 AARR for wasting of 0.9% to 5.4%  and the annual inflation, we have assumed that the annual wage in USD will not increase over time. In addition, we have kept constant all other variables, such as the labor force participation, and average work-life.child cognition deficit resulting in inferior school performance and adult productivity), as explained in pathway 1 for the variables of population, average wage, work-life, and labor participation, we followed the calculation for annual losses due to depressed future productivity, applied the 3% of concurrence of wasting and stunting and then follow the Equation (7):For pathway 2  and 4 , the saving was based on the yearly averted cases estimated using the previous cost of treatment, care, and loss of labor time over the 10 years. The same estimates for cost were used than during the modeling for 2019. As explained previously, we have assumed that the cost of health treatment and wages in USD will not increase over time.For pathways 3  of the number of children (under 5 years of age) with wasting is currently slow and needs to be increased significantly to meet the SDG target of <5% prevalence wasting by 2030. The AARR would need to increase from 0.1%to 5.4% see . ReachinWith current (2019) levels of wasted children being treated , the impact of wasting on the Ethiopian economy is estimated to be as high as 225.5 million USD/year. Moving from an AARR of 0.10% to the required 5.4%, in order to reach the 2030 SDG wasting target will demand coordination between agencies and multisectoral collaboration. Over the coming 10 years, depending on the level of the implementation of the National Food and Nutrition Policy and strategy of Ethiopia, several scenarios  could emThe present study aimed to estimate the economic and productivity losses associated with child wasting in Ethiopia. The economic burden of wasting, if the prevalence remains around 10%, is estimated to be between 152.1 million and 225.5 million USD. The highest economic burden is related to the cost of supplies and human resources to treat wasting, followed by the economic burden associated with childhood mortality related losses of the workforce. Substantial increases in AARR are required to meet the SDG wasting prevalence target of <5% by 2030. To this end, increasing the reach for wasting treatments is essential, but preventing it from happening in the first place would be even more crucial. Unfortunately, in Ethiopia and elsewhere, the entire suite of interventions focuses on the treatment of wasting, mainly because we know little about the underlying etiology of wasting.We know that poor diets, seasonality, conflicts, food insecurity, poverty, and diseases, all contribute to increased risk of wasting ,27. HoweUnderstanding the timing of wasting, its incidence, and the most critical age, is essential to program effective interventions that can help reach the 5.4% AARR needed to reach the SDGs and help save over 680 million USD in the next decade. Such understanding requires longitudinal data or surveys covering multiple time points within a year to capture different seasons. However, the dynamic nature of wasting and its categorization as an acute phenomenon seem to have led to the failure to capture or interpret weight-for-height indicators in longitudinal studies. Among the few exceptions is the study by Mertens et al.  that pooTo increase the AARR and ensure the return on potential investment in wasting prevention and treatment, more studies on the timing and incidence of wasting are needed. Existing evidence suggests the need to focus on the first 1000 days, from pregnancy to the child’s second birthday, and that the aim of such interventions should be both preventive and therapeutic. Given the increasing number of studies linking wasting to child linear growth faltering, prevention efforts need to explicitly adopt stunting and wasting as outcome indicators. This is echoed by a recent cohort study from Malawi that showed higher risk of stunting among wasted children, and in turn, stunting in children who had just recovered from moderate or severe wasting had a higher rate of relapse . This biThe present study had some limitations that need to be considered when interpreting the findings. Given that wasting prevalence can change rapidly and frequently, even in the course of a year, the AARR projections may not be fully accurate. The available data used was from cross-sectional surveys, and thus only provide a snapshot of the situation at the time of the survey. Consequently, much more useful information on seasonal variation, incidence of wasting, and relapse was not available. The estimated loss of earnings related to wasting is likely to be an underestimate, as we only considered the proportion of children that are likely to be concurrently wasted and stunted (3%) when applying the available models from stunting. Converting indicators of malnutrition to economic activity and attaching a monetary value to that economic activity could be interpreted and analyzed in many ways. First, monetizing the consequences of malnutrition is dependent on evolving evidence bases, complex methodologies, national health, and demographic and economic statistics of uneven quality; second, many factors beyond individual physical and intellectual potential determine earnings or work performance. Workplace incentives, available technology, and sense of opportunity all affect how increased human potential translates into actual improved productivity. In addition, productivity growth was not considered in the model, even if we can speculate that today’s children are likely to be much more productive in their working life than their parents are today; finally, the benefits of improved nutrition extend beyond the workplace to a range of “voluntary” activities, including parenting and household activities, to educational improvement, entrepreneurial pursuits, and community participation. In a world where improvement in nutrition, health, and subsequent productivity can also emerge from individual choices and behaviors, the significance of these “voluntary” activities cannot be overstated. For all these reasons and more, the margin of error is large, and the calculations should be considered as an order of magnitude. These are projections to focus and facilitate policy discussion and present a solid and conservative case for such policy discussions. Therefore, data judgments or assumptions are consciously and consistently “biased” to minimize the impact of malnutrition. Consequently, conclusions drawn may be considered conservative low-end estimates.Notwithstanding the above limitations, the pace of reduction of the prevalence of child wasting should be accelerated. The short- and long-term economic burden of child wasting is significantly high and requires concerted multi-sectoral efforts to accelerate progress. The sole focus on the treatment, and not the prevention of wasting, has not helped address the underlying conditions leading to frequent relapse and child stunting. The world has wasted time to unravel the etiology of wasting; and hence, has missed opportunities to implement effective interventions that prevent wasting. Increasing the reach of therapeutic interventions, but also identifying wasting prevention interventions will be critical if the SDG targets are to be met, and the opportunity of the children to thrive is not to be wasted.


Diabetes is a global health challenge. Currently, an effective treatment for diabetes is to reduce the postprandial hyperglycaemia by inhibiting the carbohydrate hydrolysing enzymes in the digestive system. In this study, we investigated the in vitro α-glucosidase and α-amylase inhibitory effects of free and bound phenolic extracts, from the bran and kernel fractions of five sorghum grain genotypes. The results showed that the inhibitory effect of sorghum phenolic extracts depended on the phenolic concentration and composition. Sorghum with higher phenolic contents generally had higher inhibitory activity. Among the tested extracts, the brown sorghum (IS131C)-bran-free extract  = 18 ± 11 mg sorghum/mL) showed the strongest inhibition against α-glucosidase which was comparable to that of acarbose (IC50 = 1.39 ± 0.23 mg acarbose/mL). The red sorghum (Mr-Buster)-kernel-bound extract  was the most potent in inhibiting α-amylase but was much weaker compared to acarbose (IC50 = 0.50 ± 0.03 mg acarbose/mL). The key features of the disease are impaired body glucose metabolism and chronic hyperglycaemia, which can lead to damage to a range of body parts, including eyes, kidneys, nerves and blood vessels . One of e system . Synthete system . Howevere system . Researce system . In parte system ,7,8.Sorghum bicolor) grain, one of the top five most produced cereal crops worldwide [Sorghum  to attenuate hyperglycaemia. Kim, Hyun and Kim [α-glucosidase (from Bacillus stearothermophilus) and α-amylase , and found that some sorghum cultivars exhibited strong α-glucosidase inhibitory activity, while foxtail and proso millets had no detectable effect on these enzymes. However, most of the research has focused only on sorghum bran or sorghum tannin, or α-amylase inhibition. Knowledge on the effect of different sorghum genotypes and their polyphenols in inhibiting the digestive enzymes is still limited.Studies have shown that the administration of sorghum phenolic extract to streptozotocin-induced diabetic rats had a significant hypoglycaemic effect ,14. The , et al.  found th, et al.  showed t and Kim  comparedα-glucosidase and α-amylase digestive enzymes. We also determined the total phenolic , flavonoid  and tannin (by way of the vanillin-HCl assay method) contents to assist in understanding the phenolic compounds/fractions responsible for the enzyme inhibition.In our previous work, we investigated the phenolic profile of free and bound phenolic extracts of bran and kernel fractions from five Australian-grown sorghum genotypes , with a total of 20 extracts . In thisp-nitrophenyl-α-D-glucopyranoside (PNPG), bovine serum albumin protein standard, acarbose, p-nitrophenyl α-D-maltohexaoside (PNPG6), porcine pancreas α-amylase were obtained from Sigma-Aldrich . All other chemicals were obtained from Chem-Supply . All chemicals used in this study were of analytical or HPLC grade.Folin–Ciocalteu phenol reagent, gallic acid, (+)-catechin hydrate, vanillin, rat intestinal acetone powders, White colour Liberty (W), red colour Mr-Buster (RM), red colour Nuseed Cracka (RC) sorghum variety grains were kindly supplied by Nuseed Company  in 2019. Brown colour IS131C (BR) and black colour Shawaya Short Black 1 (BL) sorghum grains were grown and harvested from the experiment field of the Bentley campus of Curtin University, from January to April 2019 . All sorghum grains were abrasively decorticated by a TM05C SATAKE Testing Mill  to separate the bran and kernels. The grains were decorticated for 60 s to collect the bran, and the remaining grains were further decorticated for 45 s to remove uncleared bran to obtain the kernels. Both bran and kernels were ground, sieved 100% through a 500 µm sieve and then stored at −20 °C in the dark until analysis.v/v) under N2 gas, and the mixture was incubated at 25 °C with shaking in the dark for 2 h. The mixture was then centrifuged at 3500× g and 4 °C for 10 min to collect the supernatant, and the residue was re-extracted with 20 mL 80% methanol (v/v) two more times. Supernatants were pooled and evaporated at 40 °C under vacuum to dryness, and the resulting solid was re-dissolved in 10 mL of methanol and stored under N2 at −20 °C in the dark before analysis. The remaining residue was then used for extraction of bound phenolic compounds. The residue was mixed with 15 mL of 2 M HCl under N2 and incubated at 100 °C in a water bath for 1 h for hydrolysation. After cooling, 20 mL ethyl acetate was added and mixed thoroughly, and the ethyl acetate fraction was collected after partitioning. The hydrolysate was re-extracted with 25 mL ethyl acetate five more times, and all ethyl acetate fractions were combined and evaporated at 40 °C under vacuum to dryness. The resulting solid was re-dissolved in 10 mL of methanol and stored under N2 at −20 °C in the dark before analysis. The extraction was done in triplicate for all samples.Free and bound phenolic compounds were extracted according to the method described previously . For thev/v) for 5 min. Then, 600 µL of 7.5% Na2CO3 (w/v) solution was added, and the mixture was incubated at 25 °C in the dark for 2 h. The absorbance of the mixture was measured at 765 nm by a Multiskan GO UV-Vis spectrophotometer . Gallic acid was used as the standard (0 to 0.400 mg/mL), and the results were expressed as mg gallic acid equivalents (GAE)/g sorghum sample dry basis (db).The TPC was determined by the Folin–Ciocalteau method, as described previously . In brie2 (w/v) solution and were left to stand for 5 min. Then 45 µL of 10% AlCl3 (w/v) solution was added to the mixture and was left to stand for 6 min. Next, 600 µL of 0.5 M NaOH solution was added to develop the colour, and the mixture was then incubated at 25 °C in the dark with shaking for 10 min until it turned pink. The absorbance was measured at 510 nm by the UV-Vis spectrophotometer. (+)-Catechin hydrate was used as the standard (0 to 0.800 mg/mL), and the results were expressed as mg catechin equivalents (CAE)/g db.The TFC was measured according to the method of Zhishen, et al. . The extw/v) solution with acidified methanol solution . The phenolic extracts (800 µL) were mixed with 700 µL of vanillin-HCl reagent and then incubated at 25 °C for 20 min, and the absorbance was measured at 500 nm by the UV-Vis spectrophotometer. (+)-Catechin hydrate was used as the standard (0–0.800 mg/mL), and the results were expressed as mg catechin equivalents (CAE)/g db.The TTC was determined by the vanillin-HCl assay method ,22. Vaniα-glucosidase enzyme solution. In brief, rat intestinal acetone powder (1.0 g) was suspended in 25 mL potassium phosphate buffer-I . The mixture was maintained in cold conditions in an ice water bath and sonicated at 50 Hz for 5 min by a Q55 sonicator . Then, the sonicated mixture was centrifuged at 18,000× g and 4 °C for 15 min, and the supernatant containing the α-glucosidase was collected and stored at −20 °C until analysis. The protein content of the enzyme prepared was determined to be 4.99 ± 0.27 mg/mL using the Bradford assay method with bovine serum albumin as the protein standard.Rat intestinal acetone powder was used to prepare a mammalian α-glucosidase inhibitory activity of sorghum phenolic extracts was evaluated by the method of Ng, et al. [α-glucosidase solution were mixed in individual wells in a 96-well plate. Then, 20 µL of 25 mM p-nitrophenyl-α-D-glucopyranoside (PNPG) solution was added to start the enzymatic reaction. All reagents were kept cold in an ice water bath during the preparation. A program was set up to incubate the 96-well plate (covered with lid) at 37 °C for 60 min using the Multiskan GO UV-Vis spectrophotometer. After incubation, the lid was removed, and the absorbance was measured at 410 nm. The inhibition of α-glucosidase was determined using the formula below, where Asample = sample + enzyme + PNPG, Asample background = sample + enzyme + PNPG solvent, Acontrol = sample solvent + enzyme + PNPG and Acontrol background = sample solvent + enzyme + PNPG solvent. All the measurements including control and background samples were conducted in triplicate.The , et al. , and modα-amylase inhibitory activity of sorghum sample extracts was evaluated using the chromogenic substrate p-nitrophenyl α-D-maltohexaoside (PNPG6) and adapted to a 96-well microplate. Porcine pancreas α-amylase was diluted to 250 units/mL in sodium phosphate buffer-III  to prepare the α-amylase working solution. PNPG6 solution (5.0 mg/mL) was prepared with sodium phosphate buffer-IV . The reaction mixture containing 20 µL of sorghum phenolic extract, 100 µL sodium phosphate buffer-IV and 50 µL of the α-amylase working solution was added to wells in a 96-well plate. Then, 50 µL of the PNPG6 solution was added to initiate the reaction. All reagents were kept cold in an ice water bath during the preparation. A program was set to incubate the 96-well plate covered with lid at 37 °C for 40 min by the UV-Vis spectrophotometer. After incubation, the 96-well plate was cooled in an ice water bath, and 100 µL dimethyl sulfoxide (DMSO) was added to each well to dissolve precipitate, and then the absorbance was measured at 410 nm. The inhibition of α-amylase was determined using the formula below, where Asample = sample + enzyme + PNPG6, Asample background = sample + enzyme + PNPG6 solvent, Acontrol = sample solvent + enzyme + PNPG6 and Acontrol background = sample solvent + enzyme + PNPG6 solvent. All the measurements including control and background samples were conducted in triplicate.The Since the crude sorghum phenolic extracts  have different phenolic concentrations and the inhibition was expected to be derived from the phenolic contents, the inhibitory activity of each sorghum phenolic extract sample was divided by its total phenolic content (TPC) to evaluate the phenolic inhibition efficiency ,25. Resuα-glucosidase and α-amylase activities was determined from the inhibitions obtained from a range of concentrations of sorghum phenolic extracts. The IC50 value was derived from the least-squares regression line of the plot of inhibition % versus log10 sorghum phenolic concentration [The half-maximal inhibitory concentration (IC50) against ntration . ResultsAll measurements were carried out in triplicate and results were expressed as means ± SD. The significant difference between means was determined by one-way ANOVA with Tukey grouping at 95% confidence level using Minitab . The partial least squares (PLS) correlation test  was condUnderstanding the sorghum phenolic profile is the essential step in identifying the key phenolic compounds that contribute to the enzyme inhibition and other bioactive functions. The total phenolic content (TPC), total flavonoid content (TFC) and total tannin content (TTC) of the free and bound phenolic extracts from the bran and kernel fractions of five sorghum genotypes, white Liberty (W), red Mr-Buster (RM), red Nuseed Cracka (RC), brown IS131C (BR) and black Shawaya Short Black 1 (BL), are shown in p < 0.05). Similarly, the TFC of BL (14.05 ± 0.67 mg CE/g db) and BR (15.92 ± 0.67 mg CE/g db) sorghums were also much higher than that of RC (3.95 ± 0.75 mg CE/g db) and RM (4.78 ± 0.92 mg CE/g db) sorghums, followed by W (1.97 ± 0.07 mg CE/g db) sorghum (p < 0.05). Regarding the TTC, BL (138.64 ± 7.06 mg CE/g db) and BR (129.93 ± 9.24 mg CE/g db) sorghums had the highest, followed by RC (69.27 ± 5.92 mg CE/g db) sorghum, then RM (47.90 ± 0.29 mg CE/g db) sorghum, and finally W (5.74 ± 0.50 mg CE/g db) sorghum had a very low level of TTC (p < 0.05). In addition, the phenolic contents were concentrated in the bran. As shown in p < 0.05) than the bound phenolic contents, especially in the BL and BR sorghums and dominated the phenolic contents in these sorghums; except W sorghum. In the kernels, no significant difference was observed between the free and bound phenolic contents (p > 0.05). The results indicated that the phenolic compounds in sorghum bran are easily extractable, and BR and BL bran have huge industrial potential due to their high phenolic contents.The phenolic contents were significantly different between sorghum varieties. The TPC of black BL (31.33 ± 0.48 mg GAE/g db) and brown BR (33.74 ± 0.90 mg GAE/g db) sorghums were much higher than that of red RC (10.98 ± 0.86 mg GAE/g db) and RM (11.62 ± 0.34 mg GAE/g db) sorghums, followed by white W (7.16 ± 0.11 mg GAE/g db) sorghum  generally have higher levels of phenolics ,29. Moreα-glucosidase in varying degrees. Black BL and brown BR sorghum extracts generally had stronger α-glucosidase inhibition than red RC and RM sorghum extracts, followed by white W sorghum extracts. In addition, the bran extracts generally had stronger inhibition than the kernel extracts. In the bran, the free form phenolic extracts showed much stronger α-glucosidase inhibition than the bound phenolic extracts (p < 0.05), especially in the BL  and BR  sorghums, which had the highest inhibitory activity among all extract samples. The result was in line with the phenolic contents    as discuα-amylase inhibition was quite different. As shown in α-amylase inhibition than red RC sorghum extracts, followed by white W sorghum extracts, with the exception of red RM sorghum which also showed strong inhibitory activity. In addition, the bran extracts generally had stronger α-amylase inhibition than the kernel extracts, except for red RM sorghum. RM sorghum, the RM-kernel-bound extract, had the highest α-amylase inhibition (71% inhibition) among all sorghum extracts. However, no obvious trend was observed between the free and bound extracts. The result is roughly consistent with phenolic contents   was very low and was not significantly different (p > 0.05) from that in other kernel extracts (The result of and TTC) , as sorgextracts , which cα-glucosidase and α-amylase, with the RB-kernel-bound extract having the highest inhibition efficiency (p < 0.05).The inhibitory activities of sorghum phenolic extracts were divided by their TPC to evaluate the phenolic inhibition efficiency ,25, i.e.α-glucosidase and α-amylase [α-amylase inhibition is closely related to the tannin content, and sorghum with higher degrees of polymerisation tannins have shown greater inhibitions [α-glucosidase and α-amylase inhibition potency. The correlation between the inhibitory activity and specific sorghum phenolic compounds will be discussed in Research has shown that sorghum rich in tannins has strong inhibitory activity against -amylase . The α-aibitions ,17. Our ibitions ,31. Therα-glucosidase and α-amylase were determined and compared with that of acarbose, shown in α-glucosidase, and comparable to acarbose (IC50 = 1.39 ± 0.23 mg/mL). While the RM-kernel-bound extract (IC50 = 160 ± 12 mg sorghum/mL) was the most effective in inhibiting α-amylase but was much weaker, about 320 times weaker, than acarbose (IC50 = 0.50 ± 0.03 mg/mL).The half-maximal inhibitory concentration (IC50) of sorghum extracts  against α-amylase inhibition were generally higher than that of α-glucosidase inhibition, suggesting the phenolics in our tested sorghums were more effective against α-glucosidase than α-amylase.Another IC50 (mg GAE/mL) value, in terms of the amount of  sorghum phenolic contents required per mL reaction, was also determined to compare the phenolic inhibition efficiency . The resα-glucosidase and α-amylase by sorghum phenolic extract, in comparison with acarbose, have shown inconsistent results. Kim, Hyun and Kim [α-glucosidase (from Bacillus stearothermophilus) inhibition by phenolic extracts from the whole grain of some Korean sorghum cultivars (IC50 = 1.1–102.7 µg sorghum/mL) and found that some sorghum cultivars exhibited slightly stronger inhibition than acarbose (IC50 = 2.1 µg/mL). Links, Taylor, Kruger and Taylor [α-glucosidase (from yeast) and α-amylase (from porcine) inhibition by phenolic extracts from tannin-rich sorghum bran (PAN 3860 cultivar) and showed that the phenolic extract (IC50 = 0.4 µg sorghum/mL) had far stronger α-glucosidase inhibition (about 2000 times stronger) than acarbose (IC50 = 8464.0 µg/mL), but the extract (IC50 = 554.5 µg sorghum/mL) had weaker α-amylase inhibition (about 180 times weaker) than acarbose (IC50 = 3.1 µg/mL). The inconsistency between these studies (and our study), in comparison with acarbose, may be due to the difference in sorghum phenolic concentration and composition. It should be noted that the data between these studies (and our study) were not directly comparable, as the enzymes, substrates and assay methods used were different. Besides, the data obtained using yeast or bacteria α-glucosidase is less relevant than using mammalian α-glucosidases used in the present study, as mammalian α-glucosidases are different and have a narrower inhibitory spectrum with lower inhibition by phenolics [Previous studies evaluating the inhibition of  and Kim  investigd Taylor  investighenolics ,33.The sorghum phenolic extracts in this work showed significant inhibitory activity against these digestive enzymes, although their inhibition strength was relatively weak compared to acarbose. Sorghum phenolic compounds are well-known for their high antioxidant and anti-inflammatory activities , which cα-glucosidase inhibition was significantly positively correlated with TPC (0.961), TFC (0.954), TTC (0.955), 3-deoxyanthocyanidin (0.895), flavan-3-ol (0.581), flavanone (0.892), flavonol (0.722) and proanthocyanidin dimer (0.842) ; and positively correlated with bran (0.617) (p < 0.01); but had no correlation with sorghum genotypes (p > 0.05). The results suggest that the α-glucosidase inhibition is closely related not only to the total phenolic content but also to certain (active) phenolic subclasses/compounds; the active compounds are widely present in all sorghum genotypes, and sorghum bran has a high proportion of these compounds. Moreover, a total of 27 individual phenolic compounds  with α-glucosidase inhibition, and most of them are common plant phenolic compounds and have been widely studied and documented expect for 3-deoxyanthocyanidins [α-glucosidase inhibition (p < 0.01) (α-amylase inhibition only had a weak correlation with W (−0.453) and RM (0.447) sorghum (p < 0.05 for both), and had no significant correlation with the total/subclass phenolic contents or other variables (p > 0.05), indicating that the phenolic compounds in the tested sorghum samples generally had weak α-amylase inhibition, with RM sorghum only had a relatively stronger inhibition.The PLS correlation coefficients between enzyme inhibition, phenolic content and other variables are presented in yanidins ,36,37,38 < 0.01) . It is wα-glucosidase inhibition), the main phenolic compounds were flavonoids, including taxifolin 3-glucopyranoside (1993 µg/g), taxifolin (1479 µg/g), procyanidin B1 (1283 µg/g), catechin (1210 µg/g), apigeninidin (372 µg/g), naringenin 7-O-glucoside (269 µg/g) and eriodictyol 7-O-glucoside (78 µg/g). These compounds were all significantly positively correlated with α-glucosidase inhibition  and could be the main contributors to the α-glucosidase inhibition  and flavonoids  were the major phenolic compounds [p-coumaric acid, ferulic acid have also been shown to modulate glucose and insulin receptor function and lipid metabolism to improve glucose and lipid profiles of certain diseases including diabetes [In the kernel samples (high inhibition efficiency), phenolic acids , however, only five phenolic compounds -β-D-glucose, trans-ferulic acid, 7-methoxycoumarin-4-acetic acid and α,β-dihydroresveratrol) were found, and these phenolic compounds were also found in other sorghum samples with higher concentrations [α-amylase inhibition. We propose that kafirin, an alcohol-soluble protein and the major sorghum storage protein, may also be extracted and involved in the enzyme inhibition. According to Links, Taylor, Kruger and Taylor [α-glucosidase and α-amylase, it can interact with sorghum phenolic compounds such as tannins, and undergoes conformation change, which may bind and inhibit the enzymes. Further research is required to uncover the cause and mechanism.Regarding the RM-kernel-bound sample . Sorghum samples with a higher phenolic content generally had higher inhibitory activity: black and brown > red > white coloured sorghum. Sorghum bran, where the phenolic content is concentrated , was generally more effective in inhibiting α-glucosidase and α-amylase than the kernel; except red RM sorghum whose kernel phenolic content was more effective in inhibiting α-amylase.In conclusion, our investigation demonstrated that sorghum phenolic extracts exhibited significant in vitro inhibitory activity against rat intestinal The results from this study also suggest that the inhibitory activity may also be related to the phenolic composition, and some specific phenolic compounds may have high inhibition potency. In addition, other non-phenolic compounds such as kafirin may also be involved in the enzyme inhibition, which requires further investigation.α-glucosidase, while the RM-kernel-bound extract was the most effective inhibitor against α-amylase. Despite all sorghum extracts showing weaker inhibition relative to acarbose, sorghum has distinct advantages as it has high levels of resistant starch and other health-promoting benefits, as well as its role as a staple food especially in the regions of Africa and South Asia. Further research is under the way to isolate, purify and analyse the individual sorghum phenolic compounds responsible for the digestive enzyme inhibition observed in this study.Among the sorghum phenolic extracts, the BR-bran-free extract was the most effective inhibitor against


Devolatilization is an important process for separating and removing unnecessary residual volatile substances or solvents during the production of polymers using twin-screw extruders. Latinen proposed a surface renewal model to determine the concentration of volatile components in the extrudate of a single-screw extruder. When a twin-screw extruder is used to calculate the concentration, it is necessary to use the exposed surface area of the resin in the starved region of Latinen’s model, which, however, is difficult to estimate. In our previous work, we numerically determined resin profiles of the screws using the 2.5D Hele–Shaw flow model and the finite element method, which helps in estimating the surface area of devolatilization. In this study, we numerically analyzed the volatile concentration of the extrudate in a self-wiping corotating twin-screw extruder using Latinen’s surface renewal model along with our resin profile calculation method. The experimental results of the concentrations of the volatile component (toluene) in the extrudate of polypropylene agreed well with its numerical calculation with a relative error of 6.5% . Our results also showed that decreasing the flow rate and increasing the pump capacity were effective for removing the volatile component. The screw pitch of a full-flight screw was not affected by the devolatilization efficiency with a fixed flow rate and screw speed. A twin-screw extruder is a versatile polymer-processing machine for pelletizing, blending, alloying, compounding, devolatilizing, performing chemical reactions, and forming dies of molten polymers . During Flash devolatilization: A high-temperature and high-pressure polymer solution is first fed into an extruder. The high pressure is then released such that the extruder is at atmospheric pressure. As a result, the volatile component can be separated from the solution and removed through a process similar to flash distillation. Thereafter, the volatile components are discharged from a vent upstream of the extruder. The devolatilization efficiency of flash devolatilization can be determined using the vapor–liquid diagram of the volatile component at the initial and atmospheric-pressure levels. Although flash devolatilization is an essential part of the devolatilization sequence, only a few studies have investigated this process .Surface renewal devolatilization: This method depends on the surface area and residence time of the resin and the mass transfer rate of the volatile component. At a vent downstream of the feed port, the volatile substance diffuses out from the surface of the polymer solution owing to the difference between the partial pressure of the volatile component in the polymer solution and the atmospheric pressure. Latinen presented a pioneering work on modeling the surface renewal devolatilization phenomena in a single-screw extruder . SurfaceFoam devolatilization: When the residual volatile components are present in the range of a few parts per million, removal becomes very difficult solely by increasing the degree of vacuum through surface renewal devolatilization. To overcome this shortcoming, water is injected into the resin to induce steam-bubble foaming. The volatile component of the resin diffuses out into the steam bubbles. When the bubbles collapse on the resin surface, the volatile component of the bubbles dissipates in air. Because the total surface area of the bubbles is much larger than that of the surface renewal devolatilization area, the overall mass transfer increases significantly. This water-assisted foam devolatilization method is efficient in removing low-concentration volatile components .The following devolatilization phenomena—flash, surface renewal, and foaming—can be performed using twin-screw extruders.Hence, it is clear that for modeling the devolatilization phenomenon, it is essential to design a function of the surface area, residence time, and mass transfer rate of the volatile component. The mass transfer rate depends on the physicochemical properties of the resin and volatile components. The surface area and residence time change depending on the degree of fill, which is a function of the flow rate, screw speed, and screw geometry.® [Wang and Hashimoto derived the equations of surface area and residence time for a corotating twin-screw extruder . They el® . Their sRecently, our group developed a calculation method for the resin profile on starved or unfilled screws of a self-wiping corotating twin-screw extruder  using thIn this paper, we established the numerical calculation method for the volatile concentration of the extrudate in a self-wiping corotating twin screw extruder using the 2.5D Hele–Shaw flow model and the finite element method. Toluene was used as an example volatile component in the extrudate of polypropylene and its concentration was experimentally measured in a vacuum-vent-equipped self-wiping corotating twin-screw extruder. The experimental results agreed well with the simulation results except for the case of the lowest rotation speed (60 rpm). We also reviewed several theoretical and experimental studies to elucidate the aim of our study.The paper is organized as follows. In Our theoretical model of surface renewal devolatilization is based on Latinen’s model  Figure . The expkm is the mass transfer coefficient, C is the concentration of the volatile component in a resin, C* is the equilibrium concentration of the volatile component at a given partial pressure of the volatile component and resin temperature, Dm is the diffusion coefficient of the volatile component in the resin, and θ is the exposure time. The exposure time corresponds to the time when the molten resin passes through the exposed surface boundary length, which can be evaluated using Equation (2):Db is the barrel diameter, N is the screw rotational speed, and In the starved screw of a corotating twin-screw extruder, the surface of the molten resin formed on the barrel surface promotes surface renewal devolatilization. If the mass transfer rate of the volatile component at the surface is in a steady state, the amount of the volatile component evaporated as the gas is in balance with the mass transferred from the resin (Equation (1)). The third term in Equation (1) is obtained when the mass transfer in the resin occurs in a semi-infinite medium .(1)kmC−θ and dividing it by θ:The time-averaged mass transfer coefficient was obtained by integrating Equation (1) with respect to exposure time dz of the axial direction of screw z is described by the advection diffusion equation shown below:zu is the flow velocity of the screw axis (z), E is the diffusion coefficient of the volatile component in the z-direction, S is the exposed surface boundary length, and A is the cross-sectional area of the resin. In Equation (4), we have neglected the flow and diffusion of the volatile component along the circumference direction of the screws.Assuming a steady state, the material balance in the infinitesimal distance The process is in a steady state and the convection term is dominant rather than the diffusion term of Equation (4).zu is obtained from the mass flow rate and density of the resin:Qw is the mass flow rate and ρm is the density of the resin. Dividing Equation (5) with zu and substituting it with Equation (6) give:The mean flow velocity zs and ze by assuming that S is constant if the integration range is sufficiently small.Devolatilization efficiency χ is obtained when Equation (7) is integrated between S and combining Equations (2), (3), and (8), we obtain Equation (9).Assuming the exposed surface boundary length S, is obtained from the resin surface profile on screws, which was calculated by the method proposed in our previous paper [In Equation (9), the surface boundary length, us paper . The resS. In S vanishes and the devolatilization efficiency χ becomes unity. Devolatilization does not occur in the fully filled region. Instead, it occurs in the melt-sealed zone between the screw head and the kneading disk zone, indicated as the “devolatilization zone” in If the local degree of fill is close to unity and the summation of a-b, c-d, and e-f is zero, the exposed surface boundary length ze is derived from the devolatilization efficiency:The volatile concentration at The resin profile in the corotating screw extruder is determined based on the input of the screw geometry, feed rate, screw rotation speed, barrel temperature, and resin properties such as viscosity function, density, thermal conductivity, and heat capacity of the polymer . The heiDm, changes with the concentration of toluene in the resin and temperature. The following empirical equation is used to estimate the diffusion coefficient.rD is a diffusion coefficient at a reference temperature, φ is a volatile component concentration, φm is the initial value of the volatile component, T is the temperature, rT is the reference temperature, α is the temperature dependence, and β is a model parameter that represents the concentration dependence. Diffusion coefficient Dr was determined using the values reported by Pereira et al. [The diffusion coefficient of toluene, a et al.  . From these values, we can obtain the devolatilization efficiency at iz. Iterating this procedure, we finally obtain the volatile concentration at ez.Homo-polypropylene  was purchased from Prime Polymer . The melt flow rate was 0.7 g/min, measured in accordance with the ISO 1133:97 standard. The oscillation shear viscosity (complex viscosity) was measured using a modular compact rheometer  with ϕ = 25 mm and a 1-mm-thick parallel plate fixture. The oscillation ranged from 0.01 to 100 rad/s. The strain was 1.0%. P-PTD200 and H-PTD200 temperature control units were used. The temperatures used were 180, 190, and 200 °C. Based on the Cox–Merz rule, the complex viscosity was used to obtain the parameters for the Cross model described by Equations (13) and (14), as listed in 2) was used as a cryogen to cool and condense the toluene vapor exhausted from the vent. The temperature of the cryogen was below −50 °C.Toluene  was used as a volatile component. A mixture of ethanol  and dry ice . Using the flow rate of toluene in the extrudate given by Equation (15), the concentration of toluene in the extrudate can be determined as follows:Fp is the mass feed rate of the polymer.The devolatilization efficiency was obtained from the mass balance of toluene.The mean toluene flow rate from the vent port was calculated by measuring the mass of the condensed toluene every 15 min. To evaluate the effect of the screw rotation speed on the toluene concentration at the exit of the extruder, screw rotation speeds of 60, 90, 120, 150, and 180 rpm were used.The numerical simulation of devolatilization was performed in the starved zone by changing the screw rotational speed. The simulation results of the concentrations of toluene at the extruder were compared with the experimental results. The resin can maintain its resin profile at a faster screw rotation speed owing to the inertia of screw rotation, while it cannot do so at slow screw rotation speeds because of gravity. More work is needed to match the simulation result with the experimental result of the resin profile at 60 rpm.C* (Hereafter, the developed method is applied to the flow rate, screw pitch, and equilibrium concentration, C* . An incrIn The effect of the equilibrium concentration is shown in We developed a calculation method for determining the devolatilization efficiency and volatile-component concentration using Latinen’s surface renewal model, where the exposed surface boundary length is given by the local degree of fill in the self-wiping corotating twin-screw extruder. The local degree of fill was obtained from the method described in our previous study, which was used to determine the resin profile of starved screws. The concentrations of toluene as a volatile component in the simulation and experiments at the exit of the corotating twin-screw extruder agree well. The increase in the screw rotation speed increases both the exposed surface boundary length and the devolatilization efficiency. The flow rate and pump capacity are effective in increasing the devolatilization efficiency and decreasing the volatile concentration. The increase in the screw pitch of a full-flight screw is insensitive to the devolatilization efficiency.However, our simulation method has several drawbacks as well. Our calculation method of local degree of fill cannot be applied to extruders such as a counterrotating twin-screw extruder. The flow calculation to determine the local degree of fill is decoupled with the devolatilization efficiency calculation. The viscosity of the resin is a function of the volatile component concentration. The viscosity increases with increasing devolatilization. The viscosity change affects the pressure distribution, the degree of fill, and finally, the devolatilization efficiency. In addition, it is challenging to incorporate the viscosity change in the devolatilization simulation. This study used the surface renewal model of devolatilization, which is only one type of devolatilization. We will investigate the incorporation of foam devolatilization, where bubble nucleation and growth occur, in our next study.


Inflammasomes are multi-protein complexes that mediate the activation and secretion of the inflammatory cytokines IL-1β and IL-18. More than half a decade ago, it has been shown that the inflammasome adaptor molecule, ASC requires tyrosine phosphorylation to allow effective inflammasome assembly and sustained IL-1β/IL-18 release. This finding provided evidence that the tyrosine phosphorylation status of inflammasome components affects inflammasome assembly and that inflammasomes are subjected to regulation via kinases and phosphatases. In the subsequent years, it was reported that activation of the inflammasome receptor molecule, NLRP3, is modulated via tyrosine phosphorylation as well, and that NLRP3 de-phosphorylation at specific tyrosine residues was required for inflammasome assembly and sustained IL-1β/IL-18 release. These findings demonstrated the importance of tyrosine phosphorylation as a key modulator of inflammasome activity. Following these initial reports, additional work elucidated that the activity of several inflammasome components is dictated via their phosphorylation status. Particularly, the action of specific tyrosine kinases and phosphatases are of critical importance for the regulation of inflammasome assembly and activity. By summarizing the currently available literature on the interaction of tyrosine phosphatases with inflammasome components we here provide an overview how tyrosine phosphatases affect the activation status of inflammasomes. Inflammasomes are multi-protein complexes responsible for the cleavage and subsequent release of several inflammatory cytokines, including IL-1β and IL-18 . The firTo date, more than nine intracellular inflammasome-inducing receptors have been described. Each of them responds to distinct and in some cases very diverse stimuli, which typically consist of pathogen-associated molecular patterns (PAMPS), such as certain bacterial components , viral DNA, or danger/damage-associated molecular pattern (DAMP), including urate crystals, silica or other particulate molecules within the cytosol ,2. The mNLRP1. The first receptor that has been described to induce inflammasomes was NLRP1. The main activators of NLRP1 include bacterial products/toxins [s/toxins , such ass/toxins  and the s/toxins , but NLRs/toxins , and in s/toxins , more res/toxins , and thas/toxins .NLRP3. NLRP3 responds to a stunningly wide range of diverse stimuli, but despite extensive investigations it is still not fully understood how NLRP3 senses such a diverse range of molecules [olecules . Nevertholecules ,21. Furtolecules . Howeverolecules ,24, and NLRC4. The NLRC4 inflammasome is initiated upon cytosolic presence of bacterial flagella or type 3 secretion system components. However, in contrast to other inflammasome receptor proteins, NLRC4 does not directly interact with bacterial products but serves as an adaptor for NLR apoptosis inhibitory proteins (NAIPs) [ (NAIPs) ,26,27. S (NAIPs)  and acti (NAIPs) ,26,27. G (NAIPs) ,30.AIM2. The AIM2 inflammasome recognizes the cytosolic presence of double-stranded DNA (dsDNA), which is typically present upon infection with bacteria or DNA viruses. In this process, AIM2 directly binds to double stranded DNA and subsequently promotes the formation of ASC multimers [ultimers . In contultimers , indicatPyrin. The inflammasome receptor pyrin reacts to the invasion of diverse pathogens into the host cell [ost cell . Pyrin iost cell . RhoA GTost cell . Thus, pGiven the pro-inflammatory nature of inflammasome-mediated cytokine release, its involvement in inflammatory cell death, and the potent inflammatory action of the inflammasome product IL-1β, it is not surprising that inflammasome activation is a highly regulated process and that a series of steps are required for efficient and sustained inflammasome activity ,5,34. AsA more recently explored regulatory mechanism of inflammasome assembly/activity is the regulation of inflammasome components via their phosphorylation status. As an example, almost a decade ago, Qu et al. demonstrated that the inflammasome receptor NLRC4 is phosphorylated at Ser533 by protein kinase C upon recognition of its ligand, bacterial product-bound NAIP molecules . This stThe concept of reversible phosphorylation at specific amino acid residues to modulate the conformational stage and/or the activity of proteins has been described many decades ago ,49,50, aFor most inflammasome receptors, potential phosphorylation sites have been reported  on inflammasome activation and explore how they impact inflammatory responses in the body:(i) PTPN22 as a direct regulator of NLRP3 activity; (ii) PTPN2 as a regulator of the phosphorylation status of the adaptor molecule ASC; (ii) SHP2 (PTPN11) as a modulator of NLRP3 inflammasome activity via regulation of mitochondrial integrity; and (iv) PTP-S2 as a regulator of caspase-1 expression.For this review, the PubMed database was searched for the terms “Inflammasome AND phosphorylation”, which yielded 582 results, including 39 review articles. Non-English articles were excluded from the review. All 577 articles written in English were briefly screened for those containing relevant information on tyrosine residues and whether this affected their regulation. This yielded 30 articles that were used to compile an overview on inflammasome components that that are regulated via tyrosine phosphorylation. Based on these findings, PubMed was searched for the terms “NLRP3 AND tyrosine phosphorylation”, ASC AND tyrosine phosphorylation”. The resulting search hits rendered the four tyrosine phosphatases PTPN22, PTPN2, SHP2 and PTP-S2 as relevant for the regulation for inflammasome activation. A background on the function of these four tyrosine phosphatases has been added to the description on how they regulate inflammasome activation.Although tyrosine phosphorylation has been described for several inflammasome receptors, i.e., the NLRP molecules NLRP2, NLRP3, NLRP6, NLRP7, NLRP9, NLRP10, NLRP11, and NLRP14 , the effAdditionally, several reports showed that Bruton’s tyrosine kinase (Btk) is involved in NLRP3 regulation . InitialC. albicans and other fungal species, while being dispensable for ATP or nigericin-mediated NLRP3 activation [Furthermore, it has been demonstrated that spleen tyrosine kinase (Syk) activation is required for NLRP3 activation upon presence of tivation . Howevertivation . Acknowltivation ,62.The first evidence that tyrosine phosphorylation modulates the efficacy of ASC to serve as an inflammasome adaptor and to promote inflammasome assembly downstream of several inflammasome receptor molecules was provided by a study from Hara et al. . Here, tIn this section, we will highlight four tyrosine phosphatases  that have been described to influence inflammasome activation either directly via dephosphorylating inflammasome components, or indirectly via interfering with inflammasome activating mechanisms/pathways. An overview on the point and mode of interaction of these four tyrosine phosphatases with inflammasome components is given in PTPN22 is exclusively expressed in innate and adaptive immune cells ,75 and cWhile exploring the function of PTPN22 in macrophages and monocytes, we identified NLRP3 as a direct interaction partner of PTPN22 . PTPN22 Mice expressing the murine R619W ortholog of the human R629W PTPN22 variant are protected in acute DSS colitis . Having The NLRP3 inflammasome has been associated with the pathogenesis of different auto-inflammatory diseases such as Crohn’s disease, Alzheimer’s disease, diabetes, and atherosclerosis ,93,94. TPTPN2 results in generalized and uncontrolled inflammatory responses, and full-body PTPN2 knockout mice succumb to severe systemic inflammation 3–5 weeks after birth [PTPN2 are associated with several inflammatory disorders, including IBD [PTPN2 is closely related to PTPN22 and exerts prominent anti-inflammatory functions in the body. PTPN2 is expressed in all cell types and controls several pro-inflammatory mediators, including signal transducer and activator of transcription (STAT) molecules ,103, c-Jer birth . Variantding IBD , RA [112ding IBD , highligWith respect to inflammasome activation, PTPN2 has a dual regulatory effect: via interference with inflammatory signaling cascades ,105, PTPPTPN2-variant carriers [Regulation of ASC-mediated inflammasome activation by PTPN2 is critical for reducing inflammatory responses to several well-described inflammasome activators, such as flagellin, particulate molecules  or bacterial toxins . Defecticarriers ,115.PTPN11) into the picture of inflammasome-modulating tyrosine phosphatases. Like PTPN2, SHP2 is expressed in all tissues of the body, and it is involved in numerous signaling cascades, including growth factor and cytokine receptor induced signaling [NLRP3 responds to several cell damage-inducing compounds, and as mentioned above, it is still not fully understood how NLRP3 reacts to a wide range of stimuli with apparently different molecular and structural nature. However, many of these compounds promote oxidative stress and mitochondrial damage either directly or indirectly ,116, whiignaling . Althougignaling ,119,120.ignaling . In T ceignaling ,122, whiignaling . In thisWith regards to regulating inflammasome activation directly, Guo et al. first noticed that SHP2-deletion promoted NLRP3-mediated inflammasome activation , althougWhile macrophage-specific ablation of SHP2 promoted susceptibility to inflammasome-driven, experimental peritonitis , it alsoPTP-S2 is a nuclear tyrosine phosphatase that binds non-specifically to DNA within the cell nucleus. PTP-S2 is upregulated during mitosis and promotes progression through the G1 to the S phase ,130, indIn this review, we summarized the effect of tyrosine phosphorylation on inflammasome activity and subsequent IL-1β and IL-18 release. We further described the consequence of defects in the expression of four specific protein tyrosine phosphatases that modulate inflammasome activity either directly or indirectly. As summary of these phosphatases and their action is given in While focusing on NLRP3 and ASC as the inflammasome components for which tyrosine de-phosphorylation has already been explored in depth, it will be of high interest to investigate the effect of tyrosine phosphatases on other inflammasome components. Besides the well-studied effect of tyrosine phosphorylation on NLRP3, several tyrosine phosphorylation sites have been described in additional inflammasome receptors. However, to date, for most of them, neither the kinase responsible for phosphorylation has been identified, nor is it known whether these sites are targeted by tyrosine phosphatases. Therefore, the functional consequence of tyrosine phosphorylation on these receptors is still elusive. Nevertheless, with respect to those inflammasome components where tyrosine phosphorylation has been described more in depth, i.e., NLRP3 and ASC, it is obvious that the effect of tyrosine phosphorylation critically determines activation status and protein stability. Thus, tyrosine phosphatases contribute essentially to guarantee efficient, adequate, yet controlled inflammasome activation. Given the high impact of tyrosine phosphorylation on the activity of inflammasomes, studies that explore the effect of tyrosine kinases and tyrosine phosphatases on additional inflammasome components will clearly promote our understanding how inflammasomes and their potent pro-inflammatory effect in the immune system are regulated.Inflammasome components are subjected to regulation via tyrosine phosphorylationWhile the effect of some tyrosine phosphorylation sites on regulation of inflammasome assembly/activation is known, the effect of many putative or known tyrosine phosphorylation sites is still elusiveNLRP3 and ASC are the most well-described inflammasome components regulated via tyrosine phosphorylationTyrosine phosphorylation can have opposite effects (activation vs. inhibition)To date, four main tyrosine phosphatases  have been described to affect inflammasome assemblyFuture studies that assess the effect of tyrosine phosphorylation of additional inflammasome receptors will be of great relevance to understand the effect of (tyrosine) phosphorylation on inflammasomes more in depth


Orthoreovirus in the family Reoviridae. The disease caused by DRV leads to huge economic losses to the duck industry. Post-translational modification is an efficient strategy to enhance the immune responses to virus infection. However, the roles of protein phosphorylation in the responses of ducklings to Classic/Novel DRV (C/NDRV) infections are largely unknown. Using a high-resolution LC–MS/MS integrated to highly sensitive immune-affinity antibody method, phosphoproteomes of Cairna moschata spleen tissues under the C/NDRV infections were analyzed, producing a total of 8,504 phosphorylation sites on 2,853 proteins. After normalization with proteomic data, 392 sites on 288 proteins and 484 sites on 342 proteins were significantly changed under the C/NDRV infections, respectively. To characterize the differentially phosphorylated proteins (DPPs), a systematic bioinformatics analyses including Gene Ontology annotation, domain annotation, subcellular localization, and Kyoto Encyclopedia of Genes and Genomes pathway annotation were performed. Two important serine protease system-related proteins, coagulation factor X and fibrinogen α-chain, were identified as phosphorylated proteins, suggesting an involvement of blood coagulation under the C/NDRV infections. Furthermore, 16 proteins involving the intracellular signaling pathways of pattern-recognition receptors were identified as phosphorylated proteins. Changes in the phosphorylation levels of MyD88, NF-κB, RIP1, MDA5 and IRF7 suggested a crucial role of protein phosphorylation in host immune responses of C. moschata. Our study provides new insights into the responses of ducklings to the C/NDRV infections at PTM level.Duck reovirus (DRV) is a fatal member of the genus  Orthoreovirus in the family Reoviridae1. The disease caused by DRV was firstly reported in South Africa in 1950 and isolated in France in 1972 3. In China, infection of DRV, named classical DRV (CDRV), was firstly discovered in Muscovy ducklings in 19974. The disease was characterized by liver and spleen swollen, and covered small white necrotic foci6. Additionally, the CDRV infection also causes a series of clinical symptoms, such as basal weakness, diarrhea, hypoevolutism, etc8. In the past ten years, a novel duck reovirus  was widely identified in China11. The disease is principally discriminated by hemorrhagic-necrotic lesions in various internal organs, particularly in liver and spleen12. Infection of C/NDRV causes a high morbidity and mortality rate. Although the commercial MDRV vaccine is available, but it is still difficult to prevent their infection and transmission in ducklings within 4 weeks of birth12.Duck reovirus (DRV) is a fatal aquatic bird pathogen belonging to the genus 14. Completed DRV consists of a double protein capsid shell and 10 double-stranded RNA fragments, including three large size segments, three medium size segments, and four small size segments14. At least, 10 structural proteins, including three λ translation products, three μ translation products, and four σ translation products, are encoded by the DRV genome15. Though the structure of DRV is known, the responsive mechanisms underlying the infection of C/NDRV are uncovered yet.The genome and structure of DRV have been reported yet17. By recognizing pathogen-associated molecular patterns (PAMPs), activated PRRs transmit signals to various adaptors, such as Myeloid differentiation primary response gene 88 (MyD88), TIR-domain containing adaptor protein (TRIF), MyD88-adapter-like protein/TIR domain-containing adapter protein (MAL/TIRAP), TRIF-related adaptor molecule (TRAM), and Interferon beta promoter stimulator-1 (IPS-1), which activate a number of downstream kinases  family member associated NF-κB activator binding kinase 1 (TBK1), and IPS-1 interacts with receptor-interacting protein-1 (RIP-1)) and transcription factors 18. Several previous studies have revealed the responses of intracellular signaling pathways of PRRs to pathogen infections. In Pekin ducks, the expression levels of several PRRs encoding genes, such asTLR4/7, RIG-1 and Melanoma Differentiation-Associated Gene-5(MDA5), were significantly up-regulated under the infection of duck hepatitis A virus19. In cherry valley duck, NLRP3, a pattern recognition receptor in host innate immunity, has a certain antibacterial activity during Escherichia coli infection20. The expression of pattern recognition two receptors encoding genes, RIG-I and MDA5, was significantly affected by the infection of duck Tembusu virus21. The interactions between MDRV/NDRV infections and hosts immune response also have been reported in the past years. In MDRV, σB is a structural protein that is able to induce immune response in ducks22. A robust immunity response against NDRV in ducklings depends on a subunit vaccine of sigma C protein12. Further studies showed that MDRV infection activated host immune response mainly through RIG-I, MDA5 and TLR3-dependent signaling pathways23.Innate and adaptive immunity is required for host defense system to fight against pathogen infections. Pattern recognition receptors (PRRs), including Toll-like receptors (TLRs), retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) and NOD-like receptors (NLRs), play critical roles in initiating the innate and adaptive immune responsesThe immune responses against pathogen infections play an important role in controlling the prevalence of pathogens.24. Several fatty acid degradation-related genes, such as ATP binding cassette transport G8 and apolipoprotein A-IV, were siginificantly induced by the MDRV infection25. The expression levels of chemotaxis cytokine receptors encoding genes  were also significantly up-regulated under the MDRV infection26. By two-dimensional polyacrylamide gel electrophoresis, 59 of differentially expressed proteins, involving metabolism and utilization of carbohydrates and nucleotides, anti-stress, and regulation of immune, were detected in Muscovy duck embryo fibroblasts infected with MDRV27. Our previous studies showed that proteins involved in the serine protease play roles in the responses to the C/NDRV infections in both liver and spleen28. Additionally, two glycolysis-related enzymes, fructose bisphosphate aldolase and pyruvate kinase, were largely up-regulated by the C/NDRV infections29.Recently, increasing studies have focused on screening of C/NDRV infection responsive genes and proteins of ducklings. For examples, a number of genes involved in the RIG-I-like and TLR signaling pathways respond to the infection of MDRV31. Over the past years, PTMs, such as phosphorylation, ubiquitination, succinylation, acetylation methylation, and crotonylation, have been deeply studied in both prokaryotic and eukaryotic organisms34. Reversible phosphorylation leads to conformational changes involving various cellular processes, including protein–protein interaction, enzyme inhibition and activation, and energy supply35. In mammals, protein phosphorylation is essential for the immune responses to virus infections36. For examples, protein phosphorylation is a important component of host cell responses to dengue virus infection37. Phosphorylation of eIF2α, an important regulator in the PERK pathway, is essential for the responses to Chikungunya and Sindbis virus infections38. Phosphorylation of transcription factor Sp1 enhances the responses to herpes simplex virus type 1 infection39. However, the roles of protein phosphorylation in the responses of ducklings to C/NDRV infections are largely unknown.In addition to transcriptional regulation, post-translational modification (PTM) is an efficient strategy for enhancing the regulation of cellular physiology and the immune responses to virus infections29. Our previous studies have identified a number of proteins responsive to the C/NDRV infections at expression level29. In the present study, the global phosphorylation proteome of duckling spleen was investigated using high-resolution LC–MS/MS integrated to highly sensitive immune-affinity antibody analysis. This study provides new insight into the molecular mechanism underlying the C/NDRV infections in ducklings at the PTM level.As a target organ of DRV infection, spleen and live are two major organs of ducklings for digestion and immune responses40. Twenty seven 1-day-old healthy ducklings were randomly separated into three groups. For the CDRV infection, one group (nine ducklings) was inoculated intramuscular with 0.5 mL of CDRV  at a titer of 105.19 median tissue culture infective dose (TCID50) per mL. For the NDRV infection, another group (nine ducklings) was inoculated intramuscular with 0.5 mL of medium infected by NDRV  contained the same TCID50. Additionally, one group with nine un-infected ducklings was treated with sterile medium in the same manner. For each treatment group, nine ducklings were divided into three independent subgroups (three ducklings each). Ducklings of each subgroup were euthanatized after 72 h post infection (hpi). Their spleen samples were collected and snap-frozen in liquid N2. Subsequently, frozen tissues were kept at − 80 °C until used28.The experimental infection was carried out using 1-day-old healthy Muscovy ducklings, and their blood samples were checked by RT-PCR. P10, P18 encoding genes was used for the RT-PCR2 into cell powder and then transferred to a 5 mL centrifuge tube. Four volumes of lysis buffer containing 8 M urea, 1% Protease Inhibitor Cocktail were added to the cell powder, followed by sonication three times on ice using a high intensity ultrasonic processor . The remaining debris was discarded by centrifugation at 12,000g at 4 °C for 10 min. Finally, the supernatant was collected and protein concentration was determined using a bicinchoninic acid assay kit  according to the manufacturer’s instructions29.The samples were grinded by liquid N28.For digestion, the protein solution was reduced with 5 mM dithiothreitol for 30 min at 56 °C and alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. The protein sample was then diluted by adding 100 mM TEAB to urea concentration less than 2 M. Finally, trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion29.After trypsin digestion, peptide was desalted by Strata X C18 SPE column  and vacuum-dried. Peptide was reconstituted in 0.5 M TEAB and processed according to the manufacturer’s protocol for TMT kit. Briefly, one unit of TMT reagent was thawed and reconstituted in acetonitrile. The peptide mixtures were then incubated for 2 h at room temperature, pooled, desalted and dried by vacuum centrifugation. The tryptic peptides were fractionated into fractions by high pH reverse-phase HPLC using Thermo Betasil C18 column . Briefly, peptides were first separated with a gradient of 8% to 32% acetonitrile (pH 9.0) over 60 min into 60 fractions. Then, the peptides were combined into 6 fractions and dried by vacuum centrifuging4OH. The supernatant containing phosphopeptides was collected and lyophilized for LC–MS/MS analysis28.Peptide mixtures were first incubated with IMAC microspheres suspension with vibration in loading buffer (50% acetonitrile/6% trifluoroacetic acid). The IMAC microspheres with enriched phosphopeptides were collected by centrifugation, and the supernatant was removed. To remove nonspecifically adsorbed peptides, the IMAC microspheres were washed with solutions of 50% acetonitrile/6% trifluoroacetic acid and 30% acetonitrile/0.1% trifluoroacetic acid, sequentially. The enriched phosphopeptides were eluted from the IMAC microspheres by adding elution buffer containing 10% NHThe tryptic peptides were dissolved in 0.1% formic acid (solvent A), directly loaded onto a home-made reversed-phase analytical column . The gradient was comprised of an increasing from 6 to 23% solvent B (0.1% formic acid in 98% acetonitrile) over 26 min, 23% to 35% in 8 min, climbing to 80% in 3 min, and then holding at 80% for the last 3 min. The experiment was processed at a constant flow rate of 400 nL/min on an EASY-nLC 1,000 UPLC system .m/z scan range was set from 350 to 1,800 for full scan, and intact peptides were detected in the Orbitrap at a resolution of 70,000. Peptides were then selected for MS/MS using NCE setting as 28 and the fragments were detected in the Orbitrap at a resolution of 17,500. A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans with 15.0 s dynamic exclusion. Automatic gain control was set at 5E4. Fixed first mass was set as 100 m/z31. The MS proteomics data have been set to the ProteomeXchange Consortium by PRIDE partner repository program under identifier PXD016423.The peptides were subjected to NSI source followed by MS/MS in Q ExactiveTM Plus  coupled online to UPLC. The electrospray voltage applied was set at 2.0 kV. The 29.The resulting MS/MS data were processed using MaxQuant search engine (v.1.5.2.8). MS/MS data were searched against UniProt database concatenated with reverse decoy database (unip_Anas_8839 database). Trypsin/P was specified as cleavage enzyme allowing up to 4 missing cleavages. The mass tolerance for precursor ions was set as 20 ppm in first search and 5 ppm in main search, and the mass tolerance for fragment ions was set as 0.02 Da. Carbamidomethyl on Cys was specified as fixed modification and Acetylation modification and oxidation on Met were specified as variable modifications. FDR was adjusted to < 1% and minimum score for modified peptides was set > 4042, and InterPro domain databases. GO annotation was derived from the UniProt-GOA database (https://www.ebi.ac.uk/GOA/). All identified peptide IDs were converted into UniProt IDs and then mapped onto the GO database. KEGG was used to annotate the proteins involving in pathways. KEGG online service tool KAAS was used to provide the KEGG description of proteins. KEGG online service tool KEGG mapper was used to map the annotation results on the KEGG pathway database. Identified proteins domain functional description was annotated by InterProScan using protein sequence alignment method29.All identified proteins were annotated searching against Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG)P value < 0.05 are considered significant.For each GO, KEGG, and domain category, a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially modified protein against all identified proteins. The GO term, KEGG pathway and domain with a corrected P values. The filtered P value matrix was transformed by the function x =  − log10 . Finally, these x values were z-transformed for each functional category. These z scores were then clustered by one-way hierarchical clustering  in Genesis. Cluster membership were visualized by a heat map using the “heatmap.2” function from the “gplots” R-package28.For enrichment based clustering, all the categories after enrichment along with their 34.Motif-x algorithm was used to analysis the model of sequences constituted with amino acids in specific positions of modify-13-mers (6 amino acids upstream and downstream of the site) in all protein sequences. And all the protein sequences in database were used as background parameter. Minimum number of occurrences was set to 20. Emulate original motif-x was ticked, and other parameters with defaultSignificant differences in phosphorylation level between two groups were calculated using a one-way analysis of variance with a Tukey's test. Three biological replicates were applied in protein phosphorylation level analysis.28.Animal experiments were performed in the Institute of Animal Husbandry and Veterinary Sciences (IAHV), Zhejiang Academy of Agricultural Sciences (ZAAS). All ducks used in the present experiments were treated in accordance with the Regulations of the Administration of Affairs Concerning Experimental Animals approved by the State Council of China. The bird protocol used in this study was approved by the Research Ethics Committee of ZAAS (permit number: ZAAS20160802)Through affinity enrichment integrated with LC–MS/MS approach, the phosphoproteomic changes in the spleen tissues of duckling under the C/NDRV infections were investigated Fig. a. CorrelIn the spleen tissues of ducklings, 1,196 41.2%) phosphorylated proteins were modified at a single site, 624 (21.9%) phosphorylated proteins were modified at two sites, and 1,031 (36.1%) phosphorylated proteins contain three and more sites , Nicotiana tabacum (3.05), human breast cancer cells (2.71), and human glial cells (2.75) , threonine (T), and tyrosine (Y) residuesues Fig. . Five siA position-specific heat map was produced to assess the preference for specific amino acid residues surrounding each phosphorylation site. For the S-based motifs, a strong preference for aspartic acid (D), glutamic acid (E), and proline (P) in the upstream and lysine (K) and arginine (R) in the downstream of the phosphorylation sites were observed. For the T-based motifs, a preference for P in the upstream and P and R in the downstream of the phosphorylation sites were observed Fig. c.After normalization with proteomic data, the effect of differential protein accumulation on the modified signal was removed. Then, the accumulation levels of all phosphorylated proteins in different treatment groups are shown by a heatmap Fig. a. In theFor phosphorylation sites, 96 and 79 sites were up- and down-regulated respectively by both of the C/NDRV infections. For phosphorylated proteins, 90 and 74 proteins were up- and down-regulated respectively by both of the C/NDRV infections Fig. c.Under the C/NDRV infections, most of the DPPs were assigned into at least one GO term. In the CDRV/Control comparison, the most significantly enriched ‘biological process’ term was ‘intracellular protein transport’, the most significantly enriched ‘molecular function’ term was ‘rRNA binding’, and the most significantly enriched ‘cellular component’ term was ‘actin cytoskeleton’ Fig. ; in the For the up-regulated DPPs, three important KEGG terms, including ‘carbon metabolism’, ‘glyoxylate and dicarboxylate metabolism’, and ‘citrate cycle’, were significantly enriched in both of the CDRV/Control and NDRV/Control comparisons. The ‘biosynthesis of amino acids’ KEGG term was siginificantly enriched in the CDRV/Control comparison, and the ‘focal adhesion’ and ‘phosphatidylinositol signaling system’ terms were siginificantly enriched in the NDRV/Control comparison Fig. a. For th48. In our study, a number of HSPs, including 60 kDa, 71 kDa, 90 kDa, A-, H-, and DnaJ types of HSPs, were identified as phosphorylated proteins  was higher than most of the published species, such as Sus domesticus (966 phosphorylated proteins), Bombyx mori , Kandelia candel , Nicotiana tabacum , Lotus japonicus , Ammopiptanthus mongolicus , Catalpa fargesii , Abelmoschus esculentus , and Physcomitrella patens 53. High-throughput method gives us an opportunity to screen potential phosphorylated proteins and reveal the involvement of protein phosphorylation in the responses to the C/NDRV infections. The number of phosphorylation sites per protein is near 3, higher than most of the published phosphoproteomes, indicating a high degree phosphorylation level of C. moschata proteome.Protein phosphorylation is a universal PTM; however, the abundance of phosphorylation largely varies among different species. In 54. Several intermediates of the TCA cycle, which generates ATP to provide energy for key cellular functions, are involved in the innate immune system during infection and inflammation55. In our study, enrichment analysis showed that phosphorylation levels of the enzymes involved in the TCA cycle was significantly up-regulated by both of CDRV and NDRV infections. It suggested that higher phosphorylation level of the TCA cycle might provide more energy to enhance the responses to the C/NDRV infections. Moreover, 143 DPPs were identified in the NDRV/CDRV comparison, suggesting a great difference between the classic and novel DRVs. The ErbB signaling pathway and MAPK signaling pathway participate in regulating diverse biologic responses, including proliferation, differentiation, cell motility, senescence, apoptosis and survival57. KEGG enrichment analysis showed that the ErbB signaling pathway was significantly up-regulated and the MAPK signaling pathway was significantly down-regulated in the NDRV/CDRV comparison. Changes in the phosphorylation levels of the two disease-related signaling pathways might be required for the responses to the C/NDRV infections.Infectious bursal disease virus infection siginificantly affected the energy metabolisms29. However, the roles of protein phosphorylation in the responses of serine protease system to the C/NDRV infections are largely unknown. Under virus infection, coagulation cascade is activated to fight against the invasion of pathogenic microorganisms58. In the coagulation cascade, coagulation factor X is a plasma serine endopeptidase that plays a chief role in blood coagulation59. In the present study, coagulation factor X was identified as phosphorylated protein, and its phosphorylation site (S393) was significantly reduced by the CDRV infection and induced by the NDRV infection. It suggested that phosphorylation of the S393 site in coagulation factor X might be involved in the blood coagulation pathway of C. moschata. Fibrinogen, a soluble protein with three chains, including α, β and γ chains, is a therapeutic target for bleeding60. Two phosphorylation sites in the α chain of fibrinogen were identified in C. moschata, and one (S492) of which was significantly down-regulated by the CDRV infection and up-regulated by the NDRV infection. Changes in phosphorylation level of fibrinogen might affect the control of blood coagulation under the C/NDRV infections. Though the importance of complement proteins in serine protease system is well known, no phosphorylation site was detected in complement proteins.Our previous works indicated a central role of the network of seine proteases, consisting of the complement, coagulation and fibrinolytic systems, in the innate immune reactions61. Chaperones, HSPs in particular, participate in activation of the immune system62. In our study, 11 HSPs were identified as phosphorylated proteins, such a 71 kDa HSP with four phosphorylation sites, a 60 kDa HSP with two phosphorylation sites. Increasingly, several phosphorylation sites of the 60 kDa HSPs, such as the S67 and S70 of 60 kDa HSP, were up-regulated under the CDRV infection, suggesting a role of phosphorylation in the folding of immunologically important proteins.Chaperones belong to a class of small compounds and play essential roles in the protein folding process63. PTMs, such as phosphorylation, help to ensure the balance of immune signal transduction by activating or terminating the immune responses at the proper time64. In our study, GO analysis showed that 183 phosphorylated proteins belonging to the ‘immune system process’ term. In detail, 16 phosphorylated proteins involving the signaling pathways of PRRs, including receptors, adaptors, kinases and downstream TFs, were identified, suggesting a crucial role of protein phosphorylation in host immune responses of C. moschata.To effectively respond to environmental antigens, the immune system is exquisitely balanced16. RIG-I-like receptors, including RIG-I, MDA5 and LGP2, are the primary sensors for recognizing viral RNA65. MDA5 functions as a cytoplasmic double-stranded RNA sensor and plays a critical role in antiviral innate immunity. PTMs are essential for activation of MDA518. For example, ubiquitination of MDA5 at lysine 743 is critical for oligomerization and activation of MDA566. In C. moschata, two phosphorylation sites (S304 and S1009) were identified in MDA5, suggesting that phosphorylation of MDA5 at serine 304 and 1,009 might also be required for the activation of MDA5. Adaptor molecules act as messengers that deliver signals from the receptors to activate various downstream kinases and TFs. MyD88, a well characterized adaptors in TLR signaling, is located in the cytosol near the cytosolic part of TLRs and delivers an activation signal to activate NF-κB67. In C. moschata, there is one phosphorylation site (S195) in MyD88, and its phosphorylation level was significantly up-regulated under the CDRV infection. Phosphorylation of MyD88 might enhance the activation signal that is initiated by receptor activation. Further, another five adaptors, such as TRAM1/2, IRAK, TRAF3/6, were identified as phosphorylated proteins, although no quantitative data was available.Recognition of pathogen-associated molecular patterns by PRRs is the initial step of innate and adaptive immune responses18. For examples, IκB kinase complex, consisting of two catalytic components, IKKα and IKKβ, and a regulatory component, NF-κB essential modifier (NEMO), acts as main kinase68. TRAF family member-associated NF-κB activator binding kinase-1(TBK1) functions as a kinase in the phosphorylation and activation of IFN regulatory factor3 (IRF3)69. In our study, NEMO, IKKβ, and TBK1 contained one phosphorylation each, suggesting that phosphorylation might be involved in their function of kinase. Phosphorylated IκB is degradated by ubiquitination18. In C. moschata, phosphorylated IKKβ might be responsible for the degradation of IκB kinase complex.A series of kinases that was activated by pathogen signals from adaptors can phosphorylate downstream TFs70. Our results showed that the phosphorylation levels of NF-κB (T953) and IRF7 (S13) were siginificantly reduced under CDRV and NDRV infections, respectively. Further analysis of how these phosphorylated TFs contributes to the responses to the C/NDRV infections is needed.A number of TFs have been previously reported in PRRs. As downstream TFs, NF-κB, AP1, and IRF produced effecter molecules including cytokines, chemokines, inflammatory enzymes, and type I interferones71. In ducks, disruption of C/NDRV induced host protein phosphorylation might contribute to virus control.Recently, the roles of protein phosphorylation in host cell response to virus infection have been reported. For example, phosphorylation of AKT-mTORC1 pathway led to disruption of DNA double-strand breaks-induced herpes simplex virus-1 reactivationCairna moschata spleen tissues under the C/NDRV infections were analyzed. A total of 8,504 phosphorylation sites were identified on 2,853 proteins, of which 6,343 sites of 2,407 proteins were quantified. The phosphorylation levels of coagulation factor X and fibrinogen α chain were changed, suggesting an enhancement of blood coagulation under the C/NDRV infections. Furthermore, 16 proteins involving the intracellular signaling pathways of PRRs were identified as phosphorylated proteins, suggesting that phosphorylation might also be required for the activation of innate and adaptive immune responses. Our study provides new insights into the responses of ducklings to the C/NDRV infections at the PTM level.Using a high-resolution LC–MS/MS integrated to highly sensitive immune-affinity antibody method, phosphoproteomes of Supplementary Information 1.Supplementary Information 2.


Dear Editor,1 belongs to the class A GPCR subfamily. The major TGR5-dependent actions of BAs include maintaining energy homeostasis, regulating glucose/lipids metabolism, as well as immunosuppressive properties.2 TGR5 is identified as a potential therapeutic target for protecting hepatocytes from bile acid overload, preventing atherosclerosis, and inhibiting macrophage inflammation due to its critical role in bile acid sensitization. Thus, elucidation of structural characteristics of TGR5 and its activation mechanism would benefit the discovery of therapeutic drugs for these metabolic disorders.Takeda G protein-coupled receptor 5 (TGR5), also known as G protein-coupled bile acids (BAs) receptor 1 (GPBAR1),3 and Gs protein , resulting in the elevation of intracellular terminal of TM6 for GαsRas interaction. Two helical turns extension of TM5 helix, which contributed to the interaction between TGR5 and GαsRas, was observed , A1133.53, V1143.54, V1885.62, A1925.66, and Q1955.69 (Fig. β forms hydrogen bonds with R44ICL1 of TGR5 (Fig. s-coupled peptide activated class B GLP-1 receptor5 but not in β2AR. This suggested that other than stabilizing the N-terminal α helix of Gαs, Gβ might also involve in receptor binding. Besides, Nb35 binds to the interface between Gβ and GαsRas to stabilize the complex for structure determination (Fig. Sequence analysis revealed that several TGR5 residues involved in the interaction were identical to that in β.69 Fig. . It is wGR5 Fig. , which wion Fig. .s complex structure and mutagenesis analysis revealed the agonist binding mode of TGR5 indicating the convergent activation mechanism, in which the orthosteric binding site could recognize distinct ligands and accommodate the receptor activation. The slight differences in detailed recognition of 23H and LCA will also shed light on the development of therapeutics with improved efficacy and specificity. We firmly believed that TGR5 is a proper prototype on the mechanistic understanding of other GPCRs sensing steroids.In summary, our studies on TGR5-GSupplementary InformationSupplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5Supplementary Figure 6Supplementary Figure 7Supplementary Figure 8Supplementary Figure 9Supplementary Figure 10Supplementary Figure 11