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Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time‐resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000‐fold in vivo . Our modeling approach reveals dose‐dependent feedback control as key property to regulate STAT5‐mediated survival decisions over a broad range of ligand concentrations. Synopsis Cells interpret information encoded by extracellular stimuli through the activation of intracellular signaling networks and translate this information into cellular decisions. A prime example for a system that is exposed to extremely variable ligand concentrations is the erythroid lineage. The key regulator Erythropoietin (Epo) facilitates continuous renewal of erythrocytes at low basal levels but also secures compensation in case of, e.g., blood loss through an up to 1000‐fold increase in hormone concentration. The Epo receptor (EpoR) is expressed on erythroid progenitor cells at the colony forming unit erythroid (CFU‐E) stage. Stimulation of these cells with Epo leads to rapid but transient activation of receptor and JAK2 phosphorylation followed by phosphorylation of the latent transcription factor STAT5. Although STAT5 is known to be an essential regulator of survival and differentiation of erythroid progenitor cells, a quantitative link between the dynamic properties of STAT5 signaling and survival decisions remained unknown. STAT5‐mediated responses in CFU‐E cells are modulated by multiple attenuation mechanisms that operate on different time scales. Fast‐acting mechanisms such as depletion of Epo by rapid receptor turnover and recruitment of the phosphatase SHP‐1 control the initial signal amplitude at the receptor level. Transcriptional feedback regulators such as suppressor of cytokine signaling (SOCS) family members CIS and SOCS3 operate at a slower time scale. Despite the ample knowledge of the individual components involved, only little is known about the specific contributions of these regulators in controlling dynamic properties of STAT5 in response to a broad range of input signals. Therefore, dynamic pathway modeling is required to understand the complex regulatory network of feedback regulators. To address these questions, we established a dual negative feedback model of JAK2/STAT5 signaling in primary erythroid progenitor cells isolated from mouse fetal livers. We provide a large data set of JAK2/STAT5 signaling dynamics employing quantitative immunoblotting, mass spectrometry and quantitative RT–PCR measured under different perturbation conditions to calibrate our model (Figure 3 ). The structure of our model was constructed to comprise the minimal number of parameters necessary to explain the data. Thereby, we aimed at a model with fully identifiable parameters that are essential to obtain high predictive power. Parameter identifiability was analyzed by the profile likelihood approach. Applying this method, we could establish a dual negative feedback model of JAK2‐STAT5 signaling with structurally and in most cases practically identifiable parameters. A major bottle‐neck in combining signal transduction events with cellular phenotypes is the discrepancy in the time scale and stimuli concentrations that are applied in the different experiments. The sensitivity of biochemical assays to determine phosphorylation events within minutes or hours after stimulation is usually lower than the threshold of sensitivity in assays to determine the physiological response after one or more days. Facilitated by the model, we were able to compute the integrated response of JAK2/STAT5 signaling components for experimentally unaddressable Epo concentrations. Our results demonstrate that the integrated response of pSTAT5 in the nucleus accurately correlates with the experimentally determined survival of CFU‐E cells. This provides a quantitative link of the dependency of primary CFU‐E cells on pSTAT5 activation dynamics. By correlation analysis, we could identify the early signaling phase (⩽1 h) of STAT5 to be the most predictive for the fraction of surviving cells, which was determined ∼24 h later. Thus, we hypothesize that as a general principle in apoptotic decisions, ligand concentrations translated into kinetic‐encoded information of early signaling events downstream of receptors can be predictive for survival decisions 24 h later. After the first hour of stimulation, it is important to constrain signaling to a residual steady‐state level. Constitutive phosphorylation of the JAK2/STAT5 pathway has a crucial role in the onset of polycythemia vera (PV), a disease associated with Epo‐independent erythroid differentiation. The two identified transcriptional feedback proteins, CIS and SOCS3, are responsible for adjusting the phosphorylation level of STAT5 after 1 h of stimulation. Since the Epo input signal can vary over a broad range of ligand concentrations, we asked how CIS and SOCS3 can facilitate control of STAT5 long‐term phosphorylation levels over the entire physiological relevant hormone concentrations. By using model simulations, we revealed that the two feedbacks are most effective at different Epo concentration ranges. Predicted by our mathematical model, the major role of CIS in modulating STAT5 phosphorylation levels is at low, basal Epo concentrations, whereas SOCS3 is essential to control the STAT5 phosphorylation levels at high Epo doses (Figure 6 ). As a potential molecular mechanism of this dose‐dependent inhibitory effect, we could identify the quantity of pJAK2 relative to pEpoR that increases with higher Epo concentrations. Since SOCS3 can inhibit JAK2 directly via its KIR domain to attenuate downstream STAT5 activation, SOCS3 becomes more effective with the relative increase of JAK2 activation. Hence, CIS and SOCS3 act in a concerted manner to ensure tight regulation of STAT5 responses over the broad physiological range of Epo concentrations. In summary, our mathematical approach provided new insights into the specific function of feedback regulation in STAT5‐mediated life or death decisions of primary erythroid cells. We dissected the roles of the transcriptionally induced proteins CIS and SOCS3 that operate as dual feedback with divided function thereby facilitating the control of STAT5 activation levels over the entire range of physiological Epo concentrations. The detailed understanding of the molecular processes and control distribution of Epo‐induced JAK/STAT signaling can be further applied to gain insights into alterations promoting malignant hematopoietic diseases. A mathematical dual feedback model of the Epo‐induced JAK2/STAT5 signaling pathway was calibrated with extensive time‐resolved quantitative data sets from immunoblotting, mass spectrometry and qRT–PCR experiments in primary erythroid progenitor cells. We show that the amount of nuclear phosphorylated STAT5 integrated for 60 min post Epo stimulation directly correlates with the fraction of surviving cells 24 h later. CIS and SOCS3 were identified as the most relevant transcriptional feedback regulators of JAK2/STAT5 signaling in primary erythroid progenitor cells. Applying the model, we revealed that CIS‐mediated inhibitory effects are most important at low ligand concentrations, whereas SOCS3 inhibition is more effective at high ligand doses. The distinct modes of inhibition of CIS and SOCS3 at various Epo concentrations provide a strategy for achieving control of JAK2/STAT5 signaling over the entire range of physiological Epo concentrations.

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types.

Maintenance of genomic stability depends on the DNA damage response, an extensive signaling network that is activated by DNA lesions such as double-strand breaks (DSBs). The primary activator of the mammalian DSB response is the nuclear protein kinase ataxia-telangiectasia, mutated (ATM), which phosphorylates key players in various arms of this network. The activation and stabilization of the p53 protein play a major role in the DNA damage response and are mediated by ATM-dependent posttranslational modifications of p53 and Mdm2, a ubiquitin ligase of p53. p53's response to DNA damage also depends on Mdm2-dependent proteolysis of Mdmx, a homologue of Mdm2 that represses p53's transactivation function. Here we show that efficient damage-induced degradation of human Hdmx depends on functional ATM and at least three sites on the Hdmx that are phosphorylated in response to DSBs. One of these sites, S403, is a direct ATM target. Accordingly, each of these sites is important for Hdm2-mediated ubiquitination of Hdmx after DSB induction. These results demonstrate a sophisticated mechanism whereby ATM fine-tunes the optimal activation of p53 by simultaneously modifying each player in the process.

Activation of the NF-κB pathway in T cells is required for induction of an adaptive immune response. Hematopoietic progenitor kinase (HPK1) is an important proximal mediator of T-cell receptor (TCR)-induced NF-κB activation. Knock-down of HPK1 abrogates TCR-induced IKKβ and NF-κB activation, whereas active HPK1 leads to increased IKKβ activity in T cells. Yet, the precise molecular mechanism of this process remains elusive. Here, we show that HPK1-mediated NF-κB activation is dependent on the adaptor protein CARMA1. HPK1 interacts with CARMA1 in a TCR stimulation-dependent manner and phosphorylates the linker region of CARMA1. Interestingly, the putative HPK1 phosphorylation sites in CARMA1 are different from known PKC{theta} consensus sites. Mutations of residues S549, S551, and S552 in CARMA1 abrogated phosphorylation of a CARMA1-linker construct by HPK1 in vitro. In addition, CARMA1 S551A or S5549A/S551A point mutants failed to restore HPK1-mediated and TCR-mediated NF-κB activation and IL-2 expression in CARMA1-deficient T cells. Thus, we identify HPK1 as a kinase specific for CARMA1 and suggest HPK1-mediated phosphorylation of CARMA1 as an additional regulatory mechanism tuning the NF-κB response upon TCR stimulation.

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal‐regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin‐induced pathway activation in primary erythroid progenitor (colony‐forming unit erythroid stage, CFU‐E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU‐E cells. Model analysis showed bow‐tie‐shaped signal processing and inherently transient signalling for cytokine‐induced ERK signalling. Sensitivity analysis predicted that, through a feedback‐mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over‐expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double‐phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions. Synopsis Cell fate decisions such as proliferation, differentiation, and survival are controlled by the activation of signalling networks. A paradigm of a complex signalling pathway is the Raf/MEK/ERK cascade that integrates information from receptors on the cell membrane to elicit cellular responses. Although the components and the wiring of this network are known, there is still a lack of information on its kinetic behaviour and how activation of signalling proteins relates to cellular responses. In particular, two highly related isoforms, ERK1 and ERK2, are expressed in mammalian cells. Although some redundancy seems to exist between these isoforms, ERK1 knockout mice are viable but ERK2 knockout mice are embryonic lethal. Thus, the contribution of these isoforms on signalling and cell fate decision needs to be analysed at a quantitative level. Earlier mathematical models addressing the ERK cascade focused on receptor tyrosine kinases (RTKs)‐induced ERK signalling. RTKs elicit strong and nearly complete phosphorylation of ERK. In contrast, cytokine receptors that have a major role in cell proliferation and the prevention of apoptosis in the haematopoietic system induce only weak phosphorylation of ERK suggesting major difference in systems properties. Activation of ERK involves phosphorylation on two residues, which could be achieved by (i) a processive mechanism, with the upstream kinase binding to ERK and phosphorylating both residues before dissociation or (ii) a distributive mechanism, with the upstream kinase binding to ERK, phosphorylating one residue, dissociating, binding a second time, phosphorylating the second residue and then dissociating. There is in vitro evidence that ERK phosphorylation may be distributive, however, scaffolding proteins might favour a processive mechanism in vivo . Although processive ERK activation would lead to fast signalling because of only one binding step, a distributive ERK activation would lead to signal amplification because of a larger sensitivity of the reaction to the upstream kinase. To address these challenging questions, we established a mathematical model of ERK signalling through a cytokine receptor, the erythropoietin receptor (EpoR). To calibrate our model, we generated time‐resolved data by quantitative immunoblotting in primary erythroid progenitor (colony‐forming unit erythroid stage, CFU‐E) cells after stimulation with erythropoietin (Epo). Distinct from cell lines that are commonly used for biochemical studies, intracellular signalling networks are mostly unperturbed in primary cells. To represent events at the plasma membrane, we tested several receptor models and selected a model that can describe both the temporal and the dose–response kinetics of phosphorylated JAK2. To resolve the underlying mechanism for ERK activation, we established two models: (i) model with a processive activation mechanism and (ii) model with a distributive mechanism. Only the distributive model could explain our time‐resolved data sufficiently (Figure 1 ). Both mechanisms can be distinguished by the extent of mono‐ and double‐phosphorylated ERK being generated. Therefore, we predicted the concentrations of non‐, mono‐, and double‐phosphorylated ERK after Epo stimulation in silico and quantified the respective concentrations in Epo‐stimulated CFU‐E cells by label‐free mass spectrometric analyses. The experimental results were in line with the predictions of our distributive model, thus showing that in vivo ERK phosphorylation occurs by a distributive mechanism. Furthermore, the mathematical model allowed us to analyse information processing through this signalling network by calculating the concentration of activated molecules at each step of the pathway. The results indicate a substantial signal attenuation between the receptor and the membrane‐associated factors SOS, Ras, and Raf, as expected for cytokine receptors. However, strong signalling amplification is observed between Raf, MEK, and ERK. Thus, signal processing of the EpoR‐induced ERK cascade represents a phosphorylation bow tie, resembling regulatory bow‐tie structures that are common motifs in biology. The main source of fragility is the knot in the bow tie, in this case SOS, Ras, and Raf. These results were confirmed by sensitivity analyses of the mathematical model: changes in the initial protein concentrations of these proteins have the largest impact on ERK phosphorylation. The sensitivity analysis provided another unexpected result regarding the interlinked wiring of the signalling network: increasing the initial concentration of ERK1 elevates the amount of phosphorylated ERK1, but at the same time reduces the amount of phosphorylated ERK2. We show by model simulations that the two isoforms cross‐regulate each other not by competition for the upstream kinase, but rather through negative feedback regulation. In line with the theoretical considerations, over‐expression of ERK1 and ERK2 in CFU‐E cells experimentally confirmed that elevating one isoform reduces phosphorylation of the other isoform. To link the signal activation with cell fate decisions, we calculated the extent of ERK1 and ERK2 activation for different Epo concentrations. We focused on the integrated response of double‐phosphorylated ERK1 and ERK2, that is the area under the activation curve. Proliferation of CFU‐E cells was determined after stimulation with different Epo concentrations. To address the contributions of the two ERK isoforms, we additionally over‐expressed ERK1 or ERK2. We could show that though proliferation depends on Epo in a sigmoidal manner, over‐expression of ERK isoforms reduces proliferation, with ERK1 having the stronger effect. This provided us with a large data set of signalling responses (integrated response of ERK1 and ERK2) and corresponding cell fate decisions (proliferation) allowing the determination of a function linking signal activation to proliferation. Analysing the individual contribution of activated ERK1 and ERK2 to signalling showed that though ERK2 enhances proliferation with saturation kinetics, strong ERK1 activation reduces proliferation (Figure 6 ). Thus, increasing ERK2 phosphorylation drives proliferation, whereas high levels of ERK1 phosphorylation prevent proliferation in case of hyperactivation. In conclusion, by combing experimental analysis and mathematical modelling, we show that ERK signalling contributes to cell fate decisions in a dose‐dependent and isoform‐specific manner. We clarified signalling mechanisms using primary cells and uncovered several motifs that are of broader implications for signalling networks in general. In particular, we show a distributive mechanism of ERK activation in vivo leading to strong signal amplification and a bow‐tie structure of signalling networks. Furthermore, we present a generic approach to analyse signalling networks by the combination of modelling and experimentation. The specific role of the isoforms ERK1 and ERK2 provides an important basis for systems‐oriented drug design, opening new possibilities for interventions against cancer. A data‐based mathematical model of the Epo‐induced ERK signalling pathway was calibrated with quantitative immunoblotting data from primary mouse cells, showing that in mammalian cells ERK activation by dual phosphorylation occurs by a distributive process, which was validated by quantitative mass spectrometry. Information processing through the EpoR signalling cascade follows a bow tie‐like architecture, where signal flux is reduced downstream of the receptor and re‐amplified in the Raf‐MEK‐ERK kinase module. Sensitivity analysis and overexpression experiments reveal that the two ERK isoforms cross‐regulate each others activation via a feedback mechanism. The extent of signal activation is linked with cell fate decisions, showing that ERK1 and ERK2 isoforms contribute differentially, with ERK1 attenuating proliferation beyond a certain activation level and ERK2 enhancing proliferation with saturation kinetics.

Diacyl glycerophospholipids (GPs) belong to the most abundant lipid species in living organisms and consist of a glycerol backbone with fatty acyl groups in sn-1 and sn-2 and a polar head group in the sn-3 position. Regioisomeric mixed diacyl GPs have the same fatty acyl composition but differ in their allocation to sn-1 or sn-2 of the glycerol unit. In-depth analysis of regioisomeric mixed diacyl GP species composed of fatty acyl moieties that are similar in length and degree of saturation typically requires either chemical derivatization or sophisticated analytical instrumentation, since these types of regioisomers are not well resolved under standard ultra-performance liquid chromatography (UPLC) conditions. Here, we introduce a simple and fast method for diacyl GP regioisomer analysis employing UPLC tandem mass spectrometry (MS/MS). This GP regioisomer analysis is based both on minor chromatographic retention time shifts and on major differences in relative abundances of the two fatty acyl anion fragments observed in MS/MS. To monitor these differences with optimal precision, MS/MS spectra are recorded continuously over the UPLC elution profile of the lipid species of interest. Quantification of relative abundances of the regioisomers was performed by algorithms that we have developed for this purpose. The method was applied to commercially available mixed diacyl GP standards and to total lipid extracts of Escherichia coli (E. coli) and bovine liver. To validate our results, we determined regioisomeric ratios of phosphatidylcholine (PC) standards using phospholipase A2-specific release of fatty acids from the sn-2 position of the glycerol backbone. Our results show that most analyzed mixed diacyl GPs of biological origin exhibit significantly higher regioisomeric purity than synthetic lipid standards. In summary, this method can be implemented in routine LC-MS/MS-based lipidomics workflows without the necessity for additional chemical additives, derivatizations, or instrumentation.

Issue Title: Comments on mass spectrometry based proteomics/Field-flow fractionation coupled to ICP-MS/Nanometer-sized materials for solid-phase extraction/Biosensing techniques for trace carcinogen detection

Written by an experienced and well-published individual, this unique reference source takes a forward-looking approach. It describes the concepts and practice of protein phosphorylation analysis by tandem mass spectrometry and related techniques. These include purification, enrichment, database searching, other software tools, synthesis, phosphatase treatment, phospho-specific staining methods, isoelectric focusing and element mass spectrometry. The book then goes on to cover the fragmentation behaviour of phosphopeptides in tandem MS (pos+neg ions) and the implementation of the particular features into an analytical strategy. The book ends with a summary and discussion of useful internet and software tools currently available.

A new method for microquantification of phospholipid classes by nanoelectrospray mass spectrometry and stable isotope dilution is presented. The method covers the sum of phosphatidylcholine and sphingomyelin and in addition selectively quantifies phosphatidylethanolamine, phosphatidylserine, and phosphatidylinositol. A phospholipid class is quantified together with its corresponding lyso-species due to the presence of a common head group. Phospholipids are extracted from tissue lysates, hydrolysed by hydrofluoric acid, and the liberated polar head groups choline, ethanolamine, serine, and inositol are quantified by nanoelectrospray mass spectrometry using deuterium-labeled analogs of the head groups as internal standards. The method is applied to tissue samples of a gastrointestinal tumor and of corresponding non-affected control tissue. In the tumor sample, the abovementioned phospholipids were found at roughly threefold elevated concentrations with a virtually unaltered relative abundance profile.

A new method for inorganic phosphate microquantification is introduced based on negative ion electrospray tandem mass spectrometry and stable isotope dilution by 18 O 4 -labeled phosphate. Quantification is performed using the non-labeled and 18 O 3 -labeled [P 18 O 3 ] − fragment ions at m / z 79 and 85, respectively, formed by dissociation of the [H 2 PO 4 ] − ion at m / z 97 and 105, respectively, visible in negative ion electrospray ionization mass spectrometry (ESI-MS) spectra. Tandem mass spectrometry was selected to remove an overlap with the isobaric [HSO 4 ] − ion at m / z 97 of sulfate and to establish an optimal sensitivity of the quantification assay. It is demonstrated that the assay can also measure the sum of inorganic and phosphoryl phosphate by prior enzymatic hydrolysis of phosphoryl phosphate. The assay works with phosphate concentrations in the micromolar range and, in combination with nano-ESI, is capable to quantitate absolute amounts of phosphate in the low nanogram range from complex samples.