The normative developmental course of inhibitory control between 2.5 and 6.5 years, and associations with maternal and paternal sensitivity and intrusiveness were tested. The sample consisted of 383 children (52.5% boys). During four annual waves, mothers and fathers reported on their children’s inhibitory control using the Children's Behavior Questionnaire. During the first wave, mothers’ and fathers’ sensitivity and intrusiveness were observed and coded with the Emotional Availability Scales. Inhibitory control exhibited partial scalar invariance over time, and increased in a decelerating rate. For both mothers and fathers, higher levels of sensitivity were associated with a higher initial level of children's inhibitory control, whereas higher levels of intrusiveness predicted a slower increase in children's inhibitory control. Developing the capacity for self‐regulation, that is, the ability to automatically or deliberately modulate affect, behavior, and cognition Karoly, , is an iEarly displays of inhibitory control are already seen during the first year of life. Even 8‐month‐old children can prevent or stop behaviors in response to their parent’s requests  add to our knowledge by providing a more ecologically valid measure. The most widely used questionnaire for parents to report on inhibitory control is the Children’s Behavior Questionnaire  but may also show different behaviors that indicate their level of inhibitory control . Potential conceptual changes may be due to preschoolers significant development in multiple domains, such as language and motor development, that interacts with the way in which inhibitory control is manifested  can be reliably compared across age, because the indicators are measuring the same thing at different ages. Thus far, a few studies using lab‐based tasks to measure inhibitory control in the preschool years have reported evidence for longitudinal measurement invariance  that examined concurrent associations between responsiveness and a slightly broader inhibitory control construct that also included anxiety‐related behavioral inhibition. Karreman et al. (nstudies = 7). Although the authors tentatively concluded that parental responsiveness and negative control may not be that important for the development of inhibitory control, a growing body of research since then indicates that this conclusion cannot yet be made. For instance, Bernier, Carlson, and Whipple , fathers offer security in situations that are challenging and stimulating  differ on average . A total of 383 families provided data on their children’s inhibitory control during at least one wave and therefore were included in the current study. A total of 373 parents reported on their child’s inhibitory control at Wave 1, whereas 329 (88.20%) parents did so during Wave 4. Little’s missing completely at random test indicated that data were missing completely at random, χ2(104) = 107.57, p = .385. Follow‐up analyses indicated that the number of missings on inhibitory control was unrelated to parental sensitivity and intrusiveness, age and sex of the child, and maternal and paternal education.Single parents, families in which either a child or parent had a severe physical or intellectual handicap, and parents born outside the Netherlands or not speaking the Dutch language were excluded from participation. In total, 390 (31%) of the 1,249 contacted families agreed to participate. These families did not differ from the non‐participating families on age, educational level of both parents, and degree of urbanization of the place of residence  during the first wave, 4.01 years  during the second wave, 5.04 years  during the third wave, and 6.03 years  during the fourth wave. Mothers were aged between 23.64 and 45.62 years  and fathers were between 25.84 and 62.97 years of age . Most participating parents were married, had a cohabitation agreement or registered partnership (93.00%), and the remaining 7.00% lived together without any kind of registered agreement. With regard to educational level, most of the mothers (79.40%) and fathers (76.80%) had a high educational level . This is higher than the national average  were on average 3.01 years old  to 7 . This is in contrast to the original rating scale, which ranges from 1 (extremely untrue of child) to 7 (extremely true of child). Parents could also indicate that an item was NA. The scale used in the current study aligns with some of the other questionnaires based on Rothbart’s work, such as the Early Childhood Behavior Questionnaire  was younger than the intended age range of the CQB, that is, 3 years of age, this could not explain this high percentage: a high percentage of parents of children older than 3 years also indicated that this item was NA (see Table r = .46–.53). Items were generally also sufficiently correlated (r = .15–.40). To create more robust scores for inhibitory control, father and mother reports were averaged at the item level for subsequent factor analyses as described in the results section. Cronbach’s alpha using these averaged items indicated that the internal consistency of the scale was good, ranging from .77 to .85 across waves.The Inhibitory Control subscale of the CBQ . Next, we examined measurement invariance. We first constrained factor loadings over time , followed by the intercepts  and the residuals . Potential sources of invariance were detected by inspecting the Modification Indices (MI) in M Little, .plus were also reported. However, as these tests are known to be too sensitive to small and unimportant deviations, we do not rely heavily on these indices. When the absolute model fit and ΔCFI were sufficient, we were very conservative with applying any further changes to the model. Any further changes of the model either had to be supported by a large MI, and/or had to be logical from a theoretical point of view.Model fit was evaluated through the comparative fit index (CFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA). CFI and TLI values above .90 and RMSEA values below .08 indicate a sufficient fit. In addition, ΔCFI between the restricted and less restricted model had to be < .01 . This approach avoids biases in growth factor variances that could potentially occur when fixed time intervals are applied to age heterogeneous samples  and Bayesian Information Criterion (BIC) were used to compare model fit for the growth models. Higher BIC and AIC values indicate a worse model fit for the restricted model. As the BIC more strongly penalizes model complexity, this fit index was considered superior to the AIC. Decreases in BIC values larger than 10 indicate serious model improvements Raftery, .2(35) = 150.668, p < .001, RMSEA = .069, CFI = .906, TLI = .885). Based on the largest MI, we allowed for covariance between the residuals of two highly similar items ("Has a hard time following instructions" and "Is good at following instructions"). This model fitted the data sufficiently (χ2(53) =  119.114, p < .001, RMSEA = .058, CFI = .936, TLI = .920). Standardized factor loadings ranged from .10 to .74.At Wave 1, a one‐factor model showed near sufficient fit to the data . The intercept and slope were not significantly correlated, −.01, p = .235, indicating that the initial level was not associated with the rate of change in inhibitory control.After determining the shape of growth, multi‐group analyses showed that a model in which the intercepts, means, variances, and covariances of the growth factors were constrained across children’s sex  showed lower BIC values and higher AIC values than a model in which all parameters were released . In addition, there were no MIs that resulted in a better fit of the model. We therefore concluded that the development of inhibitory control was not only similar in shape but also in initial level, rate, and direction of development for boys and girls. Inhibitory control showed an average increase that decelerated over time, with variance around the intercept and linear slope (interceptWe next examined associations between the intercept and linear slope of inhibitory control and parenting (associations between parenting and the quadratic slope were not estimated as we fixed the variance of this slope to zero). A model in which maternal and paternal sensitivity and intrusiveness were similarly associated with boys’ and girls’ initial level and slope of inhibitory control showed the best fit to the data . However, parental sensitivity was unrelated to growth in inhibitory control . In addition, higher intrusiveness did not predict the initial level of inhibitory control , but did predict slower growth of inhibitory control . Figure Higher parental sensitivity was related to a higher initial level of inhibitory control . The current study therefore adds to the literature on the development of inhibitory control, which has primarily focused on children at risk. However, future studies should examine the development of inhibitory control, and the role of parents in this development, in a more representative sample and in less traditional family compositions . Second, we could not examine associations between growth in parenting behaviors and development in inhibitory control, as parenting was not assessed during all four waves. Future studies should examine how the development of parenting relates to their children’s inhibitory control development. Third, although a major strength of the current study is the inclusion of both mothers and fathers, we did not observe mothers and fathers simultaneously. Previous work has shown that co‐parenting is related to children’s effortful control over and above maternal and paternal parenting (Karreman et al., Overall, the present study involved a thorough longitudinal examination of the development of inhibitory control. We found parent‐reported inhibitory control to be subjected to conceptual changes in the preschool years, emphasizing the need for researchers to test for longitudinal measurement invariance prior to modeling mean‐level changes. In the current general population study, parent‐reported inhibitory control for both boys and girls showed a decelerating increase in the preschool years. Importantly, parenting behaviors that are related to higher levels of inhibitory control during the first assessment are not predictive for faster increases in inhibitory control. The findings show that parental sensitivity is associated with a consistently higher level of inhibitory control in the preschool years. In addition, to bolster the development of inhibitory control, parents have to give their children space to explore, and interfere sparingly. Mothers’ and fathers’ parenting practices were of similar importance to the development of inhibitory control, suggesting that interventions designed to bolster the development of inhibitory control should target both parents.Table S1. Number of Parents Answering Non‐Applicable During Four WavesClick here for additional data file.


Through silicon via (TSV) is one of the most important technologies used in three dimension (3D) packaging. The void-free filling of TSV can be achieved by adding additives into the electrolyte bath during the electrodeposition process. This paper focuses on the effects of three types of commercial additives  and analyses additives’ interaction on electroplating through experimental investigations. The results showed that the suppressor, the leveler and the accelerator all have chemical behaviour of inhibition in different degrees to the copper electroplating. The interaction experiments of additives in pairs indicated that the suppressor absorbed on the cathode surface was gradually displaced by the accelerator as the concentration of the accelerator increased; the accelerator and the leveler presented a competitive adsorption relationship; the suppressor and the leveler had a synergistic effect for electroplating inhibition especially under high potential and low suppressor concentration. Experiments of micro via filling by electrodeposition have been conducted to investigated the effects of singular additive and multiple additives on the filling process of the micro vias. Owing to the merits of high density, low energy consumption, high electrical performance and low RC delay, through silicon via (TSV) becomes the key technology in the 3D package5 in recent years. TSV, usually filled by copper (Cu), is typically achieved by electrodeposition7. As the micro structure of the TSV is too small , non-uniform distribution of flow field, concentration field and electric field appears in the local region of the micro via, which leads to non-uniform filling rate between the opening and bottom of the TSV, especially of TSVs with high aspect ratio (AR)13. The non-uniformity may cause filling defects, voids and seams, and eventually result in poor electrical properties and reliability problems for TSVs18.Nowadays, as the trend for the electronic devices manufacture becomes more integrated, the size of the electronic devices keeps diminishing. This makes three dimension (3D) packaging technology become increasingly prevalent21. An effective way to accomplish this is adding additives and halogen ions into the plating bath, which changes the copper ions deposition rate on the cathode surface23. Additives are typically divided into three types: suppressor, accelerator and leveler25. The halogen ions (Cl− in most cases)27 adsorb on the cathode surface and form cuprous electron bridges and bind themselves with the additives, leading to inhibition or acceleration of the copper electrodeposition30. Ko et al.31 achieved void-free deposition with polyethylene glycol (PEG) as suppressor. It was found that the inhibition effect of Cl−–Cu+–PEG complexes was governed not only by PEG concentration but also by PEG molecular weight. Moffat et al. studied the behaviour of bis (3-sulfopropyl) disulfide (SPS) as accelerator, and found that the effect of SPS on electrodeposition was potential dependent32. Hasegawa et al. investigated the effect of Janus green B (JGB) as leveler on micro via filling, and reached the conclusion that JGB inhibits the overfill phenomenon in the later stages of the deposition process, but the inhibition become greater with higher concentrations of JGB, resulting in void formation33. Akolkar and Landauz34 studied the interactions of PEG and SPS and proposed a competitive adsorption theory between PEG and SPS. It was clear that PEG adsorbed and polarized the electrode earlier than SPS, and was later on replaced completely by SPS on the electrode. Dow et al. found that PEG and JGB interacted and produced a synergistic inhibition effect on copper electrodeposition35. Although many studies43, have discussed the mechanism of the additives’ behaviours and their effects on TSV deposition, systematic investigations on additives’ interactions have rarely been found.The principle of void-free filling of the micro via is to inhibit the filling rate at the via opening and accelerate the filling rate at the via bottomIn this study, the suppressor (AESS), accelerator (M) and leveler (PNI) are purchased from Jiangsu Mengde New Materials Technology Co., Ltd, and their interaction effects on electrodeposition were investigated. Systematic experiments regarding to each additive separately and additives interaction were performed respectively. The electrochemistry behaviours of the additives under different current density were quantitatively characterized using chronoamperometry (CA) test. Experiments of micro via filling by electrodeposition were conducted, and the results were obtained by a scanning electron microscope (SEM).2+ 0.63 mol/L, CH3SO3− 0.21 mol/L, Cl− 50 ppm) and varying concentration of the additives, as shown schematically in Fig. In order to investigate the effects of additives on the TSV filling process, the electrochemistry behaviours of the additives were quantitatively characterized using chronoamperometry (CA) test. Three additives, i.e. AESS, M and PNI were used as the suppressor, the accelerator and the leveler, respectively. The recorded current was used to analyse the effects of additives during the electrodeposition process for TSV filling. The CA measurements were conducted in a three-electrode cell with the base electrolyte , the current response of the electrodeposition system gradually became stable. Once a steady-state current was attained, the additives were rapidly injected into the bulk using a syringe. As additives with inhibition effect on electrodeposition rate injecting into the electrolyte, the current response would drop with a certain degree. When a new steady state of the current response was attained, it was recorded. The drop in current response indicated the inhibition effect of the certain amount of the additive. The injection operation was repeated until the saturated value of the current response against the concentration of the additives was obtained. Two types of experiments were conducted: (a) single additive injection, (b) multiple additives injection.As shown in Table In order to investigate the interaction of additives in the copper electrodeposition, CA tests with multiple additives were performed. Before the CA test commence, one kind of additive with a certain concentration was premixed into the electrolyte; another kind of additive was later injected into the electrolyte during the CA test process. As shown in Table 2, and the temperature was maintained at 25 °C.Based on the results of the electrochemical analysis, experiments of micro via filling by electrodeposition were conducted. The effects of singular additive and multiple additives on micro via filling were studied in separate experiments Table . The basUtilizing the parameters provided in Tables Figure entclass1pt{minimaAs shown in Fig. The experiment results are shown in Fig. In the interaction tests of the suppressor and the accelerator, the concentration of the suppressor was fixed at 6.2 ml/L, while the concentration of the accelerator was increased from 0 ml/L to 12 ml/L. From the previous analysis, it is known that the accelerator shows much weaker inhibition ability than the suppressor. By contrast, the accelerator has a relative promoting effect on electroplating comparing to the suppressor.Figure − adsorbed on the cathode surface than the suppressor. In the interaction system, the accelerator broke the bond between suppressor and Cl−, leading to suppressor’s desorption and displacement by accelerator.As shown in Fig. As shown in Fig. iN.Two groups of experiments were conducted to study the interaction of suppressor and leveler, as given in Table mentclass2pt{minimBased on the results of the electrochemical analysis presented above, experiments of micro via filling by electrodeposition were conducted Table . As showThe filling ratio is obtained, which is defined as the percentage of the filled area against the whole area of the micro via. With no additive adding into the electrolyte (base), the filling ratio is 46.8% Fig. ). With aThe results show good agreement with the results of the electrochemical analysis for the additives, i.e. the accelerator shows slight inhibition effect on electrodeposition, while the leveler is medium and the suppressor is strong.The interaction effects of additives on micro via filling through electrodeposition are presented in Fig. Figure In the singular additive system, the suppressor, leveler and accelerator all show different degrees of inhibition ability, and the inhibition behaviour is potential dependent;Interaction of the suppressor and the accelerator indicates that the suppressor absorbed on the cathode surface is gradually displaced by the accelerator as the concentration of the accelerator increases;The accelerator and the leveler have a competitive relationship with respect to adsorption amount on the cathode surface, which indicates that the leveler tends to replace the accelerator at low potential while the accelerator tends to replace the leveler at high potential;The suppressor and the leveler have a synergistic effect for electroplating inhibition, especially under high potential and low suppressor concentration.Micro vias have been filled by Cu electrodeposition, with addition of singular additive and multiple additives in the bath, and non-defect filling of the micro via has been achieved.In this paper, effects of singular additive and additives’ interaction on electroplating were investigated by experimental analysis. The conclusions are summarized as follows:


Species richness is the most commonly used metric to quantify biodiversity. However, examining dark diversity, the group of missing species which can potentially inhabit a site, can provide a more thorough understanding of the processes influencing observed biodiversity and help evaluate the restoration potential of local habitats. So far, dark diversity has mainly been studied for specific habitats or large‐scale landscapes, while less attention has been given to variation across broad environmental gradients or as a result of local conditions and biotic interactions. In this study, we investigate the importance of local environmental conditions in determining dark diversity and observed richness in plant communities across broad environmental gradients. Using the ecospace concept, we investigate how these biodiversity measures relate to abiotic gradients (defined as position), availability of biotic resources (defined as expansion), spatiotemporal extent of habitats (defined as continuity), and species interactions through competition. Position variables were important for both observed diversity and dark diversity, some with quadratic relationships, for example, plant richness showing a unimodal response to soil fertility corresponding to the intermediate productivity hypothesis. Interspecific competition represented by community mean Grime C had a negative effect on plant species richness. Besides position‐related variables, organic carbon was the most important variable for dark diversity, indicating that in late‐succession habitats such as forests and shrubs, dark diversity is generally low. The importance of highly competitive species indicates that intermediate disturbance, such as grazing, may facilitate higher species richness and lower dark diversity. Dark diversity, the group of missing species that can potentially inhabit a site, is strongly related to abiotic conditions or habitat types. Dark diversity is positively correlated with competition estimated using community mean Grime C and negatively correlated with soil organic carbon indicating that dark diversity increases at sites dominated by few, but abundant competitive species, but is reduced in undisturbed sites with long‐term stability. We removed all subspecies, hybrids, variations, and neophytes  litter samples within a 21 cm × 21 cm frame pr. site), (c) soil organic matter as a percentage of the 0–10 cm soil core that was categorized as organic soil, (d) soil organic carbon as % soil C in 0–10 cm soil layer (g/m2 average of four soil samples taken at each site) as described in Brunbjerg, Bruun, Broendum, et al.  and soil fertility index (SFI). For each site, SFI represents the predicted value from the best linear model  of site mean Ellenberg N , et al. .C value based on the site species lists.Lastly, to examine the assumption that competitive exclusion can lead to lack of co‐occurrence and hence increased dark diversity, we used Grime's plant life strategies to quantify the importance of local interspecific competition for establishment of species. Grime's plant strategy theory  states a three‐way trade‐off between life strategies that facilitate competition for resources (competitive strategy), survival in stressful environments, for example, with high salinity, flooding, and drought (stress tolerance), and survival in disturbed environments  by creating a site regional pool combining the four plot regional pools at each site. In addition to the four plots, plants had been inventories in the whole site, and therefore observed species in the site, but not included in the regional pools (n = 2) were added to the regional pools to ensure that site regional pool included all observed species. Then, dark diversity was calculated for each site as the difference between the regional pool and the observed species richness . Because dark diversity is relative it may not be suitable for comparison across habitats ; Formal analysis (lead); Methodology (lead); Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing (lead). José W. Valdez: Writing‐original draft (lead); Writing‐review & editing . Lars Dalby: Conceptualization ; Data curation (lead); Formal analysis ; Methodology ; Validation ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Jesper Erenskjold Moeslund: Conceptualization ; Data curation (lead); Formal analysis (supporting); Funding acquisition (lead); Methodology ; Project administration (lead); Validation (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Kevin K. Clausen: Conceptualization ; Formal analysis (supporting); Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Rasmus Ejrnæs: Conceptualization ; Data curation ; Methodology (supporting); Writing‐original draft (supporting); Writing‐review & editing (supporting). Meelis Partel: Conceptualization (supporting); Formal analysis (supporting); Methodology . Ane Kirstine Brunbjerg: Conceptualization ; Data curation ; Formal analysis (lead); Methodology ; Validation (lead); Visualization (lead); Writing‐original draft ; Writing‐review & editing .


Terrestrial hot springs are of great interest to the general public and to scientists alike due to their unique and extreme conditions. These have been sought out by geochemists, astrobiologists, and microbiologists around the globe who are interested in their chemical properties, which provide a strong selective pressure on local microorganisms. Drivers of microbial community composition in these springs include temperature, pH, in-situ chemistry, and biogeography. Microbes in these communities have evolved strategies to thrive in these conditions by converting hot spring chemicals and organic matter into cellular energy. Following our previous metagenomic analysis of Pisciarelli hot springs , we report here the comparative metagenomic study of three novel sites, formed in Pisciarelli as result of recent geothermal activity. This study adds comprehensive information about phylogenetic diversity within Pisciarelli hot springs by peeking into possible mechanisms of adaptation to biogeochemical cycles, and high applicative potential of the entire set of genes involved in the carbohydrate metabolism in this environment (CAZome). This site is an excellent model for the study of biodiversity on Earth and biosignature identification, and for the study of the origin and limits of life. Extreme environments such as hot springs are of great interest as a source of novel extremophilic microorganisms, enzymes, and metabolic pathways essential for the microbial survival in extreme conditions . ExtremoResearch on extremophiles and their enzymes (extremozymes) has not only reshaped our understanding of the origin and evolution of life  and the The increasing amounts of metagenomic data and fully sequenced genomes now allow us to systematically explore these microbial communities ,18, enabRecently, our group reported on the metagenomic analysis of the microbial community populating the Pisciarelli hot springs , identifying the entire carbohydrate active enzymes portfolio (CAZome) that has been cloned and partially characterized .Acidianus hospitalis, 40%; Pyrobaculum arsenaticum, 20%; Pyrobaculum oguniense, 5%; Saccharolobus solfataricus, 1%) followed by Bacteria (0.11%) and archaeal viruses (0.17%), while more than 30% of the obtained reads found no match with the nucleotide NCBI database. In contrast, the majority of the obtained reads from Pool 2 found no match with the NT NCBI database (62%). Among the assigned reads, Crenarchaeota (37%) dominate the site  followed by archaeal viruses (0.36%) .A notable difference between the result observed in Site A and the previous study consists in the percentage of reads of unknown origin, which decreased from 32% in Pool 1 to 15.4% in Site A. This variation indicates a change in biodiversity of the microbial community populating the pool as an effect of the geothermal events that occurred between March 2012 and July 2019 (Pool 1 Biosample ID: SAMN09692669).Acidianus (40%) as the dominant genus, followed by Pyrobaculum (25%)   with a rum (25%) . This paum (25%) A. IndeedAcidianus were also present in the sample from Site C. These, however, represented only 18% of the total reads, while the dominant genus was represented by Metallosphaera (33%)  , mainly  1.0–4.5 . This re species . NonetheAcidianus two-tailed virus and disparate types of Sulfolobus monocaudavirus), Ampullaviridae (Acidianus bottle-shaped viruses), Fuselloviridae (Sulfolobus spindle-shaped viruses), and Ligamenvirales  were identified.Another remarkable difference between Site C and the other two sites was the number of viral reads identified in this sample. As mentioned above, 4.6% of Site C reads were taxonomically assigned to viruses. Among these, viruses mainly belonging to Bicaudaviridae  in prokaryotes . Transfent hosts , contribTo evaluate the bacterial communities present in Sites A, B, and C, the reads of the three samples were analyzed in detail in the NT database .Acidithiobacillus (15%) and the hyperthermophilic Hydrogenobacter (8%), while the remaining 78% belonged to different genera whose relative abundance was less than 7%  and Caldanaerobacter (9%), the thermophile Thermoanaerobacterium (10%), and Acidithiobacillus, which, unlike in Site A, was much less abundant here (4%). It is important to note that one of the most abundant bacterial genus present in Site B was Thiomonas (14%), which, although generally grouping mesophilic species, also includes moderately thermophilic species identified in geothermal sources at ~45 °C and able to grow at temperatures up to 50 °C and in the pH range 4.0–7.0 [As previously indicated, among the three sites, Site B showed by far the highest percentage of bacterial reads . The analysis of the relative abundances of bacteria present in the microbial community of Site B B allowed 4.0–7.0 ,30,31.Shigella and Escherichia, respectively  and Aeromonas (8%). The Clostridium genus includes obligate anaerobic bacteria and Gram-positive bacteria and are capable of forming spores in adverse environmental conditions, which populate soil, sand, rivers, swimming pools, river bank mud, and marine sediments [Clostridium spp. and Aeromonas. In addition, a novel thermophilic Clostridium species (C. thermarum) from a thermal spring in China has recently been identified and characterized [Among the remaining 46,141 reads of Site C, the most abundant genera were ediments . Unfortuediments  and fiveediments  revealedcterized .To identify possible chunks of individual genomes present in the three samples, all the reads were separately assembled by MEGAHIT  obtaininContigs with a length ≥1000 bp were analyzed by MyCC , thus alThe clusters obtained were then analyzed by CheckM , which aCrenarchaeota phylum, in particular related to the genera Acidianus, Pyrobaculum, and Desulfurococcus.To obtain a taxonomic assignment, the validated clusters were analyzed by Diamond (in blastx mode) using the NCBI Refseq Protein database  Table 3Table 3. Acidianus spp. and by two clusters (4 and 5) with a highly heterogeneous assignment that prevented the identification of a dominant species.Site C, on the contrary, was characterized by clusters entirely related to Crenarchaeota phylum, related to Desulforococcus spp. and P. arsenaticum.Differently, the clusters identified in Site B were mainly assigned to bacteria belonging to the phyla Firmicutes and Proteobacteria, confirming what was already observed from the taxonomic analysis of the reads. Three of the clusters of Site B were assigned to the Acidianus   to cluster 10, whose taxonomic analysis was classified as related to the genus 10 (45%) , with whTo evaluate the individual contribution to the metabolic functions of the microbial consortia present in Sites A, B, and C, the replication indices of the validated clusters were calculated using iRep .Among all the analyzed clusters, only ten respected the selection parameters  and wereDesulfurococcus, despite the low number of reads assigned to this taxon , had more than 30% of the cells in active replication , including on phase .Unfortunately, it was not possible to calculate the replication index of the other clusters, probably due to the limitation of iRep, which during the analysis discarded the regions with very high and very low coverage, applying a linear regression model relating exclusively to the coverage of the region containing the origin of replication .To assess the metabolic potential of the microbial consortia populating the three sites, the contigs obtained by the assembly were analyzed by Prodigal  identifyThe analysis of the three samples showed an average comparable distribution of the functional categories reported in both databases. However, by observing in more detail the classification according to the COG database , it is pIn addition, the annotation using the SEED database  showed aAs for the “Mobility and Chemotaxis” category, the main differences were related to the presence of ORFs of bacterial origin involved in the structure and mobility of the flagellum, while in Site C, the ORFs annotated as “Archaeal Flagellum” and “Bacterial Chemotaxis” (dipeptide-binding ABC transporter) were most abundant.In all three samples, the largest number of ORFs functionally annotated belonged to the categories “Carbohydrate Transport and Metabolism” and “Carbohydrate” of COG and SEED, respectively. A similar result was previously observed in Pool 1 and Pool 2 and related to the abundant vegetation around the Pisciarelli thermal spring, rich in starch, hemicellulose, and pectins, which could represent an available carbon source for the microbial communities populating these geothermal sites .To map the difference of the enzymatic activities involved in the synthesis, degradation, and modification of carbohydrates (CAZymes) in the Sites A, B, and C, the ORFs of the three samples were analyzed by dbCAN2 . The assAcidianus-related CAZymes for Sites A and C. In addition, while Site A had numerous activities related to Pyrobaculum and Desulfurococcus and to several (hyper)thermophilic bacteria of the phylum Aquificae , the CAZymes of Site C were mainly assigned to the genera Metallosphaera, Saccharolobus, and Sulfolobus. Differently, the greater number of CAZymes identified in Site B belonged to the thermophilic bacteria of the genus Thermoanaerobacterium, Thermoanaerobacter, Caldanaerobius, Caldanaerobacter, and Desulfotomaculum  represented 28%, 43%, and 27% of the CAZymes identified in Site A, Site B, and Site C, respectively. Among the families of GHs common in all three sites , Table 7Among the identified GHs, 48 of these were present only in Site B . The anaA large fraction of the identified CAZymes was annotated as glycosyltransferase (GTs). In particular, in Sites A and C they represented the most abundant activity class , while in Site B GTs were only 38%.Acidithiobacillus, Anaeromusa, Caldanaerobacter, Desulfofarcimen, Desulfotomaculum, Desulfurella, Planifilum, Syntrophorhabdus, Thermoanaerobacter, Thermoanaerobacterium, and Thiomonas.Eight hypothetical families of GTs were identified exclusively in Site B A, namelyThermoanaerobacter and Desulforella  B, Site Blforella , and whiThermoanaerobacter and Caldanaerobacter genera , in auxiliary activities (AAs) and in polysaccharide lyases (PLs) C,D. The r genera . In contg a CBM4 .The hypothetical AAs present in all three sites belong to the families AA6  and AA7 glucooligosaccharide oxidases and chitooligosaccharide oxidases, showing >80% identity in Sites A and C with sequences mainly associated with the phylum of the Crenarchaeota. In Site B, the sequences of which more than 50% of identity have mainly been attributed to the bacterial phyla of Firmicutes and Proteobacteria as well as for the families AA1, AA2, and AA3, were identified exclusively in this sample.Pelosinus sp. UFO1, the three ORFs classified as PL15 showed identities between 90 and 99% with hypothetical proteins associated with the thermophilic genera Thermoanaerobacter and Caldanaerobacter  with the pectate lyase from robacter .The remarkable number of sequences encoding putative CAZymes makes the Pisciarelli microbial population an attractive source of novel thermophilic biocatalysts for industrial applications. The functional annotation here described represents a preliminary survey, but already promising a relevant biochemical potential of the microbial consortia of the different geothermal sites in the Pisciarelli area. Indeed, future research will carry out more detailed studies on these extremophilic communities and their CAZomes in order to deepen their knowledge and exploit their biodiversity in biotechnological processes.TM + PH1 equipped with 5051T electrode (HACH)).Samples from the hydrothermal mud/water pools, Site A, Site B, and Site C, respectively, were transferred into sterile tubes, respectively, were closed and immediately transferred to the laboratory for DNA extraction. In situ measurements of temperature and pH were performed by using an HI-93510 thermometer  equipped with a Pt100 probe, and a pH meter for field use  gels.The amounts of obtained metagenomic DNA were quantified by a Qubit 4 Fluorometer using the Qubit™ DNA HS assay kit , and DNA quality was independently assessed by visualization on 1% agarose (The extracted and purified mDNA of each site was used for shotgun sequencing with Miseq (Illumina) performed at Novogene Europe, Cambridge (UK), and the datasets obtained were provided as clean reads. The sequencing reads are available in the NCBI Sequence Read Archive (SRA) database under the accession numbers SRR12124857 (Site A), SRR12124856 (Site B), and SRR12124855 (Site C).The environmental data relative to the Sites A, B, and C (NCBI BioProject PRJNA643424) are available in the Biosamples database under the accession numbers SAMN15414048 (Site A), SAMN15414049 (Site B), and SAMN15414050 (Site C).For microbial diversity analysis, short paired-end Illumina reads (150 bp) were aligned to the nucleotide reference database of NCBI NT by using Blast+/BlastN. The resulting output data of each sample  were used as input for MEGAN6 Community Edition with the following parameters: MinScore = 40.0, MaxExpected = 0.7, TopPercent = 10.0, MinSupportPercent = 0, mode = BlastN) .Clean reads were assembled using MEGAHIT  by usingObtained contigs ≥1000 bp were grouped into bins by using MyCC ; the obtTo obtain a replicative estimation, the clusters validated by CheckM were filtered by using an ad hoc pipeline to remove contigs with shorter than 5000 bp, completeness <75%, contamination >2%, and a ratio fragment/Mbp > 175. Again, the reads of each sample were aligned to the remaining clusters, and the SAM files obtained were analyzed by iREP .All the contigs obtained by the assembly procedure were analyzed by using Prodigal  to identTo identify the hypothetical carbohydrate-active enzymes the ORFs were analyzed by the dbCAN2 pipeline , and theExtremophiles are organisms capable of adapt themselves, survive and thrive in hostile habitats that were previously thought to be adverse or lethal for life . ExtremeHowever, the study of extremophiles is a rather difficult field, mainly constrained by the complexity of reaching their ecological niches and isolating these microbes. Pisciarelli Solfatara hot springs represents a unique ecological niche for the study of hyperthermophiles. This area, so surprisingly dynamic, is affected by sudden geothermal changes such as the increase of the magmatic component of fumaroles, frequent seismic swarms, and bradyseism, indicating that the hydrothermal system undergoes repeated injections of magmatic fluid . These sThe comparative metagenomic study reported here allowed us to understand the complexity of the microbial community in three new sites that were generated by geochemical change happening in this area in July 2019. This study demonstrates that these sites, although very close to each other, showed remarkable differences in terms of pH and temperature that were reflected by significant differences in the microbial consortia inhabiting each site.Living at such selective pressure might foster the development and retention of a suite of metabolic and physiological adaptations, which could play a key role in ensuring the presence and persistence of life in extreme environments .Indeed, the metagenomic investigation revealed a broad CAZome, correlated to the abundant vegetation present around the Pisciarelli thermal spring, rich in starch, hemicellulose, and pectins, which represent a considerable carbon source for the microorganisms populating the geothermal sites.The presence of highly sophisticated mechanisms of adaptation together with the availability of specific biochemical pathways sustaining peculiar physiological metabolic capabilities makes the extremophilic microbial communities of Pisciarelli interesting from an astrobiological point of view.


Ion channels form pores that allow for the selective transport of ions across cell membranes, generating electrical signals in response to a variety of signals. Inward rectifier potassium (Kir) channels in particular are regulated by direct interactions with the complex mixture of lipids that are present in eukaryotic cell membranes. However, the molecular details of these concurrent lipid interactions with Kir channels are not clear and difficult to access via experimental methods. Here, we simulate the Kir2.2 channel in a complex lipid mixture to explore how anionic phospholipids and cholesterol dynamically organize around the membrane protein. In particular we demonstrate a synergy between binding interactions of different anionic phospholipid species which are known to activate Kir channels. 2) activates the channel; a secondary anionic lipid site has been identified, which augments the activation by PIP2; and cholesterol inhibits the channel. Molecular dynamics simulations are used to characterize in molecular detail the protein–lipid interactions of Kir2.2 in a model of the complex plasma membrane. Kir2.2 has been simulated with multiple, functionally important lipid species. From our simulations we show that PIP2 interacts most tightly at the crystallographic interaction sites, outcompeting other lipid species at this site. Phosphatidylserine (PS) interacts at the previously identified secondary anionic lipid interaction site, in a PIP2 concentration-dependent manner. There is interplay between these anionic lipids: PS interactions are diminished when PIP2 is not present in the membrane, underlining the need to consider multiple lipid species when investigating protein–lipid interactions.Protein–lipid interactions are a key element of the function of many integral membrane proteins. These potential interactions should be considered alongside the complexity and diversity of membrane lipid composition. Inward rectifier potassium channel (Kir) Kir2.2 has multiple interactions with plasma membrane lipids: Phosphatidylinositol -bisphosphate (PIP Interactions of phospholipids and cholesterol (Chol) with ion channels, receptors, and other membrane proteins play a key role in the structure and function of these proteins . Structu27Molecular dynamics (MD) simulations provide a powerful tool to investigate protein–lipid interactions. In particular, the coarse-grained MARTINI force field –13 has bAnalysis of protein–lipid interactions in MD simulations has been performed in several ways: by identifying protein residues that have high frequency of lipid contacts , which activates mammalian Kir channels . Together, the two types of lipid interaction enhance the open probability of the channel. The other major regulatory lipid is Chol, which inhibits Kir2.2 via direct interactions , and phosphatidylcholine (PC). We identify multiple lipid interaction sites for each lipid type and extract kinetic information for each site. This analysis reveals the dynamic interplay of the two anionic lipids (PIP2 and PS) at their two binding sites (the primary and secondary anionic lipid sites).Here we perform a set of large time- and length-scale simulations of Kir2.2  in complex, asymmetric lipid bilayers containing PIP2 vs. PS binding at each site. These analyses allow us to build up a picture of diverse lipid interactions on the surface of Kir2.2 channels, corresponding to the multimodal regulation of this channel by different lipid species.Here, Kir2.2 channel–lipid interactions were determined from simulations in a bilayer containing multiple, functional lipid species. By applying analytical techniques to identify lipid interaction sites, introduced recently by Barbera et al.  to look SI Appendix, Table S1), including from a model of the PM (with an inner leaflet containing PC:PE:Chol:PS:PIP2 and an outer leaflet containing PC:PE:Chol:Sph:GM3), a PM with PIP2 removed, and a bilayer containing solely PC. In order to obtain a more complete understanding of the effect of varying lipid composition and of the possible interplay between lipids, additional (50 µs) simulations were run with nine copies of the Kir2.2 channel embedded in membranes with and without PIP2, with and without PS, with and without Chol, and with and without GM3 .Coarse-grained MD simulations of 20 to 40 µs were run with 144 copies of the Kir2.2 channel embedded in membranes with varied lipid compositions  calculations.2 and PS can be observed in simulations of Kir2.2 in the PM, with PS appearing to replace PIP2 if PIP2 is not included in the membrane . In simulations where PIP2 is not included , PS is the principal lipid species of the inner leaflet that is enriched around Kir2.2 channels. These trends are apparent whether or not Chol is present . In the outer  leaflet, GM3 is the principal lipid species interacting with Kir2.2, with a degree of first annular shell enrichment comparable to that of PIP2 and PS in the inner leaflet. This is of possible biological relevance given suggestions of the role of sphingolipids (which include gangliosides such as GM3) in the formation of outer leaflet nanodomains with possible roles in brain development and/or function . PC, PE, and sphingomyelin are excluded from the first annular shell around Kir2.2.Clustering of PIPmembrane . In ordemembrane A. In thefunction . HoweverSI Appendix, Figs. S3 and S4). PIP2 and PS headgroups interact with both the transmembrane domain, at the classical PIP2 interaction site, and with the cytoplasmic domain. Chol headgroup interactions occurred at residues closer to the membrane center and were more dispersed . Chol headgroup interactions occurred in particular with residues P63, I67, R80, L83, L84, S87, I100, L103, I107, C155, P156, L157, F160, V164, and V168 , in agreement with those previously identified (https://www.rcsb.org/pdb/explore/remediatedSequence.do?structureId=3SPI). The interaction of the anionic ganglioside with cationic side chains close to the extracellular mouth, in particular at residues H108 and K121, might be anticipated to modulate the conductance of the channel.In order to understand how specific lipid molecules interact with Kir2.2 channels, the frequency of lipid headgroup interactions at each residue of Kir2.2 was calculated and mapped onto the protein surface, thus revealing lipid headgroup interaction “hotspots” . Similar behavior has been seen for the TRP channel PC2 . The PS headgroup interacts at K62 and K220, in agreement with experimental data .We examined in more detail the headgroup interaction sites of PIP2 and PS . The PIPeriments , 36. PIPtal data –36, withLipid headgroup interaction hotspots shown in  and 3 doIn order to understand the kinetics of lipid interactions at the surface of Kir2.2 channels, the interaction time of each lipid headgroup at the surface is required. The residence time is a measure that has been used previously –21, 48. 2 and PS headgroups at the Kir2.2 channel surface , and by calculating the residence time at each interaction site we showed that PIP2 and PS interact at multiple sites with diverse kinetic profiles.Using this approach, multiple, distinct interaction sites were identified for PIP2 and PS headgroup interactions at the each site, we analyzed the number of protein residues that a single lipid headgroup interacts with simultaneously when at a given interaction site  and performed FEP calculations  shows that there are a population of PIP2 molecules that frequently interact with only one residue . A snapshot of PIP2 interacting at this site, with the PIP2 headgroup in contact with 12 residues of Kir2.2, is shown in Analysis of the number of contacts made simultaneously by any single PIP2 headgroup interaction sites had much lower residence times than that of the primary PIP2 interaction site and made many fewer simultaneous contacts (maximum 10) than at the primary site . Even so, there was some heterogeneity between the remaining interaction sites. PIP2 headgroups interacted at a site containing residues Q64, R65, I67, and A68 of the slide helix and residues K48, N49, G50, A191–H198 and Q311–A312, S314, and Y316 of the cytoplasmic domain. This interaction site had the second-highest residence time (12.7 ± 0.6 µs in the PM simulations). At this site a population of PIP2 headgroups interacted with five to seven residues simultaneously . This site was split in two and had residence times of 15.8 ± 1.1 µs and 3.9 ± 0.2 µs when GM3 was not included. A third distinct site was identified further down on the cytoplasmic domain , with a residence time in order of 1 to 2 µs. PIP2 headgroup interactions at the secondary anionic lipid interaction site were on the submicrosecond  timescale, indicating a less-tight PIP2 headgroup interaction at this secondary site than at the primary interaction site. Together with the lower interaction frequency of PIP2 at this site  and made fewer simultaneous protein contacts . Intriguingly, in some cases PS was able to interact at the primary site along with PIP2, and it appears that PS can be “trapped” behind a PIP2 molecule . Accordingly, the residence time of this interaction decreases from ≥10 µs to 4 µs (to the nearest microsecond) when PIP2 is absent from the membrane  were different from those of PIPf Kir2.2 B. The incontacts A, likelymembrane C. The in2 these residues are part of the primary interaction site. The PS headgroup interaction site containing residues R78 and R80 had a residence time of 0.25 ± 0.02 µs.PS headgroup interactions at the N terminal of the first TM (transmembrane) helix (R78 and R80) formed a separate interaction site, whereas for PIPA third interaction site encompassed residues K62 and K220, which had previously been determined as the secondary anionic lipid site in experiment. This interaction site also has an intermediate residence time of 0.28 ± 0.02 µs. At this site, coinciding with the experimentally determined secondary interaction site, there was a population of PS molecules that interacted at the site via only one residue, and a second population that interacted with two to seven residues . PS headgroups made very transient interactions with Kir2.2 channels further “out” on the cytoplasmic domain—these may have occurred when there were membrane undulations .Like PIP2, the PS headgroup also had longer interaction times at the primary interaction site than at the secondary interaction site, suggesting that PS headgroups can interact at the primary site but that PIP2 outcompetes PS. PS headgroup interaction sites were similar when GM3 was removed; the most marked difference was the residence time at the primary interaction site, indicating that the high residence time in the PM simulation may have been caused by only a few long interactions.In the same way as PIPSI Appendix, Fig. S5) one can explore the interplay of different lipids at the surface of Kir2.2 channels. Thus, by comparing PIP2 headgroup interaction sites in simulations with and without PS  it is evident that PS does not alter the residues involved in the primary interaction site or the residence time. The position and corresponding residence time of the PIP2 headgroup interaction at the secondary interaction site were also not affected by the presence of PS , meaning that PIP2 would outcompete PS at the primary site. This can be rationalized by the number of contacts that the PIP2 headgroup is able to form at the site in comparison to those of the PS headgroup .In order to explore further the relative interaction free energies of PIPerformed B in whiceadgroup A. The PI2 interaction at this site is functionally important because PIP2 is understood to open the channel by favoring the conformation of Kir2.2 in which the cytoplasmic and transmembrane domains are brought together  are more stable than when PIP2 is not present . This suggests that PIP2 stabilizes the linkers between the cytoplasmic domain and the transmembrane domain, both at the N- and C-terminal ends of the transmembrane domain, which is in agreement with the effect of PIP2 on channel structure as seen in crystal structures.In order to examine the effect of lipid interactions on protein conformation, root-mean-squared fluctuation calculations were performed. Although MARTINI does not allow for changes in protein secondary structure, root-mean-squared fluctuations of the protein indicate that when PIP2 headgroup interactions seem to be impacted relatively little by PS, it appears there is some interplay in terms of effect of PIP2 on PS headgroup interactions . Surprisingly, it appears that the PS headgroup interaction at the secondary anionic lipid site was affected by the presence of PIP2. When PIP2 was absent from the membrane, the PS headgroup interaction at K62 involved fewer residues and residence times were an order of magnitude lower: When PIP2 was present in the membrane, the site included residues E55 to R65, Y66, L218 to K220, and Y342-S343, but when PIP2 was absent the site did not include residues Y66 and R219 to K220, which instead became part of the interaction site at residues R78 and R80 . The residence time for the site at R78 and R80 is not diminished when PIP2 is not present, but the secondary anionic lipid site  has a residence time of 0.28 ± 0.02 µs when PIP2 was present, diminishing to 0.02 µs when PIP2 was absent.While PIP2 is not interacting at the primary site, the secondary site has a ∆∆G of 29.4 kJ/mol, that is, a strong preference for PIP2 ; when PIP2 is present in the primary site the ∆∆G is 17.2 kJ/mol, i.e., far less strong preference for PIP2 over PS. That is, PIP2 interaction at the primary site renders the interaction of PS at the secondary site more favorable.FEP calculations also show that when PIP2 at the primary site on interactions at the secondary site may be due to the increased propensity for the positively charged side chain of R78 to be orientated toward K62 when PIP2 is not interacting at the primary site (2 is present (PM_s and No G_s), the distance between R78 and K62 has a bimodal distribution (the conformation corresponding to the larger distance is seen in snapshot in 2 is not present (simulations No PIP2_s and No PIP2G_s), the distance distribution shifts, such that R78 and K62 side chains are more likely to be close together . The more favorable free energy of binding for PIP2 even at the secondary site suggests that the interaction of PS at this site is dependent on its concentration relative to PIP2.As discussed above, the free energy of binding of PS compared to that of PIPSI Appendix, Fig. S8).PS interacts at the secondary site, and aside from the primary interaction site this is the site where PS appears to form the most contacts with the protein  and neither do Chol interactions appear to be affected by the presence or absence of PIP2 or PS . There is a slight decrease in the Chol residence times in simulations where PIP2 are not present . However, all Chol residence times are low, in the order 0.8 to 1 µs  and the relatively small variation in Chol residence times may not bear great functional significance. There is no noticeable interplay between GM3 and Chol .PIP2, PS, and other lipids as they interacted at the surface of the channel. Multiple lipid species are known to interact and affect the activity of Kir2.2, but previous simulation studies have focused on a single lipid type [e.g., just PIP2 ; in our PM simulations PIP2 comprised 5% of the total lipid composition.The simulations also identify the experimentally characterized secondary anionic lipid site at residue K62. FEP calculations showed that, even at this site, PIPtal data , which s2 and PS at the secondary interaction site: when PIP2 interacts at the primary site, PS interactions at the secondary site become more favorable. Residence times of PS at this site show that PS interacts more tightly when PIP2 is present, and FEP calculations further showed that the presence of PIP2 in the primary site decreased preference for PIP2 over PS at the secondary site. Interplay between PIP2 and PS has been indicated previously by docking studies , and accurate modeling of disordered regions remains a challenge; therefore, it would be difficult to draw firm conclusions from simulations based on such a structure + model.To further assess the interaction of PS and the impact of PIP2 and PS headgroups, and we did not observe a shift in interaction site of either PIP2 or PS on removal of Chol from the membrane. There was a very slight decrease in Chol interaction times in simulations where PIP2 was not present vs. where PIP2 was present; however, this decrease was small. This indicates that inhibition of Kir2.2 by Chol operates independently from channel opening by PIP2 and other anionic lipids.As identified previously , Chol in2 interactions . Enhanced sampling methods and/or Markov state models could be used but with more than two lipid types they could be challenging to implement.The use of the graph-theoretic and residence-time analysis allows for multiple lipids to be observed and avoids predefining an interaction site to explore. However, residence times longer than the simulation time cannot be inferred, and this limits the conclusions that can be drawn regarding the effect of different lipid types on the most long-lived PIP2 are in agreement with the proposed effect of PIP2 on channel opening; however, further work is required to understand how the interplay of PIP2 and PS interactions might affect channel gating, especially as progress has been made recently in understanding the effect of phospholipids on gating of Kir2.2 channels via a combined structural and computational approach . The behavior of PIPer model  were to er model , 17, 52.2, as previously indicated by Rosenhouse-Dantsker et al. (2 interactions sites (and indeed the secondary anionic lipid interaction site) do not overlap with the Chol interaction sites. It is possible that ordered lipid nanodomains (enriched in Chol and sphingolipids)   may moduolipids) , 55. How2 along with a secondary anionic lipid (PS), Chol, and other lipid species including gangliosides at physiological concentrations. PIP2, the lipid required to activate the channel, interacts at crystallographically identified primary interaction sites, and the secondary anionic lipid, PS, interacts at a previously identified secondary anionic lipid interaction site. PS can also interact at the primary interaction site but is outcompeted by PIP2. At the secondary interaction site, PIP2 still has a more favorable interaction free energy than PS, but is more likely to interact at other sites; thus, interaction of PS at the secondary site is dependent on the concentration of PIP2. Further, we reveal an interplay in lipid interaction: Once PIP2 is interacting at the primary site, interactions of PS at the secondary interaction site become more energetically favorable, rebalancing the likelihood of PS vs. PIP2 interaction at the secondary site. This study demonstrates the nuanced competition between distinct protein–lipid interaction sites and highlights the need to study lipid interactions in membranes with physiological lipid compositions.The lipid compositional complexity of cell membranes can have a profound influence on the biological functions of ion channels. The current study has employed large coarse-grained MD simulations to probe the effect of lipid complexity on Kir2.2, a membrane protein with multiple, well-characterized lipid interactions. We analyzed lipid interactions of Kir2.2 in a membrane containing PIPSI Appendix, Table S1. The Kir2.2 channel crystal structure  with the MARTINI2.2 force field .The simulations performed are summarized in ce field , 13 and ce field . A singlce field . The PC ce field . The Kir2/Chol in a ratio of 10:40:15:10:25. PC, PE, and PS were modeled with 1-palmitoyl-2-oleoyl lipid tails. To generate the No PIP2 simulation, PIP2 was removed from the PM model by substitution with PE, as these two species clustered in areas of higher membrane curvature in previous PM simulations . Sph and GM3 were modeled with a monounsaturated ceramide tail. Lipids were modeled using the MARTINI 2.1 parameters, except for PIP2, modeled as described previously  and the standard MARTINI protocol. Periodic boundary conditions were applied, and a time step of 20 fs was used in all simulations. The temperature was maintained at 323 K using a Berendsen thermostat , followed by Lennard-Jones interactions over a further 10 windows of 250 ns (n = 3), employing a soft-core potential. To maintain a neutral system charge, four Na+ ions were also decoupled per PIP2 molecule. All simulations were run using GROMACS 2019. Free energies were computed using Multistate Bennett Acceptance Ratio, as calculated using alchemical-analysis  Python libraries.Protein–lipid interactions were identified using in-house scripts, making use of the NumPy , MDAnaly2 the headgroup was considered as the inositol moiety and all attached phosphate groups; for GM3, the headgroup included all sugar moieties; the Chol headgroup was considered to be the core steroid moiety and associated hydroxyl group, but not the short hydrocarbon tail.Lipid headgroups were considered to be interacting with given protein residues when within 0.65 nm of one another. In these calculations, lipid headgroups were chosen as phosphate plus choline, ethanolamine, or serine moieties for phospholipids PC, PE, and PS, respectively; for PIPhttps://github.com/taynaud/python-louvain, which uses the Louvain method  is defined as the average time a single lipid headgroup spends continuously interacting with a Kir2.2. Residence times were calculated from the normalized survival time-correlation function, σ(t):Calculation of residence times has been used in previous simulation studies to look at protein–water , 72 and 19T is the total simulation time and Nj is the total number of a lipid type with nonzero interaction time. The function ρj  has value 1 if lipid j continuously interacts with Kir2.2 from time v to time v + t (inclusive), and 0 otherwise. The value of v ran from 0 ns to T ns in steps of 1 ns, and the values of σ(t) were determined for every value of t from 0 to T ns, at 1-ns intervals. σ(t) was normalized by dividing by σ(0), so that the survival time-correlation function has value 1 at t = 0. The normalized time-correlation function was modeled as a sum of exponential functions:where θ1 and θ2 were determined by fitting the values of σ(t) to a sum of exponentials. The double exponential assumes that there are two populations of protein–lipid interactions, one of short-lived interactions, where the lipid fails to bind more tightly to the protein, and the other, more long-lived interactions, where the lipid interacts tightly at the specified interaction site. The residence time of the long interaction is reported, since we are more interested in the tight protein–lipid interactions. The errors reported on residence times are the SD error of fitting to the parameter estimate.The values of 2 and PS interactions, lipid molecules were considered to be interacting at a given interaction site if one or more lipid headgroup particles were within 0.65 nm of at least one residue within the interaction site. In calculating Chol residence times, the distance cutoff was set conservatively at 1 nm (rather than 0.65 nm), since it has been shown that Chol can be mobile even when interacting at specific interaction sites  and Matplotlib (Graphs were plotted using gnuplot 4.6 (tplotlib  and moletplotlib .https://zenodo.org/record/3634884#.XjiLOOunzOR (DOI: 10.5281/zenodo.3634884). Unless otherwise stated, all code used for analysis of simulations can be found at https://github.com/annaduncan/Kir_scripts.Files (gro) containing the final frames of all trajectories, input tpr files for each simulation, and the mdp file used for all simulations can be found at Supplementary File


Cells adjust to nutrient fluctuations to restore metabolic homeostasis. The mechanistic target of rapamycin (mTOR) complex 2 responds to nutrient levels and growth signals to phosphorylate protein kinases belonging to the AGC  family such as Akt and PKC. Phosphorylation of these AGC kinases at their conserved hydrophobic motif (HM) site by mTORC2 enhances their activation and mediates the functions of mTORC2 in cell growth and metabolism. Another AGC kinase family member that is known to undergo increased phosphorylation at the homologous HM site (Ser380) is the p90 ribosomal S6 kinase (RSK). Phosphorylation at Ser380 is facilitated by the activation of the mitogen-activated protein kinase/extracellular signal regulated kinase (MAPK/ERK) in response to growth factor stimulation. Here, we demonstrate that optimal phosphorylation of RSK at this site requires an intact mTORC2. We also found that RSK is robustly phosphorylated at Ser380 upon nutrient withdrawal or inhibition of glycolysis, conditions that increase mTORC2 activation. However, pharmacological inhibition of mTOR did not abolish RSK phosphorylation at Ser380, indicating that mTOR catalytic activity is not required for this phosphorylation. Since RSK and SIN1β colocalize at the membrane during serum restimulation and acute glutamine withdrawal, mTORC2 could act as a scaffold to enhance RSK HM site phosphorylation. Among the known RSK substrates, the CCTβ subunit of the chaperonin containing TCP-1 (CCT) complex had defective phosphorylation in the absence of mTORC2. Our findings indicate that the mTORC2-mediated phosphorylation of the RSK HM site could confer RSK substrate specificity and reveal that RSK responds to nutrient fluctuations.  It The p90 ribosomal S6 kinase (RSK), another member of the AGC kinase family functions in translation, metabolism, cell adhesion/migration and becomes deregulated in diseases such as cancer ,24,25,26pKFLAG-CCTβ was obtained from Dr. John Blenis (Weill Cornell)  and HA-t−/− MEFs were cultured in complete DMEM (Sigma D-6546) . After culturing for 24 h reaching approximately 70–80% confluency, cells were resuspended either in fresh complete media or starvation media ; glutamine starvation media-Corning 15-017-CV) as described previously [HeLa, WT and SIN1The generation of mice with T cell-specific rictor deletion and thymocyte harvest were described previously . For stiProtein concentrations of cell lysates were determined by Bradford assay and samples (10–30 μg) were subjected to SDS-PAGE. Proteins were transferred onto Immobilon-PVDF (Millipore). Blots were incubated with primary antibodies overnight followed by washing in PBS-Tween. After incubation with secondary antibody for 1 h, blots were washed again. Images were visualized with a SuperSignal ECL detection kit  and captured using a Amersham Imager 600 (GE). For immunoprecipitations, lysates were Pre-Cleared by adding Protein G Sepharose beads , then allowed to tumble for 1 h at 4 °C. Supernatants were recovered then incubated with antibody overnight followed by an additional 1 h incubation with Protein G Sepharose beads at 4 °C. Beads containing immunoprecipitates were washed 3× with TBS . WT MEFs were Co-Transfected with GFP-SIN1β and HA-RSK1-WT or HA-RSK1-SA mutant by PEI  . After 24 h, cells were changed into DMEM with 10%FBS. 24 h later, cells were resuspended in media with or without serum and incubated for 15 min. Cells were fixed using 1% PFA, blocked and stained for HA and DiI in PBS containing 1% BSA. Images were acquired on a Leica SP8 confocal Laser-Scanning microscope and processed using Bitplane Imaris 9.1.2 .−/− murine embryonic fibroblasts (MEFs) [−/− cells  , we exam/− cells B. Upon r/− cells ,34. Sinc/− cells ,38, we n pHM RSK C. We alsnditions D. We theymocytes E. ERK1/2−/− MEFs, correlating with abrogated ERK1/2 phosphorylation in the latter cells  could also have the same effect. 2-DG did not affect RSK pHM in the presence of glucose but further increased RSK phosphorylation in the absence of glucose E. The st−/− cells cultured for 1 h in the presence of glucose, glutamine and serum  or the phospho-deficient mutant HA-RSK1-Ser381Ala (HA-RSK1-SA) that was devoid of HM phosphorylation even after serum restimulation . In the −/− thymocytes whereas the S6K1/mTORC1-mediated phosphorylation, Ser240/244 S6, was not altered. Another target of RSK that undergoes phosphorylation at the consensus Akt phosphorylation motif (RXRXXpS/pT) is CCTβ, a subunit of the chaperonin T-Complex protein-1 ring complex (TRiC/CCT) [−/− MEFs and found that its total protein levels were diminished in these cells , which detects phosphorylated epitope corresponding to the consensus RXRXXpS/T, we found that whereas CCTβ from the WT increased its phosphorylation upon serum addition, immunoprecipitated CCTβ from the SIN1−/− MEFs had no discernible phosphorylation even up to 30 min of serum stimulation  . We firsse cells B. To facmulation C. Hence,mTORC2 regulates cell growth and metabolism in response to levels of growth factors and nutrients. Among its known targets are protein kinases that are members of the AGC kinase family including AKT, PKC, and SGK1 ,41. HereFirst, we found that mTORC2 is required for optimal phosphorylation of RSK at Ser380, which is located at the HM. This site is flanked by two catalytic domains, the NTKD and CTKD, which are phosphorylated at the activation loop (T-Loop) by PDK1 and ERK, respectively ,26. ThesSecond, we report here that RSK HM phosphorylation is robustly increased by nutrient withdrawal, a condition that also activates mTORC2 . We have−/− MEFs, whereas S6K1 phosphorylation is not [Third, we uncover that the RSK substrate CCTβ is also modulated by mTORC2. CCTβ, which is part of the multi-protein chaperonin complex (TRiC/CCT) is involved in folding of nascent polypeptides such as those involved in the cytoskeleton ,61. Althn is not , it is pn is not . Hence, RSK and mTOR are important drug targets for diseases including cancer, cardiovascular and neurological disorders ,63. Our