In this study, the effects of sea ice and wind speed on the timing and composition of phytoplankton spring bloom in the central and southern Baltic Sea are investigated by a hydrodynamic–biogeochemical model and observational data. The modelling experiment compared the results of a reference run in the presence of sea ice with those of a run in the absence of sea ice, which confirmed that ecological conditions differed significantly for both the scenarios. It has been found that diatoms dominate the phytoplankton biomass in the absence of sea ice, whereas dinoflagellates dominate the biomass in the presence of thin sea ice. The study concludes that under moderate ice conditions (representing the last few decades), dinoflagellates dominate the spring bloom phytoplankton biomass in the Baltic Sea, whereas diatoms will be dominant in the future as a result of climate change i.e. in the absence of sea ice. The Baltic Sea has suffered historically from anthropogenic river borne nutrient loads due to intensified agriculture and waste-water discharges since the 1950s . Long nuA unique combination of physical, chemical, and biological factors shape the marine ecosystem. Due to the complexity of the system, it is difficult to distinguish between the impact of climate change and human eutrophication on the ecosystem. Studies that clarify the role of humans or climate change in marine life are essential to achieving a healthy marine ecosystem . The BalPhysical factors like light and stratification are key influences controlling phytoplankton spring bloom . The seaThe Baltic Sea ice season lasts 5–7 months between November to May. During a mild winter, ice occurs only in the Bothnian Bay and the easternmost Gulf of Finland, while the whole Baltic Sea is covered with ice during a cold winter . The maxStudies on phytoplankton spring bloom have been increasingly gaining importance in the context of global climate change. It has been reported that the changing environmental conditions have reduced the coverage and thickness of sea ice and increased the water temperatures in the Baltic Sea –17. IncrDiatoms constitute the major component of phytoplankton spring bloom in most coastal ecosystems, whereas vernal phytoplankton communities in the Baltic Sea are characterised by the co-occurrence and often dominance of cold-water dinoflagellates , 31. FacA coupled hydrodynamic–biogeochemical model was used to estimate the effects of sea ice and wind speed on phytoplankton composition during spring between 1 March and 10 May in the relatively severest winters of 2010, 2011 and 2013. Recent years have mainly observed mild winters in the Baltic sea. The results of the model in the presence and the absence of sea ice were compared. All the runs used the same initial and boundary conditions; however, sea ice conditions were varied.https://getm.eu/) .Diatom and dinoflagellate concentrations and water density at four stations  were anahttp://nest.su.se), root-mean-square error of sea surface and bottom salinity were between 0.3–1.7 PSU. The root-mean-square error of sea level elevation at Landsort measured data from Permanent Service for Mean Sea Level was 198 mm in 2005–2012. The model was able to reproduce the ice-covered areas as well as the interannual variability. All modelled eutrophication indicators—chlorophyll-a, oxygen, nitrate, and phosphate—followed the dominant seasonal cycles. The model simulated chl-a data more accurately in the southern Baltic Sea (RMSE = 0.9), and root-mean-square error was larger in the Gulf of Finland, 4 mg m-3. However, the model underestimated the phytoplankton biomass (chlorophyll-a concentration) in most parts of the Baltic Sea and overestimated the oxygen content compared to the observations which might be a consequence of the too low phytoplankton biomass.The main validation of the ocean and biochemisry component of the coupled model is provided in the report on the biogeochemical model setup for the Baltic Sea and its applications . Hydrodyhttp://nest.su.se), except North Baltic, but underestimated when compared to HELCOM data. Phosphate was overestimated in the Gulf of Bothnia and the Gulf of Finland; however, it was underestimated in the rest of the Baltic Sea. The excess phosphate in the Gulf of Finland is most probably causing the high Chlorophyll-a concentration here. The maximum values of chlorophyll-a in April 2013 are shown in The nitrate concentration was overestimated by the model when compared to BED data (http://marine.copernicus.eu/). The ice concentration data originated from the Swedish Ice Service (http://www.smhi.se/oceanografi/istjanst/havsis_en.php), and the radiometric satellite observations obtained using the sensors AVHRR, ATSR-1, ATSR-2, and AATSR were used in this study. These data were used to calculate the ice cover characteristics of the Baltic Sea for the ice season by interpolating the ice concentration data to the model grid. Areas with an ice concentration of <40% were considered ice-free (open water). Ice was considered to be present in the model grid cells if the ice concentration was ≥40%.Data on the daily ice concentrations over the Baltic Sea was provided by the Copernicus Marine Environment Monitoring Service .https://www.io-warnemuende.de/en_iowdb.html).The IOW data are available on request from the Oceanographic Database of IOW (IOWDB) . HELCOM uses indicators with quantitative threshold values to evaluate the progress towards the goal of achieving an acceptable environmental status of the Baltic Sea. The index is based on seasonal diatom and dinoflagellate biomass values and reflects dominance patterns in the biomass of phytoplankton spring bloom. It describes the relative percentage of diatoms within the total biomass and provides a proxy to estimate the entry of nutrients into the pelagic or benthic food web: if diatoms are dominant, their rapid sinking reduces the availability of food stock for zooplankton, but delivers abundant food to zoobenthos  kg/m3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities (kg/m(TIFF)Click here for additional data file.S2 Fig3) with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities (kg/m(TIFF)Click here for additional data file.S3 Fig3) with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities (kg/m(TIFF)Click here for additional data file.S4 Fig3) with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities (kg/m(TIFF)Click here for additional data file.S5 Fig3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m(TIFF)Click here for additional data file.S6 Fig3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m(TIFF)Click here for additional data file.S7 Fig3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m(TIFF)Click here for additional data file.S8 Fig3.(a) Diatoms (no ice run); (b) diatoms (ice run); (c) dinoflagellates (no ice run); (d) dinoflagellates (ice run); (e) and (f) are water densities with ice run and no ice run. The water density is defined as the sigma density [– 1000)] kg/m(TIFF)Click here for additional data file.S1 TableData from Baltic Nest, ICES and satellite data from Copernicus database.(DOCX)Click here for additional data file.S2 TablePhytoplankton events based on observations. Cases, which developed under ice cover, as illustrated in figures, have been marked with an asterisk. Stations location A  and B . Ice concentration for up to 10 days before measurements.(DOCX)Click here for additional data file. 26 Nov 2020PONE-D-20-34799Effects of sea ice and wind speed on Phytoplankton spring bloom in Central and Southern Baltic SeaPLOS ONEDear Dr. Pärn,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Both the reviewers have offered critical comments and have suggested major revision. The methodology followed and the inferences are also questioned. In view of such a review, though a major revision is suggested, authors need to convincingly answer the comments and take up much needed appropriate revision, for a fresh round of review.plosone@plos.org. 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The manuscript titled “Effects of sea ice and wind speed on Phytoplankton spring bloom in Central and Southern Baltic Sea” by Parn et al. describes a model study of the phytoplankton spring bloom composition in the Central and Southern Baltic Sea in relation to ice cover. Experiments with an established hydrodynamic–biogeochemical model (GETM + ERGOM) lead the authors to an interesting conclusion that in years with sea ice - dinoflagellates dominate in the phytoplankton biomass and, vice versa, in years with no sea ice - diatoms dominate. The authors then extrapolate that into the future when diatoms are expected to dominate as ice will almost certainly either disappear or become rare in the Southern and Central Baltic Sea due to climate change. These conclusions are certainly interesting and even intriguing. The explanations given by the authors make sense but it remains puzzling for the world ocean where many areas never experience ice but have with huge dinoflagellate blooms and, on the contrary, areas of the Arctic and Antarctic oceans with ice are often dominated by diatoms. It is difficult to understand why the Baltic Sea would be such a peculiar case. In my opinion the modeling study has been performed at high professional level and I cannot find any technical problems that would be producing those intriguing results. However, it is quite possible that the inherently crude parametrization of the dynamics of diatom and dinoflagellate populations leads to these model results. Natural diatom and dinoflagellate populations have huge variations in their characteristics, e.g. cell size, growth and sinking speed, vertical mobility, etc. and parameters fixed to some estimates may not lead to realistic outcomes of the model.The manuscript is definitely too long and looks more like a report. The text should be shortened and the flow of text improved. It has a fair share of typos and omissions. The following is just a sample.Line 26, Kili Bay is in Taiwan. Do you mean the Kiel Bay?Fig 1 does not seem to have a caption.Reviewer #2: Review of Pärn et al Effects of sea ice and wind speed on Phytoplankton spring bloom in Central and Southern Baltic Sea.The paper is a model study that investigates the spring bloom in the Baltic Sea, and in particular focuses on the shifting dominance between diatoms and dinoflagellates. There has been a shift over the past decades from diatom dominance towards more dinoflagellates during this period. The conclusion drawn from the paper suggests diatoms dominate the spring bloom when there is thick ice cover, whereas dinoflagellates dominate when the ice cover is thin. They also argue that diatoms will dominate when the ice cover is completely removed something that might take place in a warmer future.I am not a modeler and cannot go into the details behind the model, but I found the paper interesting and worthy of publication although I am not sure about the general conclusion and would like to see some revision in that respect.Getting the model to work with the slower growing dinoflagellates can be difficult, and it seems they had to increase the growth rate above what is published under optimal conditions for dinoflagellates. Also, large parts of the central Baltic Sea is very often without ice and still dinoflagellates seem to dominate, see e.g. Fig 4 in Klais et al 2011. So the distribution of dinoflagellate dominance extends from typically ice covered to not ice covered.What I think is something missing in the model is the fact that the spring bloom is driven by the inflow of freshwater creating the initial stratification rather than thermal stratification which requires longer time for the surface layer to warm up. I would suggest to play around with this in the model, or at the very least bring up the limitations in the model in the discussion as it clearly is not well able to capture the dinoflagellate dominance in the southern and central parts of the Baltic Sea that is typically ice free every winter.Minor commentsL 75: Could add a line explaining that diatoms are non-motile and sink quicker whereas dinoflagellates are motile and can position themselves vertically.L 79 Most of dinoflagellates sink in the form of cysts, otherwise vegitative cells tend to lyse before reaching the seafloor and still diatoms is probably the more quickly sinking group.L 81: This is only in the case of the resting cysts that are not decomposed in the sedimenthttps://doi.org/10.1016/j.jmarsys.2006.10.012). What is likely a factor is timing of requirement  and stratification. What is different in the Baltic Sea from many other coastal regions is the massive freshwater outflow at spring and salinity stratification rather than thermal stratification playing an effect (Stipa 2002 & 2004) which is important for the start of the spring bloom.L84-97: The difference in growth rate is the same between dia and dinos in the Baltic Sea : 482.Lipsewers, Tobias, Riina Klais, Maria Teresa Camarena-Gómez, and Kristian Spilling. "Effects of different plankton communities and spring bloom phases on seston C: N: P: Si: chl a ratios in the Baltic Sea." Marine Ecology Progress Series 644 (2020): 15-31.Camarena‐Gómez, María Teresa, Clara Ruiz‐González, Jonna Piiparinen, Tobias Lipsewers, Cristina Sobrino, Ramiro Logares, and Kristian Spilling. "Bacterioplankton dynamics driven by interannual and spatial variation in diatom and dinoflagellate spring bloom communities in the Baltic Sea." Limnology and Oceanography (2020).**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  14 Jan 2021Our sincere thanks for the relevant suggestions and comments that improve the quality of the manuscript and open up new perspectives. 1.1 Reviewer #1: These conclusions are certainly interesting and even intriguing. The explanations given by the authors make sense but it remains puzzling for the world ocean where many areas never experience ice but have with huge dinoflagellate blooms and, on the contrary, areas of the Arctic and Antarctic oceans with ice are often dominated by diatoms. It is difficult to understand why the Baltic Sea would be such a peculiar case.The focus of our manuscript is indeed on the influence of ice and wind. However, the mechanism of dominance of diatoms or dinoflagellates depends on many other factors such as water density , circulation characteristics (like upwelling), wind (water turbulent mixing), nutrient concentrations and balance, as well as phytoplankton physiology. Example, 3-4 days of wind silence (wind speed <5 m / s) can be very favorable for dinoflagellates.We strongly agree that there are open points for discussion and further research  and ecosystem model development are needed for parametrization of phytoplankton. 1.2 Reviewer #1: “ However, it is quite possible that the inherently crude parametrization of the dynamics of diatom and dinoflagellate populations leads to these model results. Natural diatom and dinoflagellate populations have huge variations in their characteristics, e.g. cell size, growth and sinking speed, vertical mobility, etc. and parameters fixed to some estimates may not lead to realistic outcomes of the model.”We fully agree with the reviewer that there are huge variations in the phytoplankton community. However, the model is inevitably a highly simplified representation of reality, where dominant types of phytoplankton are grouped according to similar characteristics (ref ERGOM papers). Updating the model according to up-to-date knowledge on phytoplankton ecology and physiology (including production of cysts), and possibly diversifying functional types of phytoplankton to include higher variability, are necessary to improve its performance in the future.1.3 Reviewer #1: The manuscript is definitely too long and looks more like a report.We would like to thank the reviewer for his comment and pointing this out. The paragraph “Ice Conditions during Spring Bloom” has now been removed from the paper to shorten its overall length.We have reformulated the discussions and have specified the scope of the paper.2.1 Reviewer #2: Getting the model to work with the slower growing dinoflagellates can be difficult, and it seems they had to increase the growth rate above what is published under optimal conditions for dinoflagellates.L123. 0.7 d-1 is likely too high, 0.4 d-1 is more realisticWe agree with the reviewer that the values of the phytoplankton parameters need further improvement and the development of the model is in progress. When we used 0.4 d-1 in the model, the dia/dino index in the results obtained was not a close match with the results of Wasmund [1]. However, after changing the value to 0.7 d-1, we were able to obtain results which were a closer match to the results of Wasmund.Therefore, we have used the value of the Ecological Regional Ocean Model (IOW ergom) 0.7 [2] which is used by the The Leibniz Institute for Baltic Sea Research, Warnemünde.2.2 Reviewer #2: “Also, large parts of the central Baltic Sea is very often without ice and still dinoflagellates seem to dominate, see e.g. Fig 4 in Klais et al 2011. So the distribution of dinoflagellate dominance extends from typically ice covered to not ice covered.” Diatoms need less light and have a higher growth rate in the model, so they are more dominant. According to the model, dinoflagellates can only grow if the concentration of diatoms is low enough. This allows more light on the sea surface leading to the dominance of dinoflagellates. This is a situation that can exist in the central parts of the Baltic Sea. Another situation that can occur is when diatoms sink deep from the surface. Such conditions occur when there is ice in the area or when the daily average wind speed is less than 5 m/s. The circulation is also important.In view of the foregoing, despite the central Baltic Sea often being without ice, dinoflagellates still seem to dominate because of the low wind speed that is not fully mixing the sea surface layers and ensuring that diatoms sink to the bottom. Also, the concentration of the diatoms is principally smaller  allowing the dinoflagellates to absorb light creating optimal conditions for their growth and dominance.2.3 Reviewer #2: “L483-486: There is ample evidence dinoflagellates dominate even under ice free winters so I do not buy this conclusion, a more interesting question is why the model gets it wrong.”The reviewer is correct that the algal spring bloom in the Baltic Sea was often characterised by the dominance of dinoflagellates over diatoms. However, in our experiments, in ice free open sea areas, often diatoms were found to dominate. On the other hand, the stratification caused in the water due to several variables would lead to a situation favouring the dominance of dinoflagellates, but this is potentially more important for coastal regions.We understand that the model is not perfect but it has been reliable in the experiments we have conducted so far. However, this paper would give impetus to further the development of the model. 2.4 Reviewer #2: “What I think is something missing in the model is the fact that the spring bloom is driven by the inflow of freshwater creating the initial stratification rather than thermal stratification which requires longer time for the surface layer to warm up.”Yes, freshwater runoff has importance for the spring bloom. Inflow of freshwater is considered in the model and it plays a role with regard to nutrient input and stratification. However, in our modelling experiment we did not analyse the effects of freshwater inputs separately, focusing on integrated change in the upper water column dynamics due to lack of ice. As we were only simulating a single spring bloom event, the effect of freshwater inputs might have been not pronounced, and would be more apparent in long-term simulations, which we are currently considering as a follow-up to this work.https://doi.org/10.1016/j.jmarsys.2006.10.012). 2.5 Reviewer #2: “L84-97: The difference in growth rate is the same between dia and dinos in the Baltic Sea  and stratification. What is different in the Baltic Sea from many other coastal regions is the massive freshwater outflow at spring and salinity stratification rather than thermal stratification playing an effect (Stipa 2002 & 2004) which is important for the start of the spring bloom.Our model does take into account the outflow of freshwater. However, our modelling experiment with and without ice cover demonstrates that presence/absence of ice as an important factor shaping the composition of phytoplankton spring bloom. We certainly agree that there are other factors at play which will influence these dynamics in the future, including changes in atmospheric forcing, runoff and nutrient balance. We have changed relevant parts of the text to clarify this. 2.7 Reviewer #2: “L 75: Could add a line explaining that diatoms are non-motile and sink quicker whereas dinoflagellates are motile and can position themselves vertically.”Thank you, we changed the sentence in Introduction:Wind is one of the factors governing vertical mixing within the euphotic layer  and in stagnant water, the diatoms, being non-motile and heavier, sink quickly, whereas the dinoflagellates are motile and can therefore position themselves vertically. 2.8 Reviewer #2: L313: In reality dinoflagellates are actually the dominating group at least in station 32.Phytoplankton composition has a great deal of variability, both in space and time. For example, on 14 and 15 March 2002, 14 measurements were made in a small sea area (data from Irina Olenina), 20⁰5’-21⁰01’; 55⁰39’-56⁰01’ (not too far from station 32). The mean biomass of the diatoms was 1024µG/L and the standard deviation was 880µG/L. The maximum value was 3425µG/L and the minimum was 71µG/L. The mean value of the dinoflagellates was 38µG/L. However, only a month later, the mean value of the dinoflagellates was 972µG/L, , while the mean value of the diatoms was 420µG/L . Therefore, it can be seen that one station cannot be fairly representative of which phytoplankton group is dominant during bloom when the measurements are taken only 1 or 2 times during spring.2.9 Reviewer #2: L440-41: no they actually swimAgreed; however, for simplicity, the model formulation does not take the ability of dinoflagellates to swim into account. We believe this assumption is justified given their slow speed of movement and the spatial resolution of the model applied in this study.2.10 Reviewer #2: L477-479: sounds like there was no spring bloom in the pastWe agree that the sentence was not clearly formulated. We have changed this sentence:1. In the past, at the beginning of spring, the sea ice was so thick that the lack of light limited the growth of the phytoplankton [50]. Therefore, phytoplankton spring blooms occurred later, in the middle or end of May. Diatoms dominated in the southern part of the Baltic Sea where sea ice was absent. References1. Wasmund N. The diatom/dinoflagellate index as an indicator of ecosystem changes in the baltic sea. 2. historical data for use in determination of good environmental status. Frontiers in Marine Science. 2017 Jun 8;4:153.2. Neumann T, Fennel W, Kremp C. Experimental simulations with an ecosystem model of the Baltic Sea: a nutrient load reduction experiment. Global biogeochemical cycles. 2002 Sep;16(3):7-1.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 1 Feb 2021Effects of sea ice and wind speed on Phytoplankton spring bloom in Central and Southern Baltic SeaPONE-D-20-34799R1Dear Dr. Pärn,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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To the Editor:Skeletal muscle wasting occurs in numerous degenerative diseases associated with high morbidity and mortality and poor capacity for independent living. Thus, it is a major economic burden to society.50 = 22.42 μM for ebselen‐treated myoblasts and IC50 = 19.93 mM for LiCl‐treated myoblasts. L‐690330 treatment did not induce cytotoxicity in C2C12 myoblasts or myotubes . Cell viability analysis in C2C12 myoblasts and myotubes indicated ICIncreased skeletal muscle wasting is associated with increased expression of E3 ubiquitin ligases, atrogin‐1 (MAFbx), and MuRF‐1 (TRIM63), which are targets of the master transcription factor, forkhead box O3 (FoxO3a) that is upregulated in muscle wasting.The effect of IMPase inhibition by ebselen in vivo was assessed in the mouse dexamethasone treatment model. Myo‐inositol concentration was increased in the gastrocnemius muscle of mice receiving dexamethasone and lowered in those receiving ebselen Figure . DexametIn conclusion, here in this study, we provide evidence that IMPase inhibition, modulation of myo‐inositol concentration, and repurposing of the clinically safe drug ebselen may be a novel drug development strategy for skeletal muscle wasting Figure . EbselenJi‐Hyung Lee and Hyun‐Jun Kim carried out cell‐based and animal experiments, and analyzed the data. Seon‐Wook Kim, and JungIn Um assisted Ji‐Hyung Lee with animal experiments. Da‐Woon Jung and Darren R. Williams designed the study and wrote the manuscript.Darren R. Williams, Da‐Woon Jung, Ji‐Hyung Lee, and Hyun‐Jun Kim have applied for a patent concerning the use of organoselenium compounds to treat skeletal muscle wasting.Supporting InformationClick here for additional data file.


The traditional perception that legumes would not be suitable for extrusion cooking is now completely outdated. In recent years, an increasing number of studies have been conducted to assess the behavior of various types of legume flours in extrusion cooking, proving that legumes have excellent potential for the production of extruded ready-to-eat foods by partially or totally replacing cereals. This review identifies the optimal processing conditions for legume-based and legume-added extruded foods, which allow the improvement of the expansion ratio and give the extrudates the spongy and crisp structure expected by consumers. In particular, the effect of the individual processing parameters on the physical-chemical and nutritional properties of the final product is highlighted. The extrusion cooking process, indeed, has a positive effect on nutritional characteristics, because it induces important modifications on starch and proteins, enhancing their digestibility, and reduces the content of trypsin inhibitors, lectins, phytic acid, and tannins, typically present in legumes. Therefore, the extrusion of legume flours is a viable strategy to improve their nutritional features while reducing home preparation time, so as to increase the consumption of these sustainable crops. Consumer demand for ready-to-eat foods is increasing due to the time-saving needs of the modern lifestyle. Extrusion cooking is a technique largely used for the production of several ready-to-eat products, such as crisp expanded snacks , breakfast cereals, instant soups, meat analogues and sport foods ,2,3. ExtLegumes are a good source of proteins , starch,Extrusion cooking technology is also known to reduce the levels of some anti-nutrients contained in legumes  such as Extrusion cooking therefore seems to be suitable for producing an array of ready-to-eat legume-added foods. This topic soon attracted the attention of researchers, and their interest increased over time. Indeed, over the past 10 years an increasing number of articles have been published containing the word combinations “extrusion” and “legume”, or “extrusion” and “pulse”, or “extrusion” and the name of a specific legume, as reported in the Scopus scientific database  Figure A. Among Despite several articles reporting the results of single studies regarding the behavior of various types of legume flours in extrusion cooking, there are no reviews available. In this framework, the purpose of this review is to identify the optimal processing conditions for each type of legume, and define the effect of processing parameters on the physical-chemical and nutritional properties of the extruded products.The development of the single-screw extruder for fast cooking and expanding corn and rice-based snacks dates back to 1946 in the US, followed by twin-screw extruders, introduced in the mid-1970s [In the initial part of the barrel (feeding zone), the raw material is conveyed and mixed by the rotating screw. Then, with the help of shear energy, the material is further kneaded and compressed (kneading zone) and, by friction and additional heating of the barrel, reaches its melting point and plasticizes, particularly in the final part of the machine (cooking zone) .As the plasticized starchy material exits from the die of the extruder, the air bubbles entrapped within the matrix expand due to instant pressure drop. In addition, with the extruded material being heated to temperatures above 100 °C, a moisture flash-off occurs at the exit of the extruder, further improving the puffing effect ,19. The The main parameters to be adjusted in the extrusion cooking process are the temperature, the screw speed and the moisture content of the fed ingredients. These parameters strongly influence the characteristics of the extruded product. Therefore, several studies have been carried out to compare sundry combinations of those parameters, in order to point out the optimal conditions for each type of legume . These sBeside the processing parameters, however, also the content of legume flour, its refining degree and particle size, as well as the type of legume, influence the expansion performance of the extrudates. Prior to extrusion, indeed, legumes are milled to flour either using the entire cotyledons or after removal of the hulls (split flours). Hull removal lowers the content of fibers and minerals but improves the expansion during extrusion . Fibers,2 at the conditions used in the extrusion process. It has to be taken into account, however, that the use of sodium bicarbonate causes an undesirable alkalization of the final product.In addition, there are differences in the attitude to extrusion of different legumes, based on their compositional features. Compared with lentil and chickpea flours, split yellow pea flour reaches an expansion ratio and bulk density similar to corn meal due to its higher starch and lower protein content . ChickpeExtrusion trials carried out using 100% faba bean flour, varying both the speed of the extruder screw (200 or 300 rpm) and the size of flour particles , while keeping constant the temperature of die zone at 140 °C, made it possible to point out that the optimal conditions involve high screw speed and a reduced particle size. In particular, the combination of 300 rpm and <0.5 mm resulted in the least hard, most expanded and crispest snack, with sensory properties similar to commercial extruded corn snacks , demonstA single-screw extruder, instead, was used to study the influence of moisture content, temperature and, again, screw speed, during the production of snacks composed of 100% cowpea flour . CombinaThe expansion was positively influenced by low moisture content, high die temperature and high screw speed, resulting in less dense and hard extrudates, having higher water absorption index (WAI), water soluble index (WSI) and organoleptic scores . TherefoIn lentil extrudates obtained by varying the die temperature , the screw speed  and the feed moisture  , the besInstead of producing 100% legume extrudates, several studies considered the partial replacement of cereals by legume flours, to compensate essential amino acid deficiencies and improve the nutritional features of cereal-based extruded foods. Moreover, these studies focused on pointing out the optimal processing conditions, and, as expected, generally confirmed the findings of extrusion trials where legumes reached 100%.The effect of feed moisture content (12–16%), barrel temperature (90–110 °C) and screw speed (100–200 rpm) was evaluated during the production of rice-based extrudates containing, among other ingredients, about 9% green pea flour. A twin-screw extruder was used. Optimal products were obtained at high temperature, high screw speed and low moisture content. In particular, 110 °C, 200 rpm and 12% moisture were the best processing conditions, which made it possible to obtain snacks with a high expansion ratio .Similarly, in maize-based extruded snacks containing 30% black bean flour (0.4 mm particle size), the effect of feed moisture content (from 15 to 25%), and screw speed (from 50 to 240 rpm), was evaluated. The best processing conditions, able to ensure high expansion ratio, were 15% moisture and 238 rpm. A single-screw extruder was used . The effL*) value, guided the selection of the optimal conditions. The most influential parameter on color, besides the amount of legumes, was the die head temperature. The optimal processing conditions were identified as: 24% moisture content, 171 °C die temperature and 104 rpm screw speed, with 19% legume flour [a*) and yellowness (b*) increased and lightness decreased as die temperature raised [a* and b* indices increase also as moisture content increases, whereas L* decreases [Extrusion cooking also has an effect on color, which is an important feature for the acceptance of food products in general, including the extruded ones. A study carried out specifically to point out the influence of these technologies on color was made by extruding millet-based blends containing 12–28% pigeon pea flour by a single-screw laboratory extruder. Moisture content was in the range of 12–24%, and the extruder operated at different die temperatures (160–200 °C) and screw speeds (100–140 rpm). The evaluation of overall acceptability, which was higher for extrudates having higher lightness  sufficiently similar to controls.In particular, the addition of germinated chickpea flour to corn-based extrudates was proposed, by using a single-screw extruder operating at 180 °C and 250 rpm screw speed, with 16% moisture . A germiThe fortification with either lentil, pea, or chickpea flour was evaluated to improve the nutritional profile of wheat-based extrudates. Levels of addition up to 15% were found to be optimal. A single-screw extruder was used, operating at 180 °C, 210 rpm and 12% moisture .Blends of maize starch with either navy or red bean flours added at levels of 15, 30 or 45% were tested to produce fortified puffed snacks. A twin-screw extruder was used, set at 150 rpm and 160 °C. Levels of up to 30% bean flour resulted in the best extrudates, as crispy as those obtained from maize starch alone but with better nutritional value .After having tested levels of 25, 50 and 75%, the best amount of everlasting pea flour to be added to wheat flour for preparing extruded vegetarian snacks was 50%, able to balance the structural and nutritional properties. A twin-screw extruder was used .In addition to snacks, the production of extruded powders such as baby foods to be reconstituted with water is another common application of extrusion cooking. These foods have less stringent expansion requirements than snacks, therefore can easily incorporate high levels of extruded legume flours. Trials carried out to produce a nutritionally balanced infant food highlighted that the best level of addition of extruded chickpea flour to nixtamalized corn flour was as high as 73%. Such a level ensured a good content of proteins and available lysine, while keeping the sensory features acceptable. The extrusion of chickpea flour was carried out by a single-screw extruder, operating at 150.5 °C and 190.5 rpm .The structural characteristics of an extruded food product, such as bulk density (BD) and hardness, are related to the size and number of gas bubbles developed within the expanded rigid starchy matrix. These characteristics, as well as color, are influenced by the level of legume addition and by the parameters of the extrusion process. In Hardness is related to the acceptability of the final product, less hard and more expanded extrudates being the most appreciated. In addition, the BD of extruded foods should be as low as possible, indicating a proper increase in volume of the extrudate. Hardness and BD are positively related to each other ,41,42,43Extrusion cooking also has a relevant effect on the pasting properties of starch , resultiFor infant foods and instant soups, a high degree of gelatinization of the starch granules should be achieved. Being pre-gelatinized, these extruded foods are able to form a viscous paste or a thick solution when room temperature, or slightly warm, water is added. In addition, as extruded flours are already gelatinized, they hydrate faster than the raw flour and are highly digestible. Gelatinized starches can also be used as food thickening agents. Therefore, the aim of extrusion cooking is to gelatinize the starch granules, with a limited dextrinization. If an intense shearing action occurs during extrusion, macromolecules may break down to smaller units, affecting the gelatinization process and, consequently, the viscosity. Low shear rate, on the contrary, makes it possible to reach higher viscosity when the extruded flour is rehydrated . LimitedPasting properties of starch are measured by using the micro visco-amylograph (MVA). In particular, extruded flours show higher initial viscosity (which is a desirable characteristic for instant flours) than raw flours . SubsequFinally, the increase in viscosity at the end of the cooling phase of the MVA analysis is due to the retrogradation of gelatinized starch. Unprocessed flours show a higher degree of retrogradation than extruded ones, which have undergone a relevant thermal and mechanical degradation ,55. TherAnother parameter related to starch is the WAI , which mWSI is related to the presence of water-soluble molecules deriving from polymer breakdown induced by extrusion. It is used as an indicator of the starch degradation. High WSI results in sticky extrudates . WSI incThe extrusion cooking process has a positive effect on the nutritional characteristics of the end-products, because it induces important modifications on starch and proteins, enhancing their digestibility, and reduces the content of trypsin inhibitors, lectins, phytic acid, and tannins.The thermal treatment associated with extrusion cooking is effective in improving protein and starch digestibility compared with traditional thermal processes. Lysine loss may however occur, depending on the extrusion conditions. In particular, starch and protein digestibility take advantage of temperature and feed moisture increases, whereas lysine loss increases as the temperature raises .Humans cannot easily digest non-gelatinized starch . ExtrusiGenerally, the digestibility of proteins also increases with extrusion cooking ,25,31,61The improvement of protein digestibility is probably mostly due to the reduction in trypsin inhibitors, which interfere with proteolysis and are heat-labile ,66,68,69Legumes contain several anti-nutritional factors, such as trypsin inhibitors, lectins, tannins, and phytates . Variati−1 have been reported in chickpeas, 6–15 TIU mg−1 in peas, 5–10 TIU mg−1 in faba beans and 3–8 TIU mg−1 in lentils [Levels of 15–19 trypsin inhibitor units (TIU) mg lentils ,76. The  lentils . A 95% d lentils . Signifi lentils .Lectins bind sugar branches of the epithelial surface proteins of the digestive tract, resulting in a disruption of the barrier function, which hampers the absorption of nutrients in the gut . Some le−1) among legumes, followed by beans (0.3–12.6 g kg−1), cowpeas (1.4–10.2 g kg−1), peas (0.6–10.5 g kg−1) and chickpeas (0.6–2.7 g kg−1) [Tannins have the ability to complex and precipitate proteins in aqueous solutions. The condensed tannins of some legumes, such as faba beans, reduce the digestibility of proteins ,82. Faba g kg−1) . The con g kg−1) ,84.Inositol hexaphosphate (IP6), or phytate, is the most abundant inositol phosphate in legume extrudates . IP6 andPhenolic compounds are abundant both in the usual raw materials for extrusion cooking, i.e., cereals, and in legumes. The extrusion conditions influence the overall impact on phenolics: the adoption of low moisture <14%) and low temperature (<140 °C) can help to retain them % and low,89,90,91A marked decrease in total tocopherols content after extrusion was reported . High exRegarding the antioxidant activity of legume-added extruded foods, it has to be highlighted that it is not only due to the presence of antioxidant bioactive compounds contributed by the extruded raw materials, as modified by the extrusion process, but also to antioxidant compounds, such as Maillard reaction products, which may arise from the thermal modifications related to the extrusion cooking process . TherefoOther compounds influenced by extrusion cooking are the α-galactosides, such as raffinose, stachyose, and verbascose. These compounds cause flatulence due to the lack of α-galactosidase in the human intestinal mucosa, but at the same time they have a prebiotic effect because are easily fermented by the colonic flora, resulting in the production of short chain fatty acids that stimulate bifidobacterial growth ,103,104.From the technical point of view, extruding cereal–legume formulations could be therefore a good strategy to produce shelf-stable ready-to-eat nutritious food products. This is of particular importance in geographic areas where is needed to relief malnutrition. However, the cost of purchasing and operating the extruders may be not affordable in developing countries . A solutLegumes have shown excellent potential for the production of extruded ready-to-eat foods by partially or totally replacing cereals. The traditional perception that legumes would not be suitable for extrusion cooking is now completely outdated. By accurately selecting the optimal processing parameters, it is possible to improve the expansion ratio and give the extrudates the spongy and crisp structure expected by consumers. Moreover, the addition of legume flours improves the nutritional value of cereal-based end-products by increasing the content of essential aminoacids, fibers, proteins, and micronutrients, while extrusion cooking inactivates the nutritionally undesirable compounds typically present in legumes.Therefore, the extrusion of legumes is a viable strategy to add value to underexploited legumes and reduce home preparation time, so as to increase the consumption of these sustainable crops.


N=12) were scanned while performing an inhibitory control task known to elicit inhibition-related activity in the r.IFG. Using a randomized, counterbalanced cross-over design, participants then received TBS over two visits: excitatory (iTBS) on one visit and inhibitory (cTBS) TBS on the other visit. The effects of each TBS condition on subsequent inhibitory control task performance were examined. A significant condition x time interaction was identified on trials requiring inhibitory control  = 7.27, p = .022, D = 1.63). iTBS improved inhibitory control, whereas cTBS impaired inhibitory control. Brain stimulation did not influence performance in control conditions including novelty detection and response execution. This is the first study to demonstrate that non-invasive neural stimulation using iTBS to the r.IFG enhances baseline inhibitory control among individuals with a SUD. Further research is needed to directly examine the potential parametric effects of TBS on corticothalamic tbFC in individuals with a SUD.Nicotine addiction, like other substance use disorders (SUD’s), is associated with deficits in prefrontal mediated inhibitory control. The strength of inhibitory control task-based functional connectivity (tbFC) between the right inferior frontal gyrus (r.IFG) and thalamus  mediates the association between successful inhibition and smoking relapse vulnerability. However, the potential efficacy of theta burst stimulation (TBS) to the r.IFG, a treatment known to alter clinical symptoms among neuropsychiatric patients, has not been reported in a SUD population. This study utilized fMRI guided neuronavigation to examine the effects of TBS on inhibitory control among nicotine dependent individuals. Participants ( Substance use disorders (SUDs) are characterized by significant disruptions to multiple forms of executive function  and the The extant literature converges on a role of the right inferior frontal gyrus (r.IFG) as a key prefrontal region involved in the initiation of a “stop” command , 14. IndcTBS)  can be used to modulate neuronal function within a focused area, as well as functionally connected brain regions. TBS has a good safety profile , 20 and cTBS)  which informance , 26, butformance . HoweverTherefore, given the lack of known medications for treating inhibitory control deficits among smokers and the unknown potential of iTBS for modulating inhibitory control among individuals with an SUD, the goal of the current study was to examine the effects of TBS on inhibitory control task performance in smokers. To this end, r.IFG regions involved in inhibitory control were identified for each individual (N = 12) using fMRI data acquired during performance of a validated IC test. Using a crossover design, we examined the effect of both excitatory (iTBS) and inhibitory (cTBS) TBS to the r.IFG on inhibitory control task performance in each individual. We hypothesized that the excitatory stimulation would improve, and that the inhibitory would disrupt, IC task performance. The effects of TBS on novelty detection and generic motor responding were also assessed.M = 31.42 years ± 7.39, three females, education: M = 13.00 years ± 1.35) nicotine dependent (FTND: M = 5.42 ± 2.19) smokers, smoking on average 16.42 ± 4.52 cigarettes per day for 13.83 ± 7.57 years completed the study . FTND scores ranged between 1 (low) and 9 (high) ; and a uThe validated event-related IC task , 28 inclImaging was performed on a 3T Siemens Prisma scanner: a high-resolution 3D MPRAGE anatomical sequence was acquired , NoGoincorrect (error of commission), RareGocorrect (novel-target detection), RareGoincorrect (novel-target error of ommission), and Goincorrect (error of omission). Each event was modeled as a delta regressor (onset dur. = 0) and convolved with a canonical hemodynamic response function. Motion was removed through rigid body rotation and translation and parameters included as covariates. A high-pass filter  was applied to remove slow signal drift. To idenitfy successful IC-BOLD response, controlling for novelty detection, a NOGOcorrect–RareGocorrect contrast image (IC-contrast) was generated  were created. Finally, the r.IFG was set as the spatial target, and the target coil trajectory was set. The same setup parameters were used across each of the two TBS visits.Following the baseline scan and ROI identification procedure performed by BF Table 2,Participants were randomized to receive TBS to the r.IFG on two separate experimental visits: iTBS on one visit; cTBS on one visit—counterbalanced across participants.Standard procedures were used to determine the participants resting motor threshold (RMT) using parameter estimation by sequential testing (PEST) procedures .The total duration of the iTBS protocol  was 190 The total duration of the cTBS protocol  was 34 sEach of three task events of interest were modeled separately with a 2  x 2  repeated measures analysis of covariance (rmANCOVA). Though nicotine dependence severity, as measured by the FTND was not significantly associated with inhibitory control performance at any one of the measurement time-points , FTND score was entered as a covariate in the rmANOVA to control for variability in this small sample. Significance was defined at α=.05. Given research suggesting cannabis use may account for significant variance in the relationship between nicotine use and inhibitory control , and uriBlind success. The double-blind procedure used in the study was successful. Neither the participants  nor the researcher administering the behavioral assessments  were able to identify TBS conditions.p =.022, η2 =.421). iTBS improved inhibitory control whereas cTBS impaired inhibitory control ; omitting FTND in the rmANOVA resulted in a reduction in the effect size (η2 =.193).A significant condition x time interaction was identified on inhibitory control trials . As demThe current study sought to compare the effects of iTBS vs. an active condition—cTBS and baseline in order to test the hypothesis that TBS to the rIFG may causally modulate IC in a stimulation-dependent manner . Though a sham condition was considered, we concluded that the sham condition adds little benefit above and beyond the active—cTBS condition (and baseline) for the purpose of testing the study hypothesis. However, future larger-scale clinical studies evaluating iTBS for improving IC and clinical outcomes among individuals with tobacco use disorder will benefit from including a sham condition as a comparator condition. Despite the limitations of the current study, including a relatively small N, brief testing period and demographic and baseline variability among the study sample, the current study results provide novel and promising early-phase evidence that rIFG iTBS may help improve IC among individuals with tobacco use disorder.The data that support the findings of this study are available from the corresponding author upon reasonable request.The studies involving human participants were reviewed and approved by the Medical University of South Carolina IRB. The patients/participants provided their written informed consent to participate in this study.BF designed the study. RN-N, MG, and PM oversaw data collection and data analysis. BF was responsible for data interpretation. RN-N and BF developed the manuscript.This research was supported by NIDA grants R01DA033459, R01DA038700 and UG3DA048510 (BF); in part by pilot research funding, Hollings Cancer Center’s Cancer Center Support Grant P30CA138313 at the Medical University of South Carolina.BF is a consultant for Promentis Pharmaceuticals, Inc. for work unrelated to the content of the manuscript.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Overall, we have corroborated phosphorylation sites at the intersection between MET and the DDR signaling networks, and suggest that these represent a class of proteins at the interface between oncogene‐driven proliferation and genomic stability.Increasing evidence suggests that interference with growth factor receptor tyrosine kinase (RTK) signaling can affect DNA damage response (DDR) networks, with a consequent impact on cellular responses to DNA‐damaging agents widely used in cancer treatment. In that respect, the MET RTK is deregulated in abundance and/or activity in a variety of human tumors. Using two proteomic techniques, we explored how disrupting MET signaling modulates global cellular phosphorylation response to ionizing radiation (IR). Following an immunoaffinity‐based phosphoproteomic discovery survey, we selected candidate phosphorylation sites for extensive characterization by targeted proteomics focusing on phosphorylation sites in both signaling networks. Several substrates of the DDR were confirmed to be modulated by sequential MET inhibition and IR, or MET inhibition alone. Upon combined treatment, for two substrates, NUMA1 S395 and CHEK1 S345, the gain and loss of phosphorylation, respectively, were recapitulated using  Interference with the MET receptor tyrosine kinase signaling affects the cellular DNA damage response (DDR) to ionizing radiation. Using two proteomic techniques to explore phosphorylation signaling networks, we identified several substrates downstream of MET and the DDR that were modulated by sequential MET inhibition and IR, or MET inhibition alone. ATMataxia telangiectasia mutatedATRataxia telangiectasia and Rad3‐related proteinCDKcell cycle‐dependent kinaseCHEK1checkpoint kinase 1CHEK2checkpoint kinase 2DDADNA‐damaging agentDDRDNA damage responseDNA‐PKcscatalytic subunit of the DNA‐dependent protein kinaseERKextracellular signal‐regulated kinaseGOgene ontologyIHCimmunohistochemistryIRionizing radiationKSRskinase–substrate relationshipsMAPKmitogen‐activated protein kinaseMETiMET inhibitor EMD1214063MSmass spectrometrymTORmammalian target of rapamycinPI3Kphosphoinositide 3‐kinasePIKKsphosphatidylinositol 3‐kinase‐related kinasesRSKsribosomal protein S6 kinasesRTKreceptor tyrosine kinaseSRMselected reaction monitoringWBwestern blotting1et al., et al., et al., The DNA damage response (DDR) involves intricate signaling networks of checkpoint and repair pathways that respond to various forms of genotoxic stress to restore and sustain genome integrity or promote cellular death. The cellular response to genotoxic stress is largely governed by three members of the family of phosphatidylinositol 3‐kinase‐related kinases (PIKKs), ataxia telangiectasia mutated (ATM), ataxia telangiectasia and Rad3‐related protein (ATR), and the catalytic subunit of DNA‐dependent protein kinase (DNA‐PKcs or PRKDC) . Detroit 562 cells were maintained in MEM (Sigma‐Aldrich) supplemented with 10% FCS, nonessential amino acid solution , and antibiotic/antimycotic solution.Human gastric carcinoma cell line GTL‐16 was provided by P. Comoglio , nonsmall cell lung cancer cell line EBC‐1 by S. Giordano , and pharyngeal carcinoma line Detroit 562 was purchased from American Type Culture Collection . GTL‐16 and EBC‐1 cells were cultured in RPMI  supplemented with 5% and 10% FCS , respectively, and an antibiotic/antimycotic solution , VE‐821 (ATR inhibitor), KU57788 (PRKDC inhibitor), AZD6244 (ERK inhibitor), and AZD5363  were used at a final concentration (f.c.) of 10 µm. GDC0941 [PI3K inhibitor ] was used at f.c. of 1 µm. Inhibitors were dissolved in DMSO, and working solutions were prepared freshly and remained in the media for the duration of the respective experiment.The MET small molecule inhibitor tepotinib , NUMA1 Ser395 (#3429), phospho‐Ser/Thr‐Gln‐Gly (#6966), phospho‐Ser‐Gln (#9607), phospho‐Thr(Asp/Glu)X(Asp/Glu) (#BL4176), phospho‐Thr‐X‐Arg (#2351), CHEK1 Ser345 (#2341), CHEK1 , p90RSK Ser380 (#9335), PathScan®  Multiplex Western Cocktail I , cleaved caspase‐8 , cleaved caspase‐3 , 53BP1 (#4937), ACIN1 (#4934), cdc2 (#9116T), cell cycle‐dependent kinase 2 , H2AX (#7631S), 11 (#8967), SMC3 (#5696), and MET (#8198S and #4560) antibodies were all obtained from Cell Signaling Technology . Histone H2AX Ser139 (#05‐636), ATM Ser1981 (#05‐740), histone H3 Ser10 (#06‐570), and β‐actin (#MAB1501) antibodies were obtained from Merck Millipore Corporation . TIF1B (KAP1) Ser824 (#A300‐767A) antibody was purchased from Bethyl Laboratories, Inc. . SMC3 Ser1083 (#NB100‐653) and ATM (#NB100‐309) antibodies were obtained from Novus Biologicals .2.4in vitro studies, cells were irradiated (single dose of 10 Gy) using a 137Cs source  at a dose rate of 0.86 Gy·min−1.In all 2.5imagej (http://imagej.nih.gov/ij/). The area of treated cells was normalized to the area of nontreated controls. Experiments were performed three times. Statistical significance was calculated with the graphpad prism Software  using a two‐way ANOVA test .Cells were seeded in 24‐well plates  and were treated with EMD1214063 (24h after seeding) and irradiated (48h after seeding) as indicated. Control samples were treated with DMSO at the corresponding time points. Seventy‐two hours after irradiation, the cells were fixed and stained with 2% crystal violet dissolved in acetic acid/methanol (2:1) for 30min at room temperature. The dye was removed, and the plates were washed with water. The plates were scanned, and cell density was calculated using 2.6m HEPES (pH 8.0), 9.0m urea, 1mm sodium orthovanadate, 2.5mm sodium pyrophosphate, and 1mm β‐glycerol‐phosphate. Lysates were sonicated and cell extracts cleared by centrifugation. Total protein concentration was determined using the Bio‐Rad protein quantification reagent . Total protein extracts (20–100µg) were resolved by SDS/PAGE, transferred onto PVDF membranes , and blotted by standard procedures. Western blots for GTL‐16 and EBC‐1 cells using phospho‐Ser/Thr‐Gln‐Gly and Phospho‐Ser‐Gln motif antibodies along with β‐actin and MET Tyr1234&1235 were performed by Cell Signaling Technology (KinomeView Service).Cells were lysed in a buffer containing 20m2.7et al., imagej software (http://imagej.nih.gov/ij/). Statistical analysis was performed with the GraphPad Prism Software using one‐way ANOVA test .Tissue samples were cut into 5‐μm sections using Leica Biosystems CM 3050S Research Cryostat , and immunohistochemistry (IHC) was performed as described (Francica 2.86 cells injected subcutaneously on the right flank) were grown in 12‐week‐old female Rag2 common gamma‐null mice , and the growth of the tumors was regularly monitored by caliper measurements. Once tumors had reached a size of 300 mm3 (day 0), mice were randomly divided into four treatment groups: vehicle control , tepotinib only , IR only (one single 6 Gy dose on day 3), and combination of tepotinib with IR. Radiation was delivered locally using the XStrahl 150 . Tumors were harvested 24 h after the last tepotinib treatment, frozen in Tissue‐Tek optimum cutting temperature medium , and stored at −20 °C. Animal experiments were conducted in strict compliance with Swiss Federal guidelines and have been approved by Federal Food Safety and Veterinary Office.EBC‐1 xenografts , protease inhibitor cocktail , and phosphatase inhibitor cocktails 2 and 3 (Sigma‐Aldrich), and the resulting extracts were spun for 10min at 37°C, 150 g, Bicinchoninic acid protein assay  was used for determining protein concentrations. Lysates were reduced with tris(2‐carboxyethyl)phosphine (Sigma‐Aldrich), alkylated with iodoacetamide (Sigma‐Aldrich), and digested over night at 37°C with sequencing‐grade modified trypsin  at a protein‐to‐enzyme ratio of 50:1. Peptides were desalted on SEP‐PAK C18 cartridge  and dried under vacuum. Phosphopeptides were isolated from 1 to 1.8mg of total peptide mass using TiO2 by a protocol modified from Bodenmiller et al.  and solvent B . The instrument was operated in scheduled positive SRM mode at a unit resolution  for both Q1 and Q3 analyzers. Unless further optimized, collision energies (CEs) were calculated according to the formulas: CE = 0.044 × m/z precursor + 5.5 and CE = 0.051 × m/z precursor + 0.55, for doubly and triply charged precursor ions, respectively.Peptide samples were analyzed on a 5500QTRAP hybrid triple quadrupole/ion trap mass spectrometer  equipped with a nanoelectrospray ion source. Chromatographic separation was performed by a NanoLC AS2 (SCIEX) coupled to a 15‐cm (75 μm ID) fused silica emitter , self‐packed with ProntoSIL C18 AQ 3 μm resin . Peptides were separated at a flow rate of 300 nL·min2.10.3et al., et al., Synthetic phosphopeptides (Thermo Scientific) labeled with heavy isotopes at the C‐terminal Lys (+8 Da) or Arg   , which contained a phosphopeptide corresponding to PRKDC itself. Last, modulated by METi alone, cluster 3 was enriched for CDK2 and RPS6KB1 . Of note, few substrates were predicted or known for these kinases in our dataset, but these included RB1, a substrate of CDKs, and RPS6, a substrate of ribosomal protein S6 kinases (RSKs). To visualize the relationships between the enriched kinases and the respectively mediated phosphorylation sites, we generated a network of substrates connected to the predicted kinases enriched in this dataset or to their known upstream kinases  and these assays are then used to selectively detect and quantify dozens of such peptides of interest in multiple biological samples  were harvested following METi alone, IR alone, or a combination of treatments. By employing this diverse set of cell line models, we aimed to identify the signaling nodes and phosphorylations that are commonly regulated by MET signaling. Cells were collected at two time points (1 and 8 h post‐IR) to possibly distinguish between the early and the late DDR signaling elicited by IR alone or in combination with METi. Altogether, in 54 samples , 60 phosphopeptides were quantified with heavy reference standard peptides Fig.  and two et al., et al., The three cell lines responded similarly to IR Fig. , as measet al., While the response to IR was similar across all three cell lines, EBC‐1 and GTL‐16 responded differently to the combination of METi and IR Fig. . By usin3.3et al., et al., As aforementioned, the discovery data suggested that MET inhibition leads to downregulation of CDKs and RSKs, but the data did not include enough phosphorylations to select from for SRM Fig. . TherefoThe downregulation of phosphopeptides mapped to RPS6 and IF4B observed in the discovery data suggested downregulation of RSKs upon MET inhibition. This is likely due to the downregulation of one or numerous signaling networks governed by MET in these cell lines, such as the SRC‐PI3K‐AKT1‐mTOR, ERKs, JNKs, STAT. Extending on the downregulation of RPS6 phosphorylation analyzed by WB, we found by SRM that several signaling networks are modulated in EBC‐1 and GTL‐16, but not in Detroit 562 cells. In both EBC‐1 and GTL‐16 cells, numerous phosphorylations measured along the SRC‐PI3K‐AKT1‐mTOR network were downregulated by METi. From the discovery data, IF4B S422 was confirmed as downregulated by METi Fig. , along wImportantly, none of these surrogate phosphorylations was modulated by IR in these cell lines. Yet, the inhibition of proliferation and metabolism networks, as observed here, can lead to indirect effects on the response to IR, by proteins that are regulated by proliferation but play a role in the DDR (and thus act as synergistic ‘platforms’). In EBC‐1 cells, based on SRM data, we could suggest several such phosphorylations—CHEK1 S280 (by RSKs), ATRIP S224 (by CDK2), and JUN S73 (by MAPKs). Such phosphorylations highlight the differences between EBC‐1 and GTL‐16 cells in response to METi, while other phosphorylations, such as NUMA1 S395, highlight their common response to prolonged METi followed by IR. The distinct lack of downstream response in Detroit 562 cells to METi further asserts the differences between these MET‐overexpressing cells.3.4et al., Thus far, in the described experiments samples were all collected after a total 24 h of METi treatment . We aimed to further investigate the time dependency of the METi‐modulated phosphoproteome upon IR. In addition, based on the network analysis, as well as our previous data . In addition to these conditions, cells that were pretreated with METi were treated also with ATMi for 1 h before IR to assess ATM dependency of the regulated phosphopeptides. In total, 67 phosphopeptides were quantified with heavy reference standard peptides in this second SRM dataset. Twelve phosphopeptides were added based on known substrates of ATM, CDKs, and RSKs, and five phosphopeptides were removed , local irradiation (a single dose of 6 Gy on day 3), or the combination of these two modalities. Following these treatments, the levels of MET Y1234&5, NUMA S395 , and those downregulated by METi (as exemplified by CHEK1 S345). Last, we aimed to examine in vivo settings and would be thus considered as potential markers for the MET‐DDR crosstalk in the context of future translational applications.In summary, in the current study we have identified and validated phosphorylation changes associated with signaling networks at the intersection between MET and the DDR. Out of the multiple phosphorylations characterized by SRM, WB, and IHC, several candidates were demonstrated to be modulated by the sequential MET inhibition and IR. Regulation of two selected specific sites could be reproduced in 4et al., et al., et al., et al., et al., et al., Previous studies provided experimental evidence supporting a role for MET in the DDR  or as an early readout of an event to come (such as apoptosis). Accordingly, the combined treatment led to a rapid reduction in a proliferation marker, followed by an apoptotic response that was pronounced at later time points Figs . One celet al., et al., de novo pyrimidine synthesis from glutamine  which can also have roles at the intersection between cell cycle and DNA damage: RSK activity has been shown to modulate CHEK1 activity and the G2 cell cycle checkpoint by phosphorylation of CHEK1 on S280 *QG (A), T*(D/E)X(D/E) (B), and T*XR (C). Arrows are pointing at some prominent phosphorylation changes of these substrates. MET autophosphorylation was blotted separately as a control for MET inhibition, and duplicated here from Fig. 1A. Enrichment of GO terms for all regulated proteins (D) and clusters 1 and 2 (E).Click here for additional data file.Fig. S2. Network of KSRs, in which edges represent KSRs predicted by networKIN (dashed), and KSRs known from PSP (solid). The network was restricted to the predicted kinases and their respective known kinases (where applicable), and organized by the clusters . The regulated phosphorylation sites are illustrated with the fold changes as bars and colored based on their cluster association.Click here for additional data file.Fig. S3. (A) Regulation of phosphorylation of MET and its downstream signaling molecules in EBC‐1 cells upon inhibiting MET, AKT , PI3K  or ERK  with or without IR  was assessed by WB. β Actin was used as a loading control. (B) Histone H3 Ser10 phosphorylation following METi (16 h pretreatment prior to IR) alone and in combination with IR  in EBC‐1 and GTL‐16 cells. β Actin was used as a loading control. (C) Total protein levels of MET, 53BP1, CDK2, SMC3 and β Actin (used as a loading control) upon METi (24 h), IR  and their combination  in EBC‐1, GTL‐16 and Detroit 562 cells.Click here for additional data file.Fig. S4. Modulation of selected DDR‐related phosphorylations in EBC‐1 cells upon 3 h of pretreatment by METi prior IR (10 Gy) (A) and upon ATM inhibition  prior or post‐IR in combination with METi (3 h pretreatment) (B). The cells were lysed 3 h post‐IR, β Actin was used as a loading control. (C) Time‐dependent phosphorylation of NUMA1 Ser395 following METi treatment  in EBC‐1 cells. (D) Cleaved caspase‐3 levels following METi (16 h pretreatment prior to IR) alone and in combination with IR  in EBC‐1 and GTL‐16 cells. Staurosporine treatment (f.c. 1 µm for 17 h) was used as a positive control and β Actin was employed as a loading control. (E) Log2FC per condition are presented in comparison to control sample in EBC‐1 cells. Selected phosphorylation events were assessed in EBC‐1 cells upon IR (1 or 8 h) with or without METi and/or ATMi pretreatment as compared to untreated condition.Click here for additional data file.Table S1. MaxQuant and Perseus results for the two immunoaffinity experiments.Click here for additional data file.Table S2. PTMScan motifs final dataset used for analysis, including clustering results.Click here for additional data file.Table S3. Kinase prediction enrichment table.Click here for additional data file.Table S4. List of phosphopeptides measured by SRM with the statistical results.Click here for additional data file.


In this study, we developed a mutant 3CLpro (T25G) with an expanded S1′ space that demonstrates 43.5-fold better kcat/Km compared with wild-type in cleaving substrates with a larger Met at P1′ and is suitable for tag removal from recombinant fusion proteins. Two vectors for expressing fusion proteins with the T25G recognition site  in Escherichia coli and yeast were constructed. Identical cloning sites were used in these vectors for parallel cloning. PstI was chosen as a 5′ cloning site because it overlapped the nucleotide sequence encoding the protease site and avoided addition of extra amino acids at the N terminus of recombinant proteins. 3CLpro (T25G) was found to have a 3-fold improvement over TEVpro in tag cleavage at each respective preferred cleavage site.A 3C-like protease (3CL A 3C-liins pp1a 86 kDa any at P1′ . As evidnd pp1ab 90 kDa fopro can undergo auto-processing , and tobacco etch virus protease (TEVpro). Moreover, we have constructed two vectors, using prokaryotic and eukaryotic hosts, which contained the nucleotides encoding the T25G recognition site AVLQ↓M between the tags and the N-terminal Met of the target proteins. In these vectors, PstI (CTGCAG) was chosen as a 5′ cloning site, since its sequence overlapped the nucleotide sequence (GCGGTGCTGCAG) encoding the protease recognition site. Identical 5′-PstI/3′-XhoI cloning sites in these vectors were used to allow sticky-end DNA fragments of the target genes generated by PCR . To serve as a control, the UPPs fusion protein with AAAQ instead of AVLQ was also expressed.The UPPs-encoding gene  was emplFor expressing EGFP fusion proteins in yeast, primers were used to generate a PCR product that was ligated into pHTPY7, which was modified from pPICZαA (Invitrogen) by incorporating nucleotides encoding a starch binding domain (SBD)  and AVLQpro for 90 min at 37°C. For time course measurements, the fusion proteins (5.4 µM each) were treated with T25G (0.1 µM) at 37°C. The reactions were stopped by 2% trifluoroacetic acid after appropriate time periods and analyzed by SDS-PAGE. For comparing the tag cleavage efficiency of T25G and TEVpro (Invitrogen), the fusion proteins  were treated with 0.1 µM T25G and TEVpro at 37°C, respectively and then analyzed by SDS-PAGE.The UPPs and EGFP fusion proteins were purified using NiNTA columns. To examine the tag cleavage reactions, the purified fusion proteins (5.4 µM each) were treated with 0.1 µM wild-type and two mutant (T25G and T25S) 3CLKm and kcat measurements, 0.1 µM T25G and 10–200 µM SAVLQ↓MGFRK substrate were used, and the plot of initial rates within 10% substrate consumption versus different substrate concentrations was fitted to the Michaeli-Menten equation using the KaleidaGraph computer program .The peptides used as substrates for the T25G protease were synthesized via solid phase, using a 433A peptide synthesizer . Each peptide (100 µM) was incubated with 0.1 µM T25G for 1, 2, and 6 h, and the subsequent mixtures were analyzed by HPLC on a C-18 reverse-phase analytic column. Cleavage products were resolved using a 30-min, 2–90% linear gradient of acetonitrile plus 0.1% TFA. The product peak areas were integrated to calculate the reaction rates for each peptide substrate. For pro in complex with a peptide , we have generated a structural model of the protease binding with a modified peptide , where the Ser at P1′ was changed to Met (indicated by the asterisk). We found that Thr25/CG2 of the 3CLpro is within a short distance of 1.32 Å of Met/SD of the peptide  against the SAVLQ↓MGFRK substrate.To test the above hypothesis, T25G and T25S 3CLeviously . Comparepro with 10 selected variations  at P1′ were prepared and used to evaluate the substrate specificity of T25G. As shown in Next, peptides corresponding to the N-terminal maturation site of SARS 3CLE. coli and yeast to express fusion proteins with an AVLQ recognition site to test tag cleavage by T25G. The PstI site CTGCAG, which is part of the AVLQ-encoding sequence GCGGTGCTGCAG, was used as a 5′ cloning site, in conjunction with the 3′ XhoI site, for sticky-end ligation with the PCR product of the target gene (see the strategy illustrated in pro (T25G and T25S), and the final products are shown in lanes 2, 3, and 4, respectively. Only T25G mutant efficiently cleaved the fusion protein, yielding tag-free UPPs (28.3 kDa) and the tags (17.6 kDa) as shown in lane 3. Under these conditions, the tag cleavage reaction was completed <90 min (data not shown). As a control experiment, a UPPs fusion protein with an AAAQ sequence was incubated with the protease, but was not cleaved (data not shown), indicating that T25G specifically recognized the AVLQ cleavage site.We constructed two vectors for use with Pichia expression system, the EGFP fusion protein with SBD, His-tag, and AVLQ site was overexpressed. SBD was included for the purpose of using starch as an affinity matrix for protein purification, which would lower the associated costs. With 0.1 µM T25G, the cleavage of the fusion protein (5.4 µM) was completed <120 min (data not shown).Using a yeast pro is one of the most commonly used endopeptidases for tag cleavage and shares a similar substrate specificity with SARS 3CLpro, we compared the efficiency of tag cleavage using T25G 3CLpro to that of TEVpro against the E. coli-expressed UPPs fusion proteins containing their preferred recognition sites. Compared with TEVpro, T25G showed a 3-fold higher cleavage rate . However, compared with that of FXa, another commonly used endopeptidase, the cleavage rate of T25G was 1.7 times lower . TEVpro generally accepts any amino acid at P1′ except Pro (Since TEVcept Pro . However