The ability to obtain a homogeneous sample of protein is invaluable when studying the effect of alterations such as post-translational modifications (PTMs). Selective functionalization of a protein to investigate the effect of PTMs on its structure or activity can be achieved by chemical modification of cysteine residues. We demonstrate here that one such technique, which involves conversion of cysteine to dehydroalanine followed by thiol nucleophile addition, is suitable for the site-specific installation of a wide range of chemical mimics of PTMs, including acetylated and dimethylated lysine, and other unnatural amino acids. These reactions, optimized for the clinically relevant kinase Aurora-A, readily proceed to completion as revealed by intact protein mass spectrometry. Moreover, these reactions proceed under non-denaturing conditions, which is desirable when working with large protein substrates. We have determined reactivity trends for a diverse range of thiol nucleophile addition reactions at two separate sites on Aurora-A, and we also highlight limitations when using thiol nucleophiles that contain basic functional groups. We show that chemical modification of cysteine residues is possible not only on a flexible surface-exposed loop, but also within a deep active site pocket at the conserved DFG motif, which reveals the potential use of this method in exploring enzyme function through modification of catalytic site residues. Post-translational modifications (PTMs) play a crucial role in the function and regulation of numerous proteins In the past, synthetic mimetics of PTM-modified residues have been incorporated into recombinant proteins by two main techniques: 1) expressed protein ligation in vitro chemical modification of cysteine residues. Recombinant proteins that have been engineered to contain a cysteine residue at a selected site are subjected to synthetic procedures that result in site-specific incorporation of a structural and/or functional mimic of a desired PTM. This approach has been used to generate proteins containing a wide variety of PTMs, such as phosphorylation, glycosylation, methylation, acetylation, and lipidation, which has enabled structural and functional studies that would not otherwise have been feasible A relatively new approach to selectively introduce desired PTMs is through Many of the existing literature examples of PTM-incorporation via cysteine functionalization have been carried out on simple, small (<15 kDa) proteins, and often under denaturing conditions. However, using an optimized version of the “tag and modify” approach developed by Chalker and co-workers The two constructs of AurA used for this work (T288C and F275C) contain a number of mutations introduced by site-directed mutagenesis . As descDha proceeds to completion within 2 hours consistent with the location of C288 on a flexible surface-exposed loop; however reaction at C275, which is located deep in the kinase ATP binding pocket, does not proceed to completion in 2 hours, resulting in a small amount of residual cysteine at this position which is carried through to the second step of the reaction. We then reacted a wide range of thiol nucleophiles with C288Dha and C275Dha  from cysteine . Formati C275Dha  and dete C275Dha –S3. Consreaction . We prop1–9). We used 5000 eq. of each thiol reagent as standard conditions, however for one example, 4-methoxybenzenethiol (MOBZ), we determined the minimum excess required to drive full conversion. We found AurA 288Dha reacted completely to the desired MOBZ product when >250 eq. of MOBZ were used; whereas reactions with up to 250 eq. MOBZ still contained unreacted Dha after 2 hours (10–12), with the exception of 4-fluorobenzenethiol , which did not react at either site presumably as a result of lower nucleophilicity. Representative examples of different thiol reactions that showed excellent conversion at both sites are provided in the Supporting Information, including LC-MS/MS confirmation of the site of modification and quantification of protein recovery after the reaction, which is typically around 75% . Negatively charged reagents either did not react, or did not react to completion (14–16) consistent with previous work in which thiophosphate [(SPO3)3−] addition to Dha at C288 was incomplete and required purification to obtain the desired product 14) and 2-mercaptoacetic acid . Using positively charged thiol reagents gave mixed results (17–19). Reactions using 2-(4-methylpiperazin-1-yl)ethanethiol  were unsuccessful whilst reactions with 2-(dimethylamino)ethanethiol  worked efficiently to give the desired product, however adducts of 102–103 Da were observed on both C288 and C275 samples (vide infra); and reaction of C275 with 2-aminoethanethiol  resulted in multiple unidentified peaks observed by LC-MS with only a small amount of the C275Dha starting material detected. A thermal denaturation screen, conducted to optimize buffer conditions, revealed that 2-aminoethanol completely unfolds AurA when used as a buffer component at a concentration of >5% (v/v)  . It is tN-acetyl-lysine using N-acetylcysteamine . This reaction was successful at both the C288 and F275C sites , however, extra mass additions of 102–103 Da were also observed on both samples . The extra mass was indicative of disulfide-linked adducts of the 105 Da MNES reagent with one of the internal cysteines within AurA  or Rosetta 2 (Merck) DE3 cells in LB medium, with initial growth at 37°C followed by overnight incubation at 21°C after induction with 1 mM isopropyl β-D-1-thiogalactopyranoside. Bacterial pellets were resuspended in lysis buffer , supplemented with protease inhibitors and DNase (Roche), lysed by sonication and clarified by centrifugation. Lysates were filtered then subjected to Ni2+ affinity chromatography . Proteins were eluted in lysis buffer containing 250 mM imidazole, purified to homogeneity by S75 size exclusion chromatography  ), then concentrated to ∼10 mg/mL and flash frozen for future use. Protein concentrations were measured in triplicate with a ND-1000 Spectrophotometer (NanoDrop) using molecular weights and extinction coefficients calculated by ProtParam (ExPASy).Site-directed mutagenesis of AurA kinase domain (122–403) in a pET30-based vector was carried out using a QuikChange protocol (Agilent). The mutations were C290A, C393A, and either F275C for the 275 construct or T288C and T287A for the 288 construct. Proteins were expressed in α,α′-Di-bromo-adipoyl(bis)amide (DBAA) was synthesized and characterized as described *Typically 12.5–40 µL protein was used for each anticipated thiol addition reaction. 125 µL of protein was used for each reaction where final clean-up and quantitation were intended, in order to maintain high enough protein concentration for accurate quantitation (i.e. >0.5 mg/mL).Mass spectrometry methods are available as Figure S1Conversion of cysteine to dehydroalanine on AurA C288 (A) and AurA C275 (B). Intact protein LC-MS indicates complete conversion on C288, and nearly complete conversion on C275 with a small amount of cysteine left unreacted.(TIF)Click here for additional data file.Figure S2Dha with thiol nucleophiles.Reaction of AurA C288 Unidentified peaks are indicated by a question mark.(PDF)Click here for additional data file.Figure S3Dha with thiol nucleophiles.Reaction of AurA C275 Unidentified peaks are indicated by a question mark. Cysteine present in reaction mixtures is due to incomplete cysteine to dehydroalanine conversion.(PDF)Click here for additional data file.Figure S4Dha.Optimization of amount of MOBZ required for full conversion of AurA C288 Dha is completely eliminated after 2 hours when using >250 eq. of MOBZ.(TIF)Click here for additional data file.Figure S5Examples of thiol nucleophile addition reactions that worked well on AurA C288 (A–C) and AurA C275 (D–F). LC-MS/MS confirms the location of chemical modifications. The reaction yield is calculated by measuring the total protein recovery of modified AurA (i.e. after Dha and thiol nucleophile reactions and clean-up) and comparing against the amount of starting material, as it is not feasible to accurately quantitate chemical conversion by intact mass spectrometry.(TIF)Click here for additional data file.Figure S6Thermal denaturation curves of AurA with dimethylformamide and 2-aminoethanol  included as buffer components. Dimethylformamide is tolerated well by AurA, whereas 2-aminoethanol appears to completely denature the protein when used at concentrations above 5%. n/d =  Not determinable.(TIF)Click here for additional data file.Protocol S1Mass spectrometry methods.(PDF)Click here for additional data file.


We constructed a mechanistic, computational model for regulation of (macro)autophagy and protein synthesis (at the level of translation). The model was formulated to study the system-level consequences of interactions among the following proteins: two key components of MTOR complex 1 (MTORC1), namely the protein kinase MTOR (mechanistic target of rapamycin) and the scaffold protein RPTOR; the autophagy-initiating protein kinase ULK1; and the multimeric energy-sensing AMP-activated protein kinase (AMPK). Inputs of the model include intrinsic AMPK kinase activity, which is taken as an adjustable surrogate parameter for cellular energy level or AMP:ATP ratio, and rapamycin dose, which controls MTORC1 activity. Outputs of the model include the phosphorylation level of the translational repressor EIF4EBP1, a substrate of MTORC1, and the phosphorylation level of AMBRA1 (activating molecule in BECN1-regulated autophagy), a substrate of ULK1 critical for autophagosome formation. The model incorporates reciprocal regulation of mTORC1 and ULK1 by AMPK, mutual inhibition of MTORC1 and ULK1, and ULK1-mediated negative feedback regulation of AMPK. Through analysis of the model, we find that these processes may be responsible, depending on conditions, for graded responses to stress inputs, for bistable switching between autophagy and protein synthesis, or relaxation oscillations, comprising alternating periods of autophagy and protein synthesis. A sensitivity analysis indicates that the prediction of oscillatory behavior is robust to changes of the parameter values of the model. The model provides testable predictions about the behavior of the AMPK-MTORC1-ULK1 network, which plays a central role in maintaining cellular energy and nutrient homeostasis. In modern societies, aging is arguably the most unavoidable of all maladies. Encouragingly, a number of factors that mitigate the negative effects of aging and prolong lifespan and/or healthspan have been discovered . SeveralBesides playing a role in aging and aging-related diseases, autophagy serves important functions in immunity , protects against neurodegeneration , and acts as a double-edged sword in tumorigenesis  . Thus, uA key negative regulator of autophagy is MTOR , a serine/threonine kinase that has been described as a master regulator of cell growth and metabolism. MTOR is responsible for processing numerous signals, including nutrient levels, such as leucine abundance, and stimulation from growth factors, such as insulin and insulin-like growth factor 1 (IGF1). In addition to regulating autophagy, MTOR is involved in regulating and coordinating related processes, such as protein synthesis. Protein synthesis and autophagy are connected in that a major outcome of autophagy is the liberation of amino acids for use in protein synthesis .kcat and KM of the enzyme-catalyzed reaction occurring on the scaffold [MTOR regulates autophagy by phosphorylating UNC-51-like kinase 1 (ULK1) and regulates protein synthesis by phosphorylating substrates such as ribosomal protein S6 kinase 1  and eukaryotic initiation factor 4E (eIF4E)-binding protein 1  . Indeed,scaffold . The mulEIF4EBP1 is arguably the MTORC1 substrate that is most clearly involved in facilitating MTORC1-mediated regulation of protein synthesis. It is a translational repressor that is active when hypophosphorylated and inactive when phosphorylated by MTORC1 . Thus, MAs noted above, regulation of autophagy by MTORC1 occurs through phosphorylation of the serine/threonine kinase ULK1. Through its kinase activity, ULK1 initiates a cascade of signaling events that promotes the formation of autophagosomes, the double-membrane vesicles responsible for autophagic recycling through transport of cytoplasmic contents to lysosomes. ULK1 kinase activity depends on activating phosphorylation by the energy-sensing, multimeric kinase AMPK (AMP-activated protein kinase) , which mULK1 kinase activity is inhibited by mTORC1 in two ways: by the kinase activity of MTOR (in MTORC1) and by the binding activity of RPTOR ,14. The Through the mechanisms described above, the interactions of MTORC1 and ULK1 form a mutual repression circuit, in which each is both an enzyme and a substrate. This circuit is similar to the genetic toggle switch constructed by Gardner et al.  in theirHere, to determine if mutual inhibition of MTORC1 and ULK1 potentially contributes to control of autophagy and/or protein synthesis in an all-or-none manner or allows oscillations, we constructed a mechanistic model that captures interactions among the signaling proteins discussed above: the two key members of MTORC1, MTOR and RPTOR; the autophagy-initiating kinase ULK1; AMPK, which activates ULK1 and represses MTORC1; AMBRA1, a substrate of ULK1 critical for autophagy activation; and EIF4EBP1, which is taken as a representative of the downstream targets of MTORC1 that are relevant for MTORC1-mediated activation of protein synthesis. In the model, we consider two inputs or signals: the amount of rapamycin in complex with FKBP1A , which allosterically inhibits MTORC1 kinase activity ,18; and Analysis of the model indicates that bistability can arise from mutual inhibition of MTORC1 and ULK1 together with multisite (ULK1-mediated) phosphorylation of RPTOR. Analysis of the model also indicates that oscillations in MTORC1 activity can be generated as a consequence of slow negative feedback from ULK1 to AMPK, depending on the stress inputs, which affect the kinase activities of AMPK and MTORC1. According to the model, oscillations in MTORC1 activity can drive alternating periods of mutually exclusive autophagy and protein synthesis. A sensitivity analysis indicates that model-predicted behaviors are robust to parameter uncertainties, meaning that qualitative behaviors are maintained over a large region of parameter space around the parameter values presented in On the basis of a literature survey and with the goal of investigating the possibility of bistable switching between translation and autophagy, we constructed a computational model that accounts for mutual inhibition of MTORC1 and ULK1 . The modThe model was formulated in terms of rules for protein-protein interactions see . The intThe mechanistic details depicted in Parameter values of the model, which were set as described in the Materials and Methods section, are summarized in In our parameterization of the model , the neg2 (a saddle node bifurcation) is reached. At this point, AMBRA1 phosphorylation jumps to a high level and EIF4EBP1 phosphorylation jumps to a low level.The bifurcation diagrams of Thus, given our model structure Figs.  and 2, pAn aspect of the qualitative system behavior revealed in In this section, we analyze consequences of negative feedback from ULK1 to AMPK. In To further investigate this behavior, we performed a bifurcation analysis . We founThe bifurcation diagrams of In summary, The full range of possible qualitative behaviors of the system over input space, with and without negative feedback from ULK1 to AMPK, is revealed in p9 to zero , we found that negative regulation of MTORC1 by AMPK ,26, whic to zero . In our analysis .p9 and AMPK* level (p9 and rapamycin* level (p9). For p9 above a threshold value, the system is monostable for all stress inputs and its responses to stimuli are graded. Thus, remarkably, negative regulation of MTORC1 by AMPK, which may require localization of AMPK to the Ragulator complex on lysosomes [In the two-dimensional bifurcation diagrams of K* level  and p9 n* level . In Fig.ysosomes , can det6 copies per cell (under the condition where rapamycin is absent) and levels of rapamycin* from 0 to 105 copies per cell (under the condition where the level of AMPK* is 3×104 copies per cell). The bars in To further characterize how system behavior depends on parameter values, we systematically varied single parameter values with the goal of delimiting the region of parameter space where patterns of responses to inputs match those of the model with nominal parameter values . As a consequence, the value of u0 sets the system’s characteristic response time (1/u0 = 100 s), the time scale on which responses to stress inputs occur. Thus, it is not surprising that system behavior depends most sensitively on the value of u0. The rate constant u2 characterizes dephosphorylation of ULK1-phosphorylated sites in AMPK. These sites are involved in negative feedback from ULK1 to AMPK. Thus, the value of u2 controls the time scale of negative feedback, which as we have seen above, plays an important role in determining qualitative system behavior. The third most sensitive parameter is p6 , which promotes lysosome biogenesis and autophagy by increasing the transcription of genes underlying these processes . TFEB isThe range of qualitative behaviors that can potentially be exhibited by the AMPK-MTORC1-ULK1 regulatory network is consistent with results from earlier theoretical/modeling studies focused on understanding the design principles of simple regulatory circuits and the behaviors of various specific systems. For example, it is well known from such studies that relaxation oscillations can arise from a hysteretic switch  . We founRecently, Lomnitz and Savageau  used theIs there any evidence that the AMPK-MTORC1-ULK1 network behaves as predicted? There is some experimental evidence consistent with hysteretic switching and/or oscillations arising in this network. Procaccini et al.  inferredAbove, we raised the possibility that there may be localized, asynchronous oscillations across different sites of autophagosome production (and protein synthesis) in a cell. We raised this possibility because of the punctate distribution of ULK1 ,39,40 anReturning to the issue of epistemic uncertainty, we caution that the structure of our model is based on simplifying assumptions that may need to be revisited as new data emerges. The model omits several interactions and sites of phosphorylation that have been reported in the literature. As discussed in Materials and Methods, most of these omitted sites and interactions are seemingly redundant, meaning that they appear to play a role that would strengthen influences and effects already considered in the model. However, others are enigmatic, or beyond the intended scope of our study, which is limited to direct interactions among AMPK, MTORC1 and ULK1 and the noted inputs and outputs. Thus, the model does not include, for example, TRAF6-mediated positive feedback from AMBRA1 to ULK1 , indirecNegative feedback regulation from ULK1 to AMPK can be viewed as a regulatory mechanism that protects a cell against excessive autophagy , which could compromise its viability. It is intriguing to consider the possibility that negative feedback regulation, in combination with mutual inhibition of MTORC1 and ULK1, may have the more sophisticated function of generating pulses of autophagy separated by periods of translation that allow use of the amino acids generated through autophagy and recovery from self-eating. Dunlop and Tee  hypothesTo formulate the model, we used a rule-based approach, which simplifies the consideration of site-specific details  . We usedThe focus of this section is on explaining the formal representation of molecules considered in the model.Rapamycin in complex with FKBP1A (UniProt entry P62942) binds MTOR ,18, whicrapa(mtor)The level of rapamycin* is treated as an input. The level of rapamycin* is not affected by the dynamics captured in the model. We chose to study responses to rapamycin rather than physiological stimuli, such as amino acids or growth factors, because rapamycin treatment is a stimulus that can be readily manipulated in the laboratory and rapamycin directly modulates MTORC1 activity. Regulation of MTORC1 activity in response to physiological stimuli is less direct and mediated by a complex regulatory network, which integrates multiple signals.As a simplification, we only consider the form of AMPK with activating phosphorylation in its kinase domain, i.e., the form of AMPK with phosphorylation at T172 (T183) in the PRKAA2 (PRKAA1) isoform of the α subunit of AMPK (UniProt entries P54646 and Q13131). We denote this form of AMPK as AMPK*. In the model, AMPK* interacts directly with ULK1 via an undefined region in AMPK*. Furthermore, AMPK* phosphorylates sites in RPTOR and ULK1, and AMPK* is a substrate of ULK1. The kinase domain of AMPK* is considered implicitly. We represent AMPK* as follows:AMPKThe ulk1 component represents the undefined region in AMPK* that mediates interaction with ULK1. The ST component represents serine/threonine residues in the α subunit of AMPK that are phosphorylated by ULK1. This component is taken to have an internal state, which is either “unphosphorylated” (0) or “phosphorylated” (P). Internal states are abstractions useful for representing local properties of sites, such as location, conformation, or post-translational modification status. In a molecule type definition, the names of all possible internal states of a site are listed after the name of that site, with each state name being prefixed by a tilde. The level of AMPK* is treated as an input. The level of AMPK* is not affected by the dynamics captured in the model.MTOR (UniProt entry P42345) interacts directly with rapamycin* and RPTOR . The intMTORIn the model, MTOR’s kinase is taken to be active when bound to RPTOR, such that only MTOR in complex with RPTOR is able to phosphorylate substrates. As a simplification, the only components of MTORC1 that we track in our model are MTOR and RPTOR. Thus, the complex of MTOR and RPTOR is taken to be equivalent to MTORC1.RPTOR (UniProt entry Q8N122) interacts directly with MTOR, ULK1, and EIF4EBP1. The interactions with MTOR and EIF4EBP1 are mediated by WD40 repeats and the RNC domain in RPTOR, respectively. The interaction with ULK1 is mediated by an undefined region in RPTOR. Several sites in RPTOR, including S792, S855, and S859, are substrates of AMPK and/or ULK1 ,15. We rRPTORThe ulk1 component represents the undefined region in RPTOR that mediates interaction with ULK1. The S792 component is allowed to have one of two internal states: 0, which is taken to represent the unphosphorylated form of S792, or P, which is taken to represent the phosphorylated form of S792. As a simplification, the S855 and S859 residues are lumped together and represented by a single component named S855_S859. This component is taken to have one of three internal states: unphosphorylated (0), singly phosphorylated (P), or doubly phosphorylated (PP).ULK1 (UniProt entry O75385) interacts directly with RPTOR and AMPK; these interactions are mediated by undefined regions within the proline/serine-rich (PS) domain of ULK1 . SeveralULK1The components named straptor and stampk represent the undefined regions responsible for interactions with RPTOR and AMPK. The component S758 is a substrate of MTORC1, and the components S317 and S778 are substrates of AMPK. These components are each taken to have one of two internal states: unphosphorylated (0) or phosphorylated (P). In the model, ULK1’s kinase is taken to be active when phosphorylated at both S317 and S778 and not phosphorylated at S758.When recruited to RPTOR via its RCR domain, EIF4EBP1 (UniProt entry Q13541) can be phosphorylated by MTORC1 at several sites, including T37, T46, S65 and T70 . PhosphoEIF4EBP1As can be seen, as a simplification, we represent S65 and T70 (and other less rapamycin sensitive sites) together by a single component named S65_T70. This component is taken to have an internal state: either unphosphorylated (0) or phosphorylated (P). EIF4EBP1 is included in the model as a representative of MTORC1 substrates involved in regulating translation. EIF4EBP1 represses translation when hypophosphorylated and is inert when phosphorylated by MTORC1.AMBRA1 (UniProt entry Q9C0C7) is phosphorylated by ULK1. We represent AMBRA1 as follows:AMBRA1(ST∼0∼P)The ST component represents serine/threonine residues that are phosphorylated by ULK1. This component is taken to have an internal state: either unphosphorylated (0) or phosphorylated (P). AMBRA1 is included in the model as a reporter of ULK1 kinase activity and autophagy level. Phosphorylation of AMBRA1 by ULK1 promotes autophagy.The focus of this section is on explaining the formal representation of interactions considered in the model. The interactions discussed here are those that seemed most relevant for understanding mutual inhibition of MTORC1 and ULK1 after a literature search aimed at identifying interactions among AMPK, MTORC1, and ULK1. Not all known interactions among this triad are included in the model; omitted interactions are considered below and also in the Discussion section.FKBP1A-bound rapamycin (rapamycin*) can interact with MTOR provided that MTOR is not bound to RPTOR. Rapamycin* binding to MTOR and RPTOR binding to MTOR are mutually exclusive. We represent reversible binding of rapamycin* to MTOR as follows:RPTOR can interact with MTOR provided that MTOR is not bound to rapamycin*. We represent reversible binding of RPTOR to MTOR as follows:RPTOR can interact reversibly with ULK1. This interaction, which is mediated by undefined regions in RPTOR and ULK1, is destabilized by phosphorylation of RPTOR at S792, S855, and S859. We represent reversible binding of RPTOR to ULK1 using three unidirectional rules, as follows:RPTOR can interact reversibly with EIF4EBP1. This interaction occurs between the RNC domain in RPTOR and the RCR domain in EIF4EBP1. We represent reversible binding of RPTOR to EIF4EBP1 using two unidirectional rules, as follows:AMPK can interact reversibly with ULK1. This interaction occurs between an undefined region in AMPK, denoted ulk1, and an undefined region in ULK1, denoted stampk. We represent reversible binding of AMPK to ULK1 using two unidirectional rules, as follows:We represent MTORC1-mediated inhibitory phosphorylation of ULK1 at S758 using a unidirectional rule, as follows:We represent MTORC1-mediated phosphorylation of S65 and T70 in EIF4EBP1 using a unidirectional rule, as follows:We represent ULK1-mediated inhibitory phosphorylation of S792, S855, and S859 in RPTOR using three unidirectional rules, as follows:We represent ULK1-mediated phosphorylation of AMBRA1 at undefined serine and threonine (S/T) residues using a unidirectional rule, as follows:We represent ULK1-mediated phosphorylation of AMPK at undefined serine and threonine residues using a unidirectional rule, as follows:We represent AMPK-mediated activating phosphorylation of ULK1 at S317 and S778 using two unidirectional rules, as follows:ins S317 ,55.We represent AMPK-mediated inhibitory phosphorylation of RPTOR at S792 using a unidirectional rule, as follows:We represent dephosphorylation of S758 in ULK1 as follows:We represent dephosphorylation of S65 and T70 in EIF4EBP1 as follows:We represent dephosphorylation of S792, S855 and S859 in RPTOR as follows:We represent dephosphorylation of S/T residues in AMBRA1 as follows:We represent dephosphorylation of S/T residues in AMPK as follows:We represent dephosphorylation of S317 and S778 in ULK1 as follows:We represent dephosphorylation of S792 in RPTOR as follows:Even though the interactions considered in our model are fairly well established, information about the quantitative factors that influence these interactions  is scarce. To cope with this knowledge gap, we assigned values to the parameters of the model, which are summarized in The model includes 10 rules for dephosphorylation reactions, which are given by Equations –19). Ea. Ea19). u0. We set the nominal value of u0 at 10−2 s−1 . Ea. Ea12). p1, p2, p3, p4, p7, and p8 a common nominal value, 10 s−1  because each of these rate constants characterizes a pseudo first-order reaction in which an enzyme and one of its substrates are colocalized within a protein complex, i.e., confined together within a small reaction volume.We assigned the phosphorylation rate constants , 10 s−1 . The valp5, p6, and p9, characterize pseudo second-order reactions. We assume that each of these  reactions corresponds to a Michaelis-Menten reaction scheme operating in the regime of substrate limitation, far from enzyme saturation. Thus, each second-order rate constant corresponds to a ratio of the form kcat/KM.The remaining phosphorylation rate constants, p5 at 10−4 (molecule/cell)−1s−1 −1s−1 . This sell)−1s−1  because p6, which characterizes ULK1-mediated inhibitory phosphorylation of AMPK , at 10−6 (molecule/cell) −1s−1  −1s−1 . In initp9, which characterizes AMPK-mediated inhibitory phosphorylation of RPTOR, at 0 . The. The5). a1, d1, a2, and d2 such that the affinity of rapamycin* for MTOR is 10-fold greater than the affinity of RPTOR for MTOR. In other words, we selected values for these rate constants such that a1/ d1 = 10 a2/d2. This approach is motivated by the ability of rapamycin to disrupt binding of RPTOR to MTOR [We selected values for  to MTOR .a3, d3, a5, and d5 that yield roughly balanced propensities for translation and autophagy.In initial exploratory simulations, we found that the rate constants governing ULK1 interactions with AMPK and RPTOR affect the bias of a cell toward translation (or autophagy). An increase in the affinity of ULK1 for AMPK makes a cell more prone for autophagy. Conversely, an increase in the affinity of ULK1 for RPTOR makes a cell more prone for translation. Thus, we selected values for d3,max was set such that d3,max is much greater than d3: we set d3,max = 100 d3. Recall that RPTOR, when phosphorylated at particular residues, dissociates from ULK1 faster than it otherwise would d3,max wa4 and d4, which govern interaction of EIF4EBP1 with RPTOR, so as to avoid sequestration of MTORC1 by EIF4EBP1. Recall that EIF4EBP1 is included in the model simply to serve as a reporter of MTORC1 kinase activity.Finally, we selected values for 4 copies per cell, whereas the total cellular abundances of AMPK, MTOR, RPTOR, and EIF4EBP1 are far greater, roughly on the order of 106 copies per cell [4 copies per cell. This assumption is introduced to account for sequestration of AMPK and MTORC1 away from ULK1 by their binding partners not considered in the model, as well as the different spatial distributions of ULK1 and MTORC1  [In U2OS cells, for example, the total cellular abundances of ULK1 and AMBRA1 are on the order of 10per cell . We assu MTORC1) ,63. Comp4 copies per cell. This setting was guided by the 1:1 stoichiometry of MTOR and RPTOR in MTORC1. We set the abundance of ULK1 at half the abundance of MTOR, 104 copies per cell. It is important for ULK1 to be less abundant than MTORC1 so that MTORC1 is able to fully repress ULK1 while some fraction of MTORC1 remains available to interact with EIF4EBP1, which is included in the model as a reporter for translation activity. Full repression of ULK1 seems reasonable because autophagy can be repressed to undetectable levels when cells are not stressed. The levels of EIF4EBP1 and AMBRA1 were set at 104 copies per cell. These settings are largely inconsequential for predicted system behavior, because there is no feedback from these proteins to other molecules that are considered in the model. Recall that EIF4EBP1 and AMBRA1 are included in the model only for the purpose of providing readouts of MTORC1 and ULK1 activities, which presumably correlate with translation and autophagy levels. We treat the levels of AMPK* and rapamycin* as controllable inputs. Recall that AMPK* is distinct from total AMPK and active AMPK.We set concentrations or copy numbers as follows. We set the abundances of MTOR and RPTOR to be equal, at 2×10Our model omits several known interactions among the proteins of interest. Furthermore, AMPK, RPTOR, and ULK1 each contain more sites of phosphorylation than are included in the model. These interactions and sites, which are discussed below, were omitted to keep the model as simple as possible.Some of the omitted sites have roles similar to sites included in the model. In RPTOR, S863 and S877 are additional substrates of ULK1 that are involved in ULK1-mediated inhibition of MTORC1 kinase activity . In ULK1One of the omitted sites has an unclear functional role. Shang et al.  reportedIn formulating the model, we focused on interactions among AMPK, MTORC1, and ULK1, because this triad of kinases is recognized as playing a critical role in regulation of autophagy and translation . HoweverRecently, Nazio et al.  reportedIn addition to being a component of MTORC1, MTOR is a component of a second complex (MTORC2), which is distinguished by the subunit RICTOR. Following growth factor simulation, MTORC2 promotes full activation of AKT, which subsequently signals downstream to MTORC1 . AlthougAs mentioned earlier, our model simplifies the regulation of MTORC1 by AMPK. It is well established that AMPK positively regulates TSC2  via phosphorylation, which in turn suppresses the activity of the small GTPase RHEB, a direct activator of MTORC1 . For simWe performed simulations using a deterministic method available within BioNetGen . This meEach one-dimensional bifurcation analysis was performed numerically as follows. We added a rule to the model specification  that has6 copies per cell. In the second of the two series, the bifurcation parameter was the level of rapamycin*. In all cases, we considered levels of rapamycin* from 0 to 105 copies per cell. In each series, we considered 17 different values for one of the 22 parameters: we set Pm = Pm,0 ×10n/4 for n = −8,…,+8, where m is an index, in the range [Pm,0 is the nominal value of the parameter given in Pm for which the following pattern, illustrated in Fig. C Click here for additional data file.S2 FileFig. A. Phosphorylation of AMBRA1, EIF4EBP1, and sites in RPTOR and ULK1 in translation and autophagy states of the system. When AMBRA1 phosphorylation is high, EIF4EBP1 phosphorylation is low. This pattern of phosphorylation defines an autophagy state of the system. Conversely, when AMBRA1 phosphorylation is low, EIF4EBP1 phosphorylation is high. This pattern of phosphorylation defines a translation state of the system. The bars shown here indicate the fractional phosphorylation levels of various protein sites considered in the model for a condition where bistability exists. Blue bars correspond to a translation state, which is one of two stable steady states that can be realized for an AMPK* level of 30,000 copies per cell, no rapamycin*, and other parameters set at their nominal values  phase of an oscillation as the period during which more (less) than half of all AMBRA1 is phosphorylated. (A) The red (blue) curve reports the duration of the autophagy (translation) phase as a function of AMPK* level. The level of rapamycin* is zero. (B) The red (blue) curve reports the duration of the autophagy (translation) phase as a function of rapamycin* level. The level of AMPK* is 30,000 copies per cell. In both panels, the parameters considered in Fig. C. Examples of response patterns to varying levels of a stress input. Each panel shows the response of the system to a slowly increasing level of AMPK*. The vertical axis reports the fraction of AMBRA1 that is phosphorylated. The horizontal axis reports AMPK* level. In all cases, rapamycin* is zero and parameters are set at their nominal values, except as indicated. (A) The blue curve corresponds to the case where the value of p4 is 100-fold above its nominal value, and the red curve corresponds to the case where the value of p4 is 100-fold below its nominal value. In both cases, the qualitative pattern of response is the same: low AMBRA1 phosphorylation at low AMPK* levels, oscillations at intermediate AMPK* levels, and high AMBRA1 phosphorylation at high AMPK* levels. Thus, system behavior is robust to changes in the value of p4, the rate constant for phosphorylation of S855 and S859 in RPTOR. (B) As illustrated here, system behavior is less robust to changes in the value of p6, the rate constant for inhibitory phosphorylation of AMPK. When p6 is 103/4 times its nominal value, the characteristic pattern of response is conserved (black curve). However, when p6 is 104/4 times its nominal value, the system responds monotonically to increasing AMPK* level over the range of AMPK* levels considered (green curve). (C) Similarly, when p6 is 10−2/4 times its nominal value, the characteristic pattern of response is conserved (black curve). However, when p6 is 10−3/4 times its nominal value, the system switches abruptly from low AMBRA1 phosphorylation to high AMBRA1 phosphorylation without exhibiting oscillations (green curve). The results of panels B and C indicate that system responses to changes in the level of AMPK* are robust to variations of p6 that range from 10−2/4 to 104/4 times the nominal value of p6 Click here for additional data file.


Aminoacyl-tRNA synthetases (aaRSs) are enzymes that catalyze the transfer of amino acids to their cognate tRNAs as building blocks for translation. Each of the aaRS families plays a pivotal role in protein biosynthesis and is indispensable for cell growth and survival. In addition, aaRSs in higher species have evolved important non-translational functions. These translational and non-translational functions of aaRS are attractive for developing antibacterial, antifungal, and antiparasitic agents and for treating other human diseases. The interplay between amino acids, tRNA, ATP, EF-Tu and non-canonical binding partners, had shaped each family with distinct pattern of key sites for regulation, with characters varying among species across the path of evolution. These sporadic variations in the aaRSs offer great opportunity to target these essential enzymes for therapy. Up to this day, growing numbers of aaRS inhibitors have been discovered and developed. Here, we summarize the latest developments and structural studies of aaRS inhibitors, and classify them with distinct binding modes into five categories. Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes for all cellular life, playing a central role in the translation of the genetic code ,2. Despi−4–10−5 . . 111]. Tetc. ,119,120.etc. . The smaetc. .There is no aaRS inhibitor that only binds to tRNA A76 binding site to our knowledge; possibly because the potential tRNA A76 binders may also bind to ATP binding site with better affinity.Phe might bind [The editing site is another hotspot for developing aaRS inhibitors. Several substrate analogs mimic the 3′-end of mischarged tRNA to bind the editing domain of aaRSs ,124,125.ght bind .Leu thus forming a covalent tRNALeu-AN2690 adduct in the editing site [Leu in the enzyme prevents further catalytic turnover, thus inhibiting the activity of LeuRS and consequentially blocking protein synthesis [AN2690 is a broad-spectrum antifungal compound recently approved for onychomycosis treatment . It is ting site . The addynthesis . TherefoIn order to efficiently transfer amino acids to tRNA, the three substrate-binding pockets of aaRSs  are closely located. This topology provides an important strategy for developing higher affinity aaRS inhibitors by binding to more than one substrate site in aaRS. In fact, most high affinity aaRS inhibitors are found in this class .The amino acid sulfide adenylates (aa-AMS), aminoalkyl adenylates (aa-ol-AMP), and amino acid hydroxamate AMP (aa-Hdx-AMP) are non-hydrolyzable mimetics of aa-AMP, the reaction intermediate product of aaRS A 128,12,130. TheEscherichia coli LeuRS versus the human LeuRS [Drug properties of aa-AMS may be improved by modifying the adenosine moiety. Using structure based drug design, a series of ThrRS inhibitors were successfully generated by replacing the adenosine of Thr-AMS with other fragments. Several compounds possess excellent binding affinities and greatly improved bacterial selectivity . Modifican LeuRS .Escherichia, Klebsiella, Salmonella, Shigella, and Proteus species, and then processed by cellular aminopeptidases. The processed product is a non-hydrolyzable aspartyl-adenylate analogue that inhibits AspRS.Modification of amino acid moiety into an appropriate short peptide can increase the cell permeability. Microcin C is a kind of Trojan-Horse antibiotic . With anAgrobacterium tumefaciens into a toxic product TM84  than the mammalian cytoplasmic or the mitochondrial proteins [Pseudomonas aeruginosa PheRS (PaPheRS) (PaPheRS) and Phe169. Both residues are conserved in bacterial PheRS, but Met99 is replaced by a Leu residue in mammalian cytoplasmic and mitochondrial homologues, and Phe169 is replaced by an Asn residue in mammalian cytoplasmic homologues.The phenyl-thiazolylurea-sulfonamides are a novel class of PheRS inhibitors. These compounds inhibit PheRS of gram-positive and gram-negative bacteria, with ICproteins . One typPaPheRS) F. The stWithin the same amino acid-tRNA dual inhibitor group, HF and GAX both possess nanomolar inhibition constant against their targets but with different features. The interaction of HF with ProRS requires a specific conformation in the active center this is induced by ATP binding, while GAX inhibits PheRS in an ATP-independent mode as evidenced by the ATP-free PheRS/GAX complex structure.Clostridium difficile MetRS (Ki: 20 pM) and >1000 fold selectivity over human mitochondrial and cytoplasmic MetRS [Clostridium difficile is one major toxic bacteria in human intestine to cause infectious diarrhea, this inhibitor class serves as a potential therapy for Clostridium difficile infection [REP3123 is a novel inhibitor of MetRS with ultra-high inhibition constant against ic MetRS . Analoguic MetRS ,140,141.ic MetRS . These iic MetRS . Becausenfection .There is no reported ATP-tRNA dual site aaRS inhibitor to our knowledge.Glu [No triple active site (ATP-amino acid-tRNA) inhibitor has been reported. Several reasons may explain the rareness of triple active site inhibitors: (1) The dual active site inhibitor is potent enough. For example, the simple aa-AMS could easily obtain nM inhibition. (2) To occupy the three substrate-binding sites at the same time will significantly increase the complexity for chemical synthesis. However, the triple active site inhibitor may be more potent than other inhibitors, particular for certain aaRS families. For example, class I aaRSs ArgRS, GluRS, and GlnRS only catalyze the ATP/PPi pyrophosphate exchange reaction in the presence of the cognate tRNA, indicating a synergistic co-binding of the three substrates in these aaRSs ,144. TheGlu . A conceStreptomyces sp. back in 1949 [Thr. The rest of C4–C14 portion of the molecule wedges into a hydrophobic patch of the ThrRS, and induced the formation of an extra interaction pocket (the fourth site). Most of these BN-interacting residues are highly conserved in bacteria and eukaryotes, leading to the full-spectrum (bacteria and eukaryote) inhibition. In contrast, 12 of the 18 residues are altered in archaeal ThrRS, which is insensitive to BN inhibition.Only one multi-site inhibitor (target more than three sites) was characterized so far. Borrelidin (BN) is a natural product isolated from  in 1949 . It bloc in 1949 . Unlike P. falciparum growth with an IC50 of 0.97 nM, which is more potent than the clinically used antimalarial artemether, artesunate, or chloroquine [P. falciparum compared with human cells. And the most promising compound BC220 completely clears the parasitemia at 6 mg/kg per day, which is comparable to parasite clearance by chloroquine [Among 10 tested aaRS inhibitors including BN, Glu-AMS, Gln-AMS, Asn-AMS, Tyr-AMS, Ser-AMS, thialysine, mupirocin, cispentacin, and AN2729, BN showed the strongest inhibition on Ongoing exploration of noncanonical functions of aaRS has shown important contributions to control of angiogenesis, inflammation, tumorigenesis, and other disease-related processes ,38. For Small molecule has been validated to be able to bind aaRS and regulate the non-translational function completely independent of the translational active site center A. For exin silico screening method, six and 11 compounds were selected as potential inhibitors against P. falciparum AlaRS (PfAlaRS) and ThrRS (PfThrRS) aminoacylation site [PfAlaRS inhibitor candidate 4-(2-nitro-l-propenyl)-1,2-benzenediol was able to inhibit P. falciparum 3D7 growth at a low IC50 value of 8 µM, while this compound did not show cytotoxicity against mouse L929 cells and human HeLa cells within the range of parasite IC50 values [A great number of developed aaRS inhibitors still do not have their mechanism of inhibition confirmed, waiting for being grouped into one of the five categories above . For exaion site . Among t0 values . This coetc.) with connections to both their translational function and functions beyond translation. Development of aaRS specific compounds will not only generate potential therapy for treating these human diseases, but also provide unprecedented tools to directly dissect the molecular mechanism of aaRS-related etiology and their unknown functions in biology.Aminoacyl-tRNA synthetases, particularly human aaRS have been associated to various disorders  A. Each o


Mycobacterium tuberculosis complex (MTBC) may be important for understanding the epidemiology and pathogenesis of tuberculosis (TB). In this study, we report the development of a LightCycler® real-time PCR single-nucleotide-polymorphism (LRPS) assay for the rapid determination of MTBC lineages/sublineages in minimally processed sputum samples from TB patients.Accurate and high-throughput genotyping of Genotyping analysis of 70 MTBC strains was performed using the Long Sequence Polymorphism-PCR (LSP-PCR) technique and the LRPS assay in parallel. For targeted sequencing, 9 MTBC isolates (three isolates per MTBC lineage) were analyzed for lineage-specific single nucleotide polymorphisms (SNPs) in the following three genes to verify LRPS results: Rv004c for MTB Uganda family, Rv2962 for MTB lineage 4, and Rv0129c for MTB lineage 3. The MTBC lineages present in 300 smear-positive sputum samples were then determined by the validated LRPS method without prior culturing.The LSP-PCR and LRPS assays produced consistent genotyping data for all 70 MTBC strains; however, the LSP-PCR assay was 10-fold less sensitive than the LRPS method and required higher DNA concentrations to successfully characterize the MTBC lineage of certain samples. Targeted sequencing of genes containing lineage-specific SNPs was 100 % concordant with the genotyping results and provided further validation of the LRPS assay. Of the 300 sputum samples analyzed, 58 % contained MTBC from the MTBC-Uganda family, 27 % from the MTBC lineage 4 (excluding MTBC Uganda family), 13 % from the MTBC lineage 3, and the remaining 2 % were of indeterminate lineage.The LRPS assay is a sensitive, high-throughput technique with potential application to routine genotyping of MTBC in sputum samples from TB patients.The online version of this article (doi:10.1186/s12879-015-1121-7) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis (MTB) is an acid-fast bacillus that causes tuberculosis (TB) a chronically debilitating disease with a mortality rate approaching 2 million deaths per year  with reference to the first MTBC  genome  to be se4 16 withm) values (due to single base mismatch) than samples with wild type DNA (due to perfect match), whereas the MTBC L4-NU probes will produce higher Tm values with mutant DNA (due to perfect match). We used MTBC lineage 3 (CAS), Uganda family and H37Rv DNA as controls.Primers and probes for typing MTBC Uganda family (MTB L4-U) MTBC lineage 4 excluding the MTBC Uganda family (MTB L4-NU), and MTBC lineage 3 (MTB L3) were chosen based on the list of lineage-specific SNPs described previously , 16  and RD750 deletion primers (Specific for MTB lineage 3) as described by Gagneux et al.  and TsolTo further validate the LRPS assay target sequencing of three ORFs (Rv004c for MTBC L4-U Rv2962 for MTBC L4 and Rv0129c for MTBC L3) which contain lineage-specific SNPs from 9 MTBC isolates was performed using primers in Table v/v) glycerol and OADC for culturing following standard procedures [Patients were recruited from Mulago hospital TB clinic (ward 5 & 6) which serves as the main referral TB centre in Uganda. Sample processing confirmatory microscopy (both ZN and auramine staining) and culturing was done at Mycobacteriology Laboratory, Department of Medical Microbiology, College of Health Sciences, Makerere University. 300 sputum samples were processed in a Biosafety cabinet class II following standard procedures . The finocedures  and the Genotyping of MTBC from cultured and frozen isolates or processed sputum samples was performed using ORF-specific primers and probes designed based on SNP positions  was used to derive probe melting temperature (Tm), which is lineage-specific. For MTBC Uganda family and MTB Lineage 3, the Tm was lower (due to mismatch) than the wild type (perfect match) yet for MTBC lineage 4 the Tm (due to perfect match) was higher than the wild type.This study obtained ethical approval from Makerere University institutional review board and the Uganda National Council for Science and Technology. Written informed consent was obtained from all the study participants.a and Rv0407b failed to give signals (no amplification), primer/probe set Rv3133cC and Rv2959c were giving results that were conflicting with the positive  and negative controls (H37Rv), thus these sets were excluded. Also Primer/probe set Rv2949ca was excluded from further sample analysis since it required twice the probe concentration as the counterpart. Therefore primer/probe sets Rv004ca was used to analyze samples for presence of MTBC Uganda family; Rv2962b and Rv0129cc primer sets were used to identify MTBC lineage 4 and MTBC lineage 3 respectively  software 2  respectively  were genotyped using LSP-PCR with primers specific for the RD 724 deletion in parallel with LRPS assay using Rv004cc primer/probe set). Both assays were 100 % concordant in identifying MTBC lineages 3  were sequenced. The resulting sequences of the gene containing the lineage-specific SNP were compared with the corresponding H37Rv sequences using Bio Edit software  the data confirmed that the sequenced PCR products contained lineage-specific SNPs . Of those 58 % (174/300) were MTBC L4-U, 27 % (82/300) MTBC L4-NU, 13 % MTBC L3 (39/300) and 2 % (5/300) could not be classified  data from global MTBC strains , 30, 31.5 copies/reaction) enough to be used with low DNA concentration as well as with heat-inactivated samples. The advantage of using heat lysates eliminates the long steps in DNA extraction which is labor intensive and time consuming. Thus, the LRPS may be better suited for genotyping MTBC from processed sputum samples, which can have a low bacillary load. The enhanced ability to detect small amounts of DNA in a sample by LRPS can likely be attributed to the more sensitive fluorescence detection system of the Roche LightCycler® 480  machine [The findings show the successful use of 3 sets of primers/hybridization probes containing MTBC lineage-specific SNPs in RT-PCR (LRSP) to accurately delineate MTBC lineages (MTBC L4-U MTBC L4-NU and MTBC L3). The LRSP assay is based on coupling a fluorescent dye to an amplified segment of DNA and use of MTBC lineage specific probes-conjugated to dyes that ensures real-time identification of the genotype in 2 h for every 92 samples. This is contrary to other MTBC genotyping assays that require different step(s) for detection of the genotype, hence increasing the turnaround time 1–3 days depending on the method . Compari machine . In cont machine . To supp machine . Taken tUnlike the LRPS most available genotyping methods rely on prior culturing of MTBC, which is labor-intensive, time-consuming, and introduces the risk of selective growth in cases of mixed infections . While pWhile the probes used in this study were designed to rapidly identify the three MTBC lineages circulating in peri-urban Kampala  additionThe LRPS assay is a sensitive rapid, simple and high-throughput technique for detecting and/or genotyping MTBC from minimally processed, smear positive sputum and should be broadly applicable to genotyping SNPs in other microorganisms.


Curcumin (CUR), an active polyphenol derived from the spice turmeric, has been traditionally used for centuries in ancient Indian medicine to treat a number of diseases. The physiological effects of CUR have been shown to be diverse; however, the target molecules and pathways that CUR affects have yet to be fully described.(GPD1), a factor that is essential for the hyperosmotic stress response.Here, we demonstrate for the first time that the budding yeast mitogen-activated protein kinase (MAPK) Hog1 is essential for the response to CUR. Moreover, CUR-induced Hog1 phosphorylation was rescued by supplementation of iron to the growth medium. Hog1 was rapidly phosphorylated upon CUR treatment, but unlike the response to hyperosmotic shock (0.8 M NaCl), it remains activated for an extended period of time. A detailed analysis of HOG pathway mutants revealed that Pbs2p, Ptc2p, and Ssk2p are required for optimal CUR-induced Hog1 phosphorylation. We also observed a Hog1 dependent transcriptional response to CUR treatment that involved the up-regulation of glycerol-3-phosphate dehydrogenase 1 S. cerevisiae.Our present finding revealed the role of Hog1 MAPK in regulation of CUR-induced transcriptional response. We anticipate that our finding will enhance the understanding on the molecular mode of action of CUR on  S. cerevisiae [. The fundamental function of these MAPK pathways is to regulate gene expression in response to various extracellular signals.Yeast cells have evolved sophisticated mechanisms to withstand a variety of stresses including limited availability of nutrients, fluctuations in temperature, changes in osmolarity, and the presence of harmful agents such as radiation or toxic chemicals. A myriad of strategies have evolved to maintain cellular homeostasis under stressful conditions including the activation of mitogen-activated protein kinase (MAPK) pathways. Thus far, 5 MAPK pathways have been characterized in revisiae . The funThe high osmolarity glycerol (HOG) pathway is one of the most thoroughly studied MAPK pathways in yeast. The HOG pathway involves the MAPK Hog1 that specifically responds to increased extracellular osmolarity and is essential for cell survival under these conditions. Yeast cells respond to osmotic stress by activating Hog1 phosphorylation and, subsequently, translocating Hog1 to the nucleus where it directly interacts with several transcription factors to modulate gene expression. Recently, several studies have demonstrated additional functions of the HOG MAPK pathway. Evidence shows that the HOG pathway is essential for regulating the stress adaptation response induced by heat , citric Curcumin (diferuloylmethane) is the principal bioactive agent in the spice turmeric . TurmeriWe demonstrate that CUR exposure in yeast cells leads to phosphorylation of Hog1 and up-regulation of GPD1 mRNA levels. The findings presented here strongly indicate that the ability of CUR to induce the osmoresponse may underlie many of the therapeutic activities of CUR.4.7H2O  were purchased from Sigma-Aldrich. To make synthetic complete (SC) media, amino acids, yeast nitrogen base (YNB), and ammonium sulfate were mixed together as per standard protocol [Saccharomyces cerevisiae strains used in this study are listed in Table Unless otherwise stated, all the chemicals were purchased from Sigma-Aldrich. Curcumin C1386), Bathopenanthrolinebisulfonic acid- BPS (B1375) and, FeSOprotocol . Yeast c386, BathFACS analysis was performed as described earlier ,19. Brie2, and 0.4 M Sorbitol) and lysed in lysis buffer  with 8 strokes in a chilled tight-fitting pestle dounce homogenizer, after 15min incubation on ice. Unbroken cells and debris were removed by centrifugation for 5min at 300g at 4°C. The cytoplasmic fraction (supernatant) was collected after a spin at 13000g for 20 min at 4°C and the nuclear-enriched fraction (pellet) was washed once with lysis buffer before collection. Normalized volumes of cytoplasmic fraction and nuclei fraction were then subjected to western-blotting analysis.The spheroplasts were made from yeast cells as described earlier . spheropWhole cell extracts from untreated and CUR-treated samples were prepared using the trichloroacetic acid (TCA) extraction method as described previously . Westernhog1Δ cells were treated with CUR (100 μM). Total RNA was isolated at the indicated time points  using the heat/freeze phenol method described elsewhere [Exponentially growing wild type yeast cells or lsewhere . 1μg of To test the dose dependent effect of CUR on yeast cell cycle progression, we performed FACS analysis. Exponentially growing yeast cells were synchronized in the G1 phase using alpha-factor. After synchronization, G1 arrested cells were released into DMSO (control) or CUR (50 or 100 μM) supplemented media. The results from the FACS analysis revealed that the DMSO-treated cells quickly moved to the G2 phase within 30 minutes of release from the alpha-factor arrest  and histone deacetylases (HDACs) that are required to provide tolerance to CUR-induced stress . One suce to CUR , we hypoNext, we were interested to analyze the time point kinetics of CUR-induced Hog1 phosphorylation. As a positive control, 0.8M NaCl treated samples were analyzed for Hog1 phosphorylation and as per reports -29 phosp4) and BPS (Iron chelator) on Hog1 phosphorylation. As shown in Figure Since, we have observed that iron supplementation can rescue the growth inhibition caused by CUR treatment Figure . Therefohog1Δ cells with alpha-factor to arrest them in G1 phase as described in materials and methods. G1 arrested hog1Δ cells were released in fresh media containing DMSO or media supplemented with 100 μM CUR. The results from the FACS analysis revealed that the DMSO-treated cells quickly moved to the G2 phase within 30 minutes of release from the alpha-factor arrest  doesn’t affect cell cycle progression  and harvested at regular intervals  after treatment. Total RNA was extracted, reverse-transcribed to cDNA, and the expression of GPD1 was analyzed using real-time PCR. Transcript levels were normalized to the reference gene ALG9. In case of wild type cells we observed a consistent 2–3 fold increase in the expression of GPD1 within 30 min of CUR exposure  and sustained  phosphorylation of Hog1 protein [Yeast Hog1 is a stress-activated kinase that is thought to be activated exclusively by osmotic stress. However, recent studies have implicated the role of this MAPK in mediating tolerance to a variety of stress conditions including osmotic , oxidati protein -29. To d protein . Previou protein . Moreove protein . Hence, a rapid <0 min andpbs2Δ mutant was defective in Hog1 phosphorylation because it is the only immediate upstream kinase for Hog1. To test the role of upstream members of the HOG pathway in the cellular response to CUR, we monitored the phosphorylation of Hog1 in yeast strains carrying mutations in the components of the HOG pathway. Interestingly, we also observed Hog1 phosphorylation defects in SSK2, MSB2, and PTC2 deletion mutants, suggesting the requirement of these factors for optimal Hog1 phosphorylation in response to CUR treatment. The comprehensive CUR-induced growth inhibition analysis in the mutants incapable of phosphorylating Hog1 warrants future study, where methods outlined by the Clinical Laboratory Standards Institute (CLSI) M38-A [Hog1 is activated through a series of phosphorylation events involving the MAPK kinase Pbs2 and several other upstream factors ,37. To tI) M38-A  can be uGPD1 (Figure S. cerevisiae to hyperosmotic stress [S. cerevisiae and this is followed by the activation of GPD1 gene expression. The CUR has several molecular targets, other than Hog1. For example, CUR is known to target various cell signaling pathways such as JAK/STAT, Wnt/β-catanin, Notch, PI3K/PKB, AMPK, DNA damage checkpoint pathway [One of the main functions of the MAPK pathway is the regulation of transcriptional events in response to specific stimuli. Because of its phosphorylation, Hog1 gets translocated to the nucleus , where i1 Figure B, which c stress . Interes pathway ,41-45 anS. cerevisiae to CUR-induced stress (Figure Recently, the identification of bioactive dietary components has received particular interest in the field of pharmacology. Curcumin has gained immense attention for its varied therapeutic and prophylactic applications. Our results reveal new aspects of the response of s Figure . We have


Although aberrant tyrosine kinase signalling characterises particular breast cancer subtypes, a global analysis of tyrosine phosphorylation in mouse models of breast cancer has not been undertaken to date. This may identify conserved oncogenic pathways and potential therapeutic targets.murine stem cell virus-Neu, expressing truncated Neu, the rat orthologue of human epidermal growth factor receptor 2, Her2 (HER2); mouse mammary tumour virus-polyoma virus middle T antigen (PyMT); and the p53−/− transplant model (p53). Pathways and protein–protein interaction networks were identified by bioinformatics analysis. Molecular mechanisms underpinning differences in tyrosine phosphorylation were characterised by Western blot analysis and array comparative genomic hybridisation. The functional role of mesenchymal–epithelial transition factor (Met) in a subset of p53-null tumours was interrogated using a selective tyrosine kinase inhibitor (TKI), small interfering RNA (siRNA)–mediated knockdown and cell proliferation assays.We applied an immunoaffinity/mass spectrometry workflow to three mouse models: Met gene amplification. Treatment of cultured p53-null cells exhibiting Met amplification with a selective Met TKI abrogated aberrant tyrosine phosphorylation and blocked cell proliferation. The effects on proliferation were recapitulated when Met was knocked down using siRNA. Additional subtypes of p53 tumours exhibited increased tyrosine phosphorylation of other oncogenes, including Peak1/SgK269 and Prex2.The three models could be distinguished on the basis of tyrosine phosphorylation signatures and signalling networks. HER2 tumours exhibited a protein–protein interaction network centred on avian erythroblastic leukaemia viral oncogene homologue 2 (Erbb2), epidermal growth factor receptor and platelet-derived growth factor receptor α, and they displayed enhanced tyrosine phosphorylation of ERBB receptor feedback inhibitor 1. In contrast, the PyMT network displayed significant enrichment for components of the phosphatidylinositol 3-kinase signalling pathway, whereas p53 tumours exhibited increased tyrosine phosphorylation of Met and components or regulators of the cytoskeleton and shared signalling network characteristics with basal and claudin-low breast cancer cells. A subset of p53 tumours displayed markedly elevated cellular tyrosine phosphorylation and Met expression, as well as Met-amplified, p53-null tumours provide a new preclinical model for a subset of triple-negative breast cancers.This study provides network-level insights into signalling in the breast cancer models utilised and demonstrates that comparative phosphoproteomics can identify conserved oncogenic signalling pathways. The The online version of this article (doi:10.1186/s13058-014-0437-3) contains supplementary material, which is available to authorized users. It is well established that tyrosine kinase signalling pathways play major roles in breast cancer development and progression. Evidence for this includes 1) the amplification of the receptor tyrosine kinase (RTK) human epidermal growth factor receptor 2 (HER2)/avian erythroblastic leukaemia viral oncogene homologue 2 (erbB2) in approximately 25% of breast cancers  the ampl, the docGene expression profiling has revealed that breast cancer can be subclassified into luminal A, luminal B, HER2, basal and claudin-low subtypes, which differ in terms of patient prognosis and response to therapy . Basal aneu (the rat homologue of HER2/erbB2) are expressed in the mammary gland . In. Inneu  promoter. The Gateway LR recombination reaction was used as per the manufacturer's instructions (Invitrogen).Neu-pMIL was used to generate the HER2 breast cancer model. This expresses a truncated form of Neu  with elevated activity due to truncation of the extracellular domain . TruncatPrimary mammary epithelial cells from FVB/N mice were cultured and retrovirally transduced with truncated Neu encoded by Neu-pMIL as described previously ,29],,..Tp53-nulMice were aged for up to 12 months to permit spontaneous tumour formation . Tumour . All animal work was approved by the animal ethics committee of Garvan Institute of Medical Research, St Vincent's Hospital.PyMT tumours were derived from MMTV-PyMT-transgenic mice on an FVB/N background as previously described . All ani18 Sep-Pak columns , and lyophilised. pY peptides were subject to IP using the PhosphoScan procedure  with pY100 antibodies as previously described ethanesulfonic acid (HEPES), 2.5 mM sodium pyrophosphate, 1 mM β-glycerol phosphate, 1 mM sodium orthovanadate, 1 mM ethylenediaminetetraacetic acid and 1 mM Tris(2-carboxyethyl)phosphine, pH 8.0. Lysates were cleared by sonication and centrifugation prior to protease digestion, phosphotyrosine (pY) immunoprecipitation (IP) and nano-liquid chromatography tandem mass spectrometry (nano-LC-MS/MS). Lysates were quantitated, and 20 mg of each sample were diluted to a final concentration of 1 M urea with 20 mM HEPES and digested overnight with trypsin at room temperature. Heavy proline (+6 Da)– and alanine (+4 Da)–labelled synthetic standard pY peptides of MK14, elongation factor Tu and EGFR were spiked into each sample at 5 pmol, 500 fmol and 10 pmol, respectively, to enable normalisation of label-free quantitative values. Peptides were then desalted and concentrated using Cescribed . pY-enri18 micro-precolumn  with H2O:ACN  at 15 μl/min for 4 minutes. The micro-precolumn was then switched online with a nano-C18 column  and the reverse phase nano-eluent was subjected to positive nanoflow electrospray analysis in information-dependent acquisition (IDA) mode (250 nl/min for 30 minutes). Mass spectra were acquired on an Orbitrap Velos mass spectrometer . In IDA mode, survey scans in the mass-to-charge ratio (m/z) range 350 to 1,750 were acquired with lock mass enabled. Up to the 15 most abundant ions  with charge states greater than +2 were sequentially isolated and further subjected to MS/MS fragmentation within the linear ion trap using collisionally induced dissociation. MS/MS spectra were accumulated with an activation time of 30 milliseconds at a target value of 30,000 ions and a resolution of 60,000. m/z ratios selected for MS/MS were dynamically excluded for 30 seconds.pY peptides were resuspended in 15 μl of MS buffer containing 1% formic acid, 2% acetonitrile (ACN) and 0.05% heptafluorobutyric acid and subjected to nano-LC-MS/MS. Peptides were separated by nano-LC using an UltiMate 3000 high-performance liquid chromatography and autosampler system . Samples were concentrated and desalted onto a CMus musculus database (Version 2010_10) containing 35,052 entries (including common contaminants) and a proportionally sized decoy database for false discovery rate (FDR) generation. The following search parameters were selected: fixed cysteine carbamidomethylation modification, variable methionine oxidation modification, variable protein N-acetylation, and variable phosphorylation of serine, threonine and tyrosine. A minimum peptide length of six amino acids and up to two missed cleavages were allowed. The initial first search mass tolerance was 20 ppm for precursor ions and 0.5 Da for fragment ions. Matches between runs were enabled with default settings and label-free quantitation enabled. The FDR was limited to 1% for both protein and peptide identification. For identification of the standard peptides, additional variable modifications of heavy proline (+6 Da) and alanine (+4 Da) were enabled.The nano-LC-MS/MS .raw files were processed with MaxQuant software (version 1.1.1.25), which uses the Andromeda algorithm for data processing, database searching and protein identification . Extract10-transformed prior to all bioinformatics analyses, and their direct comparison enabled the relative quantitation of pY sites between the p53, PyMT and HER2 GEM models of breast cancer.Raw pY peptide spectral intensities were extracted from the 'Evidence' output files generated in MaxQuant. These intensities were then normalised against pY peptide intensities for the heavy-labelled spiked-in peptide standards. These normalised intensities were then logRaw MaxQuant output files were subjected to the following filtering criteria prior to their inclusion in further bioinformatics analysis: (1) contaminants and reversed matches were removed, ensuring that all identifications were reported with an FDR of <1%; and (2) pY sites were filtered for a minimum localisation probability confidence of 0.75.k-nearest neighbour ; su; su38]; The KOBAS web server ,42] was was42] wAntibodies against the following proteins were used, with immunoblotting carried out as previously described : IRS1  according to the manufacturer's instructions. Briefly, up to 25 mg of frozen tissue was ground and digested with proteinase K overnight at 56°C. DNA was purified on a QIAamp spin column, eluted in 200 μl of water and assessed for quality using a NanoDrop spectrophotometer . Samples were cleaned up by sodium acetate/ethanol precipitation to ensure 260/280 and 260/230 absorbance ratios >1.8. DNA integrity was checked using gel electrophoresis.2 ratios. By this method, we denoted deletions to be segments with a mean value less than −0.5, amplifications to have mean value between 0.5 and 1.5 and highly amplified regions to have a mean value greater than 1.5. Using this information, we merged segmental values across the genome to create a common set of copy number levels for each individual tumour. Analysis was carried out using the Bioconductor DNAcopy package . Ex. Ex26]. These pY sites mapped to 506 unique proteins. These proteins, which were grouped based on Gene Ontology functional categories, are illustrated in Additional file To compare tyrosine phosphorylation patterns between the different tumour types and identify potential similarities, we undertook unsupervised hierarchical clustering. This revealed that two p53 tumour samples formed a distinct subgroup characterised by markedly elevated tyrosine phosphorylation of many pY sites  six of eight p53 tumour samples, (2) six of eight PyMT tumour samples or (3) four of five Her2 tumour samples. Following this selection-filtering process, 381 pY sites remained –based approach was implemented. Thirty-two pY sites were upregulated in HER2 tumours, eighty-six in p53 tumours and eighty in PyMT tumours  characteristic of each tumour type were analysed using PINA ,38] . These data indicate that the extremely high levels of tyrosine phosphorylation detected in tumour 1204  and tumour 1204 (high-level Met amplification). Pretreatment of these cell lines with the Met TKI PHA-665752 decreased Met phosphorylation on Y1234/Y1235 and markedly reduced the elevated cellular tyrosine phosphorylation levels in tumour 1204 cells Figure A. ImportInterrogation of the tyrosine phosphorylation patterns characteristic of the other p53-null subgroups revealed that phosphorylation of the atypical kinase Peak1/SgK269  was elevSpecific genetically modified mouse models of breast cancer have made major contributions to our understanding of the role of particular oncogenes in the development of this malignancy . In addiFor an erbB2 model, we used a truncated form of Neu ,27] tha tha27] tThe finding of increased Pdgfrα phosphorylation is of interest because autocrine Pdgf action is associated with TGF-β-induced epithelial–mesenchymal transition (EMT) in MMTV-Neu mammary tumours and because increased Pdgfrα and Pdgfrβ expression occur in late-stage, invasive human breast cancers . ResearcA previous study on the PyMT model revealed a dependence on PI3K activation for efficient tumourigenesis . AlthougAn important question is the identity of the tyrosine kinases driving PyMT tyrosine phosphorylation. It is well established that PyMT associates with, and is phosphorylated by, the SFKs Src and Yes, and also that expression of Src is essential for efficient PyMT-induced tumourigenesis ,50]. Ho. Ho50]. Met and Cav1 genes. This is consistent with high expression and amplification of the MET gene in human basal breast cancers ,. In. In71]. Met gene amplification highlights how mouse models of mammary tumourigenesis can be used to identify breast cancer oncogenic 'drivers'. Of note, our phosphoproteomic profiling of p53 tumours identified markedly enhanced tyrosine phosphorylation of Peak1/SgK269 and Prex2 in other p53-null tumour subsets. The former protein is an atypical kinase recently demonstrated to exhibit characteristics of a breast cancer oncogene, and site-selective Peak1/SgK269 tyrosine phosphorylation promotes biological activity [The identification of two p53 tumours exhibiting activity . Prex2 iactivity , althougOur finding that three commonly utilised mouse models of breast cancer are characterised by distinct tyrosine phosphorylation–based signalling networks provides important insights into mechanisms of mammary tumour development and progression. The identification of Met as an oncogenic driver in a subset of p53-null tumours highlights how comparative phosphoproteomics can identify conserved oncogenic signalling pathways and identifies a new preclinical model for a subset of triple-negative breast cancers.(A) The lineage of the different tumour samples characterised. (B) Clustering relationships in comparison to tumour of origin for the xenografts. The coloured lines indicate which tumour transplants share a common origin. The coloured ovals indicate instances where only one transplant tumour from a particular clonal line was characterised. (PDF 123 KB)Additional file 1: Figure S1.: Additional file 2: Table S1.: 763 pY sites identified from the pY profiling of p53, Her2 and PyMT mammary mouse tumours. (XLSX 166 KB)(A) Gene Ontology annotation of the phosphoproteins identified from the three mouse tumour models. (B) Proportion of different kinase types identified (based on number and subclassification). (C) Contribution of different non-receptor tyrosine kinases, based on pY peptide spectral intensity. (D) Contribution of different receptor tyrosine kinases based on pY peptide spectral intensity. (PDF 1 MB)Additional file 3: Figure S2.: k-NN + column mean). (PDF 245 KB)Additional file 4: Figure S3.: Unsupervised hierarchical clustering using all 763 identified pY sites. k-NN+ row min). (PDF 175 KB)Additional file 6: Figure S4.: Unsupervised hierarchical clustering using the 381 pY sites detected in at least 75% of one tumour type. (Imputation: (A) and (B) Immunoblot quantitation of phosphorylation on specific phosphosites compared with relative tyrosine phosphorylation on the same sites determined by MS. Data are presented as in Figure 2, and colour key and histogram apply to (A) and (B). The heat maps indicate the intensity of phosphorylation on the following specific sites: ErbB3 (pY1325) and p85 (pY467). The lane corresponding to tumour 1186 has been removed due to sample degradation. Because phosphorylation of Y1325 was not detected in HER2 tumour 3051 by MS, the positive signal obtained by Western blotting may reflect cross-reactivity of the commercial antibody with a different phosphorylation site on erbB3 or another erbB receptor. (PDF 611 KB)Additional file 7: Figure S5.: k-NN + column mean). (PDF 149 KB)Additional file 8: Figure S6.: Unsupervised hierarchical clustering using the 707 pY sites identified in p53 tumours. Additional file 10: Table S4.: Amplified regions on chromosome 6 for the eight p53 tumours. (XLSX 13 KB)Additional file 11: Table S5.: Pathway analysis of proteins showing >10-fold increased phosphorylation in 1203/4 versus other p53 tumours. (XLSX 23 KB)(A) MET knockdown using siRNA in cell lines derived from p53-null mouse tumours. Western blots were incubated with the indicated antibodies. # in 1201 indicates nonspecific band. (B) Effect of MET knockdown on cell proliferation by MTS assay measuring relative absorbance. At least three independent sets of replicate experiments were performed with the same trend observed. Data from one representative experiment are shown. *P < 0.05 and **P < 0.005 by t-test (siRNA treated vs control). 'Control' refers to ON-TARGETplus nontargeting siRNA, and 'Pool' refers to SMARTpool consisting of four individual siRNAs targeting mouse MET. (PDF 947 KB)Additional file 12: Figure S7.: Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Authors’ original file for figure 5Authors’ original file for figure 6Authors’ original file for figure 7Authors’ original file for figure 8Authors’ original file for figure 9Below are the links to the authors’ original submitted files for images.


Although it has been proposed that inhibition is related to individual differences in mathematical achievement, it is not clear how it is related to specific aspects of mathematical skills, such as arithmetic fact retrieval. The present study therefore investigated the association between inhibition and arithmetic fact retrieval and further examined the unique role of inhibition in individual differences in arithmetic fact retrieval, in addition to numerical magnitude processing. We administered measures of cognitive inhibition  and a complementary, more ecologically valid measure of children’s inhibition in the classroom , as well as numerical magnitude processing  and arithmetic fact retrieval  in 86 typically developing third graders. We used a correlation, a regression and a Bayesian analysis. This study failed to observe a significant association between inhibition and arithmetic fact retrieval. Consequently, our results did not reveal a unique contribution of inhibition to arithmetic fact retrieval in addition to numerical magnitude processing. On the other hand, symbolic numerical magnitude processing turned out to be a very powerful predictor of arithmetic fact retrieval, as indicated by both frequentist and Bayesian approaches. There are large individual differences in the way children acquire mathematical competencies e.g., . BecauseSince mathematics consists of various different abilities  mathematical skills, i.e., a domain-specific and a domain-general approach e.g., . Domain-people’s elementary intuitions about quantity and their ability to understand the meaning of symbolic numbers  and cognitive inhibition or interference control . Secondly we verified if both inhibition and numerical magnitude processing play a unique role in arithmetic fact retrieval.M = 8 years 9 months; SD = 4 months). For all participants, written informed parental consent was obtained.Participants were recruited from four elementary schools located in provincial towns in the middle of Flanders, Belgium and had dominantly middle- to high socio-economic background. None of them had a developmental disorder or mental retardation, nor repeated a grade. Initially, 102 children were invited to participate, but the parents of seven children did not give consent. Due to technical problems during data collection, the final sample comprised 86 typically developing third-graders  between 8 years 3 months and 9 years 2 months  in the numerical stroop and name the ink of a color word  in the non-numerical stroop. Because both the enumeration of stimuli within the subitizing range and the reading of short, well-known words are very automatic processes, the stimuli of both tasks are ideally suitable to measure inhibition by means of a stroop task (which requires an automatic process in the baseline condition for a reliable measure). Each task included four stimuli that were repeated 10 times, namely combinations of the numbers 1, 2, 3, and 4  in the numerical and combinations of the colors blue, yellow, red, and green in the non-numerical stroop task. All stimuli were presented on a paper, with five lines of eight stimuli. Task administration was the same in both stroop tasks. The child had to name all the stimuli, while the experimenter registered accuracy and time to name the entire sheet. For each condition an inverse efficiency score  was calculated by dividing the time needed to name the sheet by the accuracy. An index of inhibition was calculated for each task by subtracting the baseline inverse efficiency score from the interference condition inverse efficiency score.We used two measures of interference control, i.e., Stroop tasks , in whicThe inhibition subscale of the Behavior Rating Inventory of Executive Function or BRIEF  version To investigate the role of inhibition in addition to the well-established influence of numerical magnitude processing, we assessed children’s numerical magnitude processing using a symbolic and a non-symbolic numerical magnitude comparison task consisting of Arabic digits and dot arrays, respectively. The tasks consisted of comparing two simultaneously presented numerical magnitudes arranged on either side of the center of the screen. The children had to select the numerically larger magnitude by pressing the key on the side of the larger numerical magnitude. The stimuli in both tasks comprised all combinations of numerosities 1 to 9, yielding 72 trials for each task. Three practice trials were presented for each task. Per task, the stimuli were randomly divided into two blocks and children were given short breaks between blocks. Each trial started with a 200 ms fixation point in the center of the screen and after 1000 ms the stimulus appeared. In the symbolic task, stimuli remained visible until response. In the non-symbolic task, stimuli disappeared after 840 ms in order to avoid counting of the dots. The position of the largest numerosity was counterbalanced. The non-symbolic stimuli were generated with the MATLAB script provided by n = 28) and each item was once presented with the correct answer and once with an incorrect answer, yielding 56 trials. The multiplication items consisted of all items with a product smaller or equal to 25 (n = 30), because these small problems are more likely to be solved by direct retrieval from long-term memory , the answer of the corresponding addition  or unrelated . Half of the false solutions were numerically larger than the correct answer. Each task was preceded by eight practice trials to familiarize the child with the task requirements. Each trial started with a 250 ms fixation point in the center of the screen and after 750 ms the stimulus appeared. The stimuli remained visible until response. The children had to indicate if the presented solution for the problem was correct  or false . For each task, stimuli were presented randomly divided into two blocks and children were given short breaks between blocks.Arithmetic fact retrieval was assessed by means of two single-digit verification tasks: one addition task and one multiplication task. Stimuli were selected from a standard set of single-digit arithmetic problems , which ery e.g., . Each itM = 100, SD = 15) was calculated.Raven’s Standard Progressive Matrices  was usedA motor reaction time task was included as a control for children’s response speed on the keyboard. Two shapes, one of which was filled, were simultaneously displayed, one on the left and one on the right of the computer screen. The children had to press the key corresponding to the side of the filled figure. All shapes were similar in size. The administered shapes were circle, triangle, square, star, and heart. Each shape occurred four times filled and four times non-filled. This resulted in 20 trials. Three practice trials were included to familiarize the child with the task. The position of the correct answer (filled shape) was counterbalanced. Each trial started with a 200 ms fixation point in the center of the screen and after 1000 ms the stimulus appeared. The stimuli remained visible until response.All children were tested at their own school during regular school hours. They all completed three sessions: an individual session (20 min), a session in small groups of four children (45 min), and a group-administered session (60 min). The individual session and small group session with the computer took place in a quiet room. All children were tested in the middle of the third grade. The individual session consisted of both stroop tasks, the small group session of the computer tasks , and the group session of the measure of intellectual ability. All children completed the tasks in the same order.We explored all data for potential outliers . In total, nine outliers were identified. Importantly, we analyzed all our data both with and without outliers. Including the outliers did not affect the results. Consequently, all subsequent results are based on the full dataset.M = (0.84–0.99)]. We therefore combined for all these tasks the accuracy and response times into an inverse efficiency score, by dividing a child’s response time by its accuracy. These inverse efficiency scores were included in all subsequent analyses.In the stroop and computerized tasks, accuracy was very high  and the non-numerical  stroop task, which indicates that the task manipulation worked. The results of our analyses were very similar if we calculated  correlations with the interference condition controlled for the baseline condition instead of the difference score.There were several careful manipulations built in our tasks. In order to verify if our task format worked, we did several analyses. Firstly, we verified if the task design of the stroop tasks had worked by comparing the baseline and interference condition. As mentioned before we used a difference score as an index of inhibition . This score was significantly different from zero for both the numerical . However, the associations of both types of items with fact retrieval were very similar , and therefore, the effect of this manipulation was not further considered.Secondly, the use of the MATLAB script provided by p < 0.01). In multiplication, accuracy was significantly lower on the table related false solutions than on the uncorrelated false solutions (p < 0.01). We further investigated whether performance on different types of false solutions was differently related to our measures of inhibition. No differential associations of the different types of false solutions with inhibition were found. Consequently, in the following analyses we did not differentiate between the different types of false solutions in the arithmetic verification tasks.Thirdly, on the verification tasks we analyzed whether performance on items with different types of false solutions differed in reaction time and/or accuracy. In the addition task accuracy was significantly lower on the items with the false solutions with a distance of 2 of the correct solution, than on the items with a false solution with a distance of 1 of the correct solution . Correlations with computerized tasks were controlled for motor reaction time by means of partial correlation coefficients. Because of the significant correlation between the arithmetic fact measures , we combined these scores  into one composite score  to improve clarity. This score was included in all subsequent analyses. However, when the pattern of correlations was investigated for each arithmetic fact measure separately, results were very similar.Pearson correlation coefficients were calculated to examine the associations between the different variables under study , which was not significantly correlated with the two cognitive measures of inhibition .t(85) = -4.23, p < 0.001].Both numerical magnitude processing tasks were significantly correlated with arithmetic verification, indicating that children with better numerical magnitude processing skills showed better arithmetic fact retrieval performance. This association was stronger for symbolic than for non-symbolic numerical magnitude processing. The William-Steiger test indicated that the difference between these associations was statistically significant [There was no significant correlation of the behavioural measure of inhibition (BRIEF) with any other measure. We found no significant correlations between the stroop tasks and arithmetic verification. The non-numerical stroop task was significantly correlated with symbolic as well as non-symbolic numerical magnitude processing.Regression analyses were calculated to assess the unique contribution of numerical magnitude processing and inhibition to arithmetic verification. To this end, all these predictors as well as confounding variables, such as intellectual ability and motor reaction time were entered simultaneously into a regression. In addition, in order to quantify evidence in favor of our hypotheses, Bayes factors were calculated for each predictor with the Bayes Factor package of Table  Inhibition skills did not significantly predict arithmetic fact retrieval above numerical magnitude processing (ps > 0.05). Symbolic numerical magnitude processing significantly predicted fact retrieval (p < 0.001), even when controlling for each inhibition task. Non-symbolic numerical magnitude processing did not significantly predict arithmetic fact retrieval (p = 0.407).The results of our regression analysis are presented in p-values only signal the extremeness of the data under the null hypothesis and p-value logic resembles a proof by contradiction – i.e., low p-values indicate extreme data and usually lead researchers to reject the null hypothesis and interpret this as evidence in favor of the alternative hypothesis  than a model that included inhibition.Since pothesis  – we alsFigure 2, when symbolic numerical magnitude processing is omitted from our regression model, the model loses much of its fit to the data. This is the only predictor where this is the case, which indicates that symbolic numerical magnitude processing is by far the most powerful predictor in our model.Turning to the effects of numerical magnitude processing, the Bayes factors only provide anecdotal evidence for the hypothesis that non-symbolic numerical magnitude processing is an important predictor. On the other hand, the Bayes factors provide extreme evidence for the hypothesis that symbolic numerical magnitude processing is an important predictor of the variability in arithmetic fact retrieval. As pictured in Understanding which cognitive processes underlie individual differences in mathematical skills is a necessary prerequisite to design validated diagnostic tests and appropriate interventions. Numerical magnitude processing has been pointed out as an important factor of these individual differences , but theThe present study failed to observe a significant association between inhibition and arithmetic fact retrieval. Moreover, the Bayesian analysis provided evidence for no influence of inhibition. These findings are not in line with the theoretically postulated association between arithmetic fact retrieval and inhibition e.g.,  and contexecutive attention, which they defined as the common aspects of executive function and working memory that are necessary in many complex cognitive tasks, including inhibition of competing responses, goal maintenance, and response selection. These authors used span tasks and a color trail test to measure executive attention. These measures tap into broader executive functions rather than inhibition per se, which might explain the differences between their findings and the current study. This inconsistency might be explained by various factors. Firstly, it could be due to differences in the measurement of inhibition in the present study, compared to existing studies. For example, per se. Therefore, these studies should be replicated in samples of children with specific difficulties in arithmetic fact retrieval.Secondly, the inconsistency might be due to the differences in samples that were used. For example, per se is related to arithmetic fact retrieval (see The present study also investigated the association between numerical magnitude processing and arithmetic fact retrieval. We used both symbolic and non-symbolic tasks to verify whether the access to numerical magnitudes from symbolic digits or numerical magnitude processing eval see , 2015a,bRecently, Firstly, our choice for specific measures of inhibition might have had an important impact on our findings. The present study only included one specific measure of inhibition, i.e., Stroop tasks, yet inhibition is not a unitary construct, but a family of functions . DiffereImportantly, we also included a teacher questionnaire, the BRIEF  to measuSecondly, the participant selection in this study might have had an impact on our results. We only investigated children in third grade, which might explain why we did not find an association between inhibition skills and arithmetic fact retrieval. Although children in third grade have already acquired a considerable number of arithmetic facts, there is still room for improvement in automatizing these facts. Through the course of primary school, problem-answer associations become stronger, and more efficient arithmetic fact retrieval arises . MoreoveThirdly, the association between inhibition and mathematics observed in previous studies might also be explained by other factors that are associated with both individual differences in mathematics and inhibition. Potential examples include working memory , socio-eFuture studies should also examine the association between inhibition and arithmetic fact retrieval at the neural level. Even though this association might not be detectable at the behavior level, it might be that it can be observed at the neural level. Indeed, neuroimaging data might generate findings that cannot be detected by behavioral data alone . Brain aEB and BS developed the study concept. EB developed the test materials, collected the data and performed the data analysis. EB, WF, and BS drafted the manuscript. WF and BS provided critical feedback to different versions of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.