Cell type-specific importance of ras-c-raf complex association rate constants for MAPK signaling

Structure-based prediction of Ras-effector binding affinities and design of "branchegetic" interface mutations

Ras is a central cellular hub protein controlling multiple cell fates. How Ras interacts with a variety of potential effector proteins is relatively unexplored, with only some key effectors characterized in great detail. Here, we have used homology modeling based on X-ray and AlphaFold2 templates to build structural models for 54 Ras-effector complexes. These models were used to estimate binding affinities using a supervised learning regressor. Furthermore, we systematically introduced Ras "branch-pruning" (or branchegetic) mutations to identify 200 interface mutations that affect the binding energy with at least one of the model structures. The impacts of these branchegetic mutants were integrated into a mathematical model to assess the potential for rewiring interactions at the Ras hub on a systems level. These findings have provided a quantitative understanding of Ras-effector interfaces and their impact on systems properties of a key cellular hub.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis

Simple Summary

Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors.

Abstract

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L₁ regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

Oncogenic KRAS G12D mutation promotes dimerization through a second, phosphatidylserine-dependent interface: a model for KRAS oligomerization

KRAS forms transient dimers and higher-order multimers (nanoclusters) on the plasma membrane, which drive MAPK signaling and cell proliferation. KRAS is a frequently mutated oncogene, and while it is well known that the most prevalent mutation, G12D, impairs GTP hydrolysis, thereby increasing KRAS activation, G12D has also been shown to enhance nanoclustering. Elucidating structures of dynamic KRAS assemblies on a membrane has been challenging, thus we have refined our NMR approach that uses nanodiscs to study KRAS associated with membranes. We incorporated paramagnetic relaxation enhancement (PRE) titrations and interface mutagenesis, which revealed that, in addition to the symmetric ‘α–α’ dimerization interface shared with wild-type KRAS, the G12D mutant also self-associates through an asymmetric ‘α–β’ interface. The ‘α–β’ association is dependent on the presence of phosphatidylserine lipids, consistent with previous reports that this lipid promotes KRAS self-assembly on the plasma membrane in cells. Experiments using engineered mutants to spoil each interface, together with PRE probes attached to the membrane or free in solvent, suggest that dimerization through the primary ‘α–α’ interface releases β interfaces from the membrane promoting formation of the secondary ‘α–β’ interaction, potentially initiating nanoclustering. In addition, the small molecule BI-2852 binds at a β–β interface, stabilizing a new dimer configuration that outcompetes native dimerization and blocks the effector-binding site. Our data indicate that KRAS self-association involves a delicately balanced conformational equilibrium between transient states, which is sensitive to disease-associated mutation and small molecule inhibitors. The methods developed here are applicable to biologically important transient interactions involving other membrane-associated proteins.

Studies of membrane-dependent dimerization of KRAS on nanodiscs using paramagnetic NMR titrations and mutagenesis revealed a novel asymmetric ‘α–β’ interface that provides a potential mechanism for the enhanced assembly of KRAS–G12D nanoclusters.

Predicted 'wiring landscape' of Ras-effector interactions in 29 human tissues

Ras is a plasma membrane (PM)-associated signaling hub protein that interacts with its partners (effectors) in a mutually exclusive fashion. We have shown earlier that competition for binding and hence the occurrence of specific binding events at a hub protein can modulate the activation of downstream pathways. Here, using a mechanistic modeling approach that incorporates high-quality proteomic data of Ras and 56 effectors in 29 (healthy) human tissues, we quantified the amount of individual Ras-effector complexes, and characterized the (stationary) Ras "wiring landscape" specific to each tissue. We identified nine effectors that are in significant amount in complex with Ras in at least one of the 29 tissues. We simulated both mutant- and stimulus-induced network re-configurations, and assessed their divergence from the reference scenario, specifically discussing a case study for two stimuli in three epithelial tissues. These analyses pointed to 32 effectors that are in significant amount in complex with Ras only if they are additionally recruited to the PM, e.g. via membrane-binding domains or domains binding to activated receptors at the PM. Altogether, our data emphasize the importance of tissue context for binding events at the Ras signaling hub.

Engineering of Biological Pathways: Complex Formation and Signal Transduction

The rational in silico design of interface mutations within protein complexes is a synthetic biology tool that enables-when introduced into biological systems-the artificial rewiring of biological pathways. Here we describe the three-dimensional structure-based design of "rewiring" mutations using the FoldX force field. Specifically, we provide the protocol for the design and selection of interface mutations in three Ras-effector complex structures (PDB entries 3KUD, 4K81, and 6AMB). Ras mutations that impair binding to some but not all interacting partners are selected.

Analysis of Ras-effector interaction competition in large intestine and colorectal cancer context

Cancer is the second leading cause of death globally, and colorectal cancer (CRC) is among the five most common cancers. The small GTPase KRAS is an oncogene that is mutated in ~30% of all CRCs. Pharmacological treatments of CRC are currently unsatisfactory, but much hope rests on network-centric approaches to drug development and cancer treatment. These approaches, however, require a better understanding of how networks downstream of Ras oncoproteins are connected in a particular tissue context – here colon and CRC. Previously we have shown that competition for binding to a ‘hub’ protein, such as Ras, can induce a rewiring of signal transduction networks. In this study, we analysed 56 established and predicted effectors that contain a structural domain with the potential ability to bind to Ras oncoproteins and their link to pathways coordinating intestinal homoeostasis and barrier function. Using protein concentrations in colon tissue and Ras-effector binding affinities, a computational network model was generated that predicted how effectors differentially and competitively bind to Ras in colon context. The model also predicted both qualitative and quantitative changes in Ras-effector complex formations with increased levels of active Ras – to simulate its upregulation in cancer – simply as an emergent property of competition for the same binding interface on the surface of Ras. We also considered how the number of Ras-effector complexes at the membrane can be increased by additional domains present in some effectors that are recruited to the membrane in response to specific conditions (inputs/stimuli/growth factors) in colon context and CRC.

Mathematical model identifies effective P53 accumulation with target gene binding affinity in DNA damage response for cell fate decision

Functional p53 signaling is essential for appropriate responses to diverse stimuli. P53 dynamics employs the information from the stimulus leading to selective gene expression and cell fate decision. However, the decoding mechanism of p53 dynamics under DNA damage challenge remains poorly understood. Here we mathematically modeled the recently dual-phase p53 dynamics under doxorubicin treatment. We found that p53 could perform sequential pulses followed by a high-amplitude terminal pulse at relatively low doxorubicin treatment, whereas p53 became steadily accumulated when damage level was high. The effective p53 integral above a threshold but not the absolute accumulation of p53 precisely discriminated survival and death. Silencing negative regulators in p53 network might promote the occurrence of terminal pulse. Furthermore, lower binding affinity and degradation rate of p53 target genes could favorably discriminate high and low dose doxorubicin treatment. Grouping by temporal profiles suggested that the p53 dynamics rather than the doxorubicin doses could better discriminate cellular outcomes and confer less variation for effective p53 integral reemphasizing the importance of p53 level regulation. Our model has established a theoretical framework that p53 dynamics can work cooperatively with its binding affinity to target genes leading to cell fate choice, providing new clues of optimized clinical design by manipulating p53 dynamics.

Intrinsic limits of information transmission in biochemical signalling motifs

All living things have evolved to sense changes in their environment in order to respond in adaptive ways. At the cellular level, these sensing systems generally involve receptor molecules at the cell surface, which detect changes outside the cell and relay those changes to the appropriate response elements downstream. With the advent of experimental technologies that can track signalling at the single-cell level, it has become clear that many signalling systems exhibit significant levels of 'noise,' manifesting as differential responses of otherwise identical cells to the same environment. This noise has a large impact on the capacity of cell signalling networks to transmit information from the environment. Application of information theory to experimental data has found that all systems studied to date encode less than 2.5 bits of information, with the majority transmitting significantly less than 1 bit. Given the growing interest in applying information theory to biological data, it is crucial to understand whether the low values observed to date represent some sort of intrinsic limit on information flow given the inherently stochastic nature of biochemical signalling events. In this work, we used a series of computational models to explore how much information a variety of common 'signalling motifs' can encode. We found that the majority of these motifs, which serve as the basic building blocks of cell signalling networks, can encode far more information (4-6 bits) than has ever been observed experimentally. In addition to providing a consistent framework for estimating information-theoretic quantities from experimental data, our findings suggest that the low levels of information flow observed so far in living system are not necessarily due to intrinsic limitations. Further experimental work will be needed to understand whether certain cell signalling systems actually can approach the intrinsic limits described here, and to understand the sources and purpose of the variation that reduces information flow in living cells.

Optogenetically controlled protein kinases for regulation of cellular signaling

Protein kinases are involved in the regulation of many cellular processes including cell differentiation, survival, migration, axon guidance and neuronal plasticity. A growing set of optogenetic tools, termed opto-kinases, allows activation and inhibition of different protein kinases with light. The optogenetic regulation enables fast, reversible and non-invasive manipulation of protein kinase activities, complementing traditional methods, such as treatment with growth factors, protein kinase inhibitors or chemical dimerizers. In this review, we summarize the properties of the existing optogenetic tools for controlling tyrosine kinases and serine-threonine kinases. We discuss how the opto-kinases can be applied for studies of spatial and temporal aspects of protein kinase signaling in cells and organisms. We compare approaches for chemical and optogenetic regulation of protein kinase activity and present guidelines for selection of opto-kinases and equipment to control them with light. We also describe strategies to engineer novel opto-kinases on the basis of various photoreceptors.

Molecular mechanisms of detection and discrimination of dynamic signals

Many molecules decode not only the concentration of cellular signals, but also their temporal dynamics. However, little is known about the mechanisms that underlie the detection and discrimination of dynamic signals. We used computational modelling of the interaction of a ligand with multiple targets to investigate how kinetic and thermodynamic parameters regulate their capabilities to respond to dynamic signals. Our results demonstrated that the detection and discrimination of temporal features of signal inputs occur for reactions proceeding outside mass-action equilibrium. For these reactions, thermodynamic parameters such as affinity do not predict their outcomes. Additionally, we showed that, at non-equilibrium, the association rate constants determine the amount of product formed in reversible reactions. In contrast, the dissociation rate constants regulate the time interval required for reversible reactions to achieve equilibrium and, consequently, control their ability to detect and discriminate dynamic features of cellular signals.

Interaction Dynamics Determine Signaling and Output Pathway Responses

The understanding of interaction dynamics in signaling pathways can shed light on pathway architecture and provide insights into targets for intervention. Here, we explored the relevance of kinetic rate constants of a key upstream osmosensor in the yeast high-osmolarity glycerol-mitogen-activated protein kinase (HOG-MAPK) pathway to signaling output responses. We created mutant pairs of the Sln1-Ypd1 complex interface that caused major compensating changes in the association (k_on) and dissociation (k_off) rate constants (kinetic perturbations) but only moderate changes in the overall complex affinity (K_d). Yeast cells carrying a Sln1-Ypd1 mutant pair with moderate increases in k_on and k_off displayed a lower threshold of HOG pathway activation than wild-type cells. Mutants with higher k_on and k_off rates gave rise to higher basal signaling and gene expression but impaired osmoadaptation. Thus, the k_on and k_off rates of the components in the Sln1 osmosensor determine proper signaling dynamics and osmoadaptation.

Piconewton-Scale Analysis of Ras-BRaf Signal Transduction with Single-Molecule Force Spectroscopy

Intermolecular interactions dominate the behavior of signal transduction in various physiological and pathological cell processes, yet assessing these interactions remains a challenging task. Here, this study reports a single-molecule force spectroscopic method that enables functional delineation of two interaction sites (≈35 pN and ≈90 pN) between signaling effectors Ras and BRaf in the canonical mitogen-activated protein kinase (MAPK) pathway. This analysis reveals mutations on BRaf at Q257 and A246, two sites frequently linked to cardio-faciocutaneous syndrome, result in ≈10-30 pN alterations in RasBRaf intermolecular binding force. The magnitude of changes in RasBRaf binding force correlates with the size of alterations in protein affinity and in α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-sensitive glutamate receptor (-R)-mediated synaptic transmission in neurons expressing replacement BRaf mutants, and predicts the extent of learning impairments in animals expressing replacement BRaf mutants. These results establish single-molecule force spectroscopy as an effective platform for evaluating the piconewton-level interaction of signaling molecules and predicting the behavior outcome of signal transduction.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.

The Caveolin-3 G56S sequence variant of unknown significance: Muscle biopsy findings and functional cell biological analysis

Purpose

In the era of next‐generation sequencing, we are increasingly confronted with sequence variants of unknown significance. This phenomenon is also known for variations in Caveolin‐3 and can complicate the molecular diagnosis of the disease. Here, we aimed to study the ambiguous character of the G56S Caveolin‐3 variant.

Experimental design

A comprehensive approach combining genetic and morphological studies of muscle derived from carriers of the G56S Caveolin‐3 variant were carried out and linked to biochemical assays (including phosphoblot studies and proteome profiling) and morphological investigations of cultured myoblasts.

Results

Muscles showed moderate chronic myopathic changes in all carriers of the variant. Myogenic RCMH cells expressing the G56S Caveolin‐3 protein presented irregular Caveolin‐3 deposits within the Golgi in addition to a regular localization of the protein to the plasma membrane. This result was associated with abnormal findings on the ultra‐structural level. Phosphoblot studies revealed that G56S affects EGFR‐signaling. Proteomic profiling demonstrated alterations in levels of physiologically relevant proteins which are indicative for antagonization of G56S Caveolin‐3 expression. Remarkably, some proteomic alterations were enhanced by osmotic/mechanical stress.

Conclusions and clinical relevance

Our studies suggest that G56S might influence the manifestation of myopathic changes upon the presence of additional cellular stress burden. Results of our studies moreover improve the current understanding of (genetic) causes of myopathic disorders classified as caveolinopathies.

SM22α inhibits lamellipodium formation and migration via Ras-Arp2/3 signaling in synthetic VSMCs

We previously demonstrated that smooth muscle (SM) 22α promotes the migration activity in contractile vascular smooth muscle cells (VSMCs). Based on the varied functions exhibited by SM22α in different VSMC phenotypes, we investigated the effect of SM22α on VSMC migration under pathological conditions. The results demonstrated that SM22α overexpression in synthetic VSMCs inhibited platelet-derived growth factor (PDGF)-BB-induced cell lamellipodium formation and migration, which was different from its action in contractile cells. The results indicated two distinct mechanisms underlying inhibition of lamellipodium formation by SM22α, increased actin dynamic stability and decreased Ras activity via interference with interactions between Ras and guanine nucleotide exchange factor. The former inhibited actin cytoskeleton rearrangement in the cell cortex, while the latter significantly disrupted actin nucleation activation of the Arp2/3 complex. Baicalin, a herb-derived flavonoid compound, inhibited VSMC migration via upregulation of SM22α expression in vitro and in vivo. These data suggest that SM22α regulates lamellipodium formation and cell migration in a phenotype-dependent manner in VSMCs, which may be a new therapeutic target for vascular lesion formation.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

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Active MEK and ERK levels differ profoundly among genetically identical cells

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A statistical framework is developed to identify the causes of this variability

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Analysis shows that extrinsic noise upstream MEK-ERK module causes cell variability

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Within-module extrinsic variability distorts signals

The yin-yang of kinase activation and unfolding explains the peculiarity of Val600 in the activation segment of BRAF

Many driver mutations in cancer are specific in that they occur at significantly higher rates than – presumably – functionally alternative mutations. For example, V600E in the BRAF hydrophobic activation segment (AS) pocket accounts for >95% of all kinase mutations. While many hypotheses tried to explain such significant mutation patterns, conclusive explanations are lacking. Here, we use experimental and in silico structure-energy statistical analyses, to elucidate why the V600E mutation, but no other mutation at this, or any other positions in BRAF’s hydrophobic pocket, is predominant. We find that BRAF mutation frequencies depend on the equilibrium between the destabilization of the hydrophobic pocket, the overall folding energy, the activation of the kinase and the number of bases required to change the corresponding amino acid. Using a random forest classifier, we quantitatively dissected the parameters contributing to BRAF AS cancer frequencies. These findings can be applied to genome-wide association studies and prediction models.

DOI:http://dx.doi.org/10.7554/eLife.12814.001

Mutations in the gene that encodes a protein called BRAF are commonly found in certain cancers, such as melanomas. The same BRAF mutation is found in nearly all of these cancers. This mutation causes the 600th amino acid in the BRAF protein – an amino acid called a valine – to be replaced with another amino acid, a glutamate.

BRAF is a type of enzyme called a kinase, and it transmits signals inside cells to promote cell growth. Kinases work by adding a phosphate group to other proteins to alter their activity. The structure of the BRAF kinase contains a pocket-like shape, and the valine at position 600 sits buried inside this pocket when the enzyme is inactive. The “valine-to-glutamate” mutation (often called V600E for short) disrupts the interactions that create this pocket. This in turn results in a permanently active form of BRAF and uncontrolled cell growth. However, it remains unclear why the valine-to-glutamate mutation is so much more common in cancer cells than any other mutation that could affect the pocket in BRAF.

To address this question, Kiel et al. used a computational tool to generate three-dimensional models for all the different amino acid substitutions that could occur in BRAF’s pocket. Each mutation was then assessed to see how it might destabilize the structure of BRAF. Only the mutations that affected the 600th amino acid were predicted to be able to open the pocket without destabilizing the part of the enzyme that adds phosphate groups to other proteins.

Kiel et al. validated their computational predictions by introducing normal or mutant versions of the BRAF-encoding gene into human cells grown in the laboratory. These experiments showed that a mutation that introduced an amino acid called histidine into position 600 could activate BRAF as much the valine-to-glutamate mutation. Kiel et al. suggest that this “valine-to-histidine” substitution is not found in cancers because it requires three changes to the DNA sequence of the BRAF gene, whereas the valine-to-glutamate substitution only requires one.

The results underscore the importance of considering changes at both the DNA and protein level when attempting to understand why certain cancer-causing mutations are more common than others.

DOI:http://dx.doi.org/10.7554/eLife.12814.002

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine

Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.

Dynamics of Inductive ERK Signaling in the Drosophila Embryo

Transient activation of the highly conserved extracellular-signal-regulated kinase (ERK) establishes precise patterns of cell fates in developing tissues. Quantitative parameters of these transients are essentially unknown, but a growing number of studies suggest that changes in these parameters can lead to a broad spectrum of developmental abnormalities. We provide a detailed quantitative picture of an ERK-dependent inductive signaling event in the early Drosophila embryo, an experimental system that offers unique opportunities for high-throughput studies of developmental signaling. Our analysis reveals a spatiotemporal pulse of ERK activation that is consistent with a model in which transient production of a short-ranged ligand feeds into a simple signal interpretation system. The pulse of ERK signaling acts as a switch in controlling the expression of the ERK target gene. The quantitative approach that led to this model, based on the integration of data from fixed embryos and live imaging, can be extended to other developmental systems patterned by transient inductive signals.

Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014

This Pulmonary Perspective summarizes the content and main conclusions of an international workshop on personalized respiratory medicine coorganized by the Barcelona Respiratory Network ( www.brn.cat ) and the AJRCCM in June 2014. It discusses (1) its definition and historical, social, legal, and ethical aspects; (2) the view from different disciplines, including basic science, epidemiology, bioinformatics, and network/systems medicine; (3) the bottlenecks and opportunities identified by some currently ongoing projects; and (4) the implications for the individual, the healthcare system and the pharmaceutical industry. The authors hope that, although it is not a systematic review on the subject, this document can be a useful reference for researchers, clinicians, healthcare managers, policy-makers, and industry parties interested in personalized respiratory medicine.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

ERK as a model for systems biology of enzyme kinetics in cells

A key step towards a chemical picture of enzyme catalysis was taken in 1913, when Leonor Michaelis and Maud Menten published their studies of sucrose hydrolysis by invertase. Based on a novel experimental design and a mathematical model, their work offered a quantitative view of biochemical kinetics well before the protein nature of enzymes was established and complexes with substrates could be detected. Michaelis-Menten kinetics provides a solid framework for enzyme kinetics in vitro, but what about kinetics in cells, where enzymes can be highly regulated and participate in a multitude of interactions? We discuss this question using the Extracellular Signal Regulated Kinase (ERK), which controls a myriad functions in cells, as a model of an important enzyme for which we have crystal structures, quantitative in vitro assays, and a vast list of binding partners. Despite great progress, we still cannot quantitatively predict how the rates of ERK-dependent reactions respond to genetic and pharmacological perturbations. Achieving this goal, which is important from both fundamental and practical standpoints, requires measuring the rates of enzyme reactions in their native environment and interpreting these measurements using simple but realistic mathematical models--the two elements which served as the cornerstones for Michaelis' and Menten's seminal 1913 paper.

Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways

Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors) and the output (transcription factors) layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

Cell function is determined by highly organized networks of biological molecules. An important class of protein pathways maintains the transmission of signals from the cell membrane to the nucleus. These signaling pathways are reused for different purposes at an evolutionary scale and in different cell types of the same organism. However, it is largely unknown how this flexibility is achieved and how this flexibility is balanced with the high degree of evolutionary conservation of some signaling proteins and the need for robustness against intra- and extra-cellular perturbations.We show how functional roles of signaling proteins determine patterns of evolutionary conservation, protein abundance (the average over different human cell lines and its variability) and disease mutations. Projecting pathway annotations on protein-protein interaction (PPI) networks, a picture emerges in which PPIs between variable and less conserved receptors and stable and conserved proteins of the core signal transmission machinery largely modulate signaling activity in a tissue-specific manner. This has important implications for the distribution of disease mutations in signaling pathways, which need to be considered for the understanding of their effect.

Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations

The Ras/MAPK syndromes ('RASopathies') are a class of developmental disorders caused by germline mutations in 15 genes encoding proteins of the Ras/mitogen-activated protein kinase (MAPK) pathway frequently involved in cancer. Little is known about the molecular mechanisms underlying the differences in mutations of the same protein causing either cancer or RASopathies. Here, we shed light on 956 RASopathy and cancer missense mutations by combining protein network data with mutational analyses based on 3D structures. Using the protein design algorithm FoldX, we predict that most of the missense mutations with destabilising energies are in structural regions that control the activation of proteins, and only a few are predicted to compromise protein folding. We find a trend that energy changes are higher for cancer compared to RASopathy mutations. Through network modelling, we show that partly compensatory mutations in RASopathies result in only minor downstream pathway deregulation. In summary, we suggest that quantitative rather than qualitative network differences determine the phenotypic outcome of RASopathy compared to cancer mutations.

Genotype to phenotype via network analysis

A prime objective of genomic medicine is the identification of disease-causing mutations and the mechanisms by which such events result in disease. As most disease phenotypes arise not from single genes and proteins but from a complex network of molecular interactions, a priori knowledge about the molecular network serves as a framework for biological inference and data mining. Here we review recent developments at the interface of biological networks and mutation analysis. We examine how mutations may be treated as a perturbation of the molecular interaction network and what insights may be gained from taking this perspective. We review work that aims to transform static networks into rich context-dependent networks and recent attempts to integrate non-coding RNAs into such analysis. Finally, we conclude with an overview of the many challenges and opportunities that lie ahead.

Modeling protein association mechanisms and kinetics

Substantial advances have been made in modeling protein association mechanisms and in calculating association rate constants (ka). We now have a clear understanding of the physical factors underlying the wide range of experimental ka values. Half of the association problem, where ka is limited by diffusion, is perhaps solved, and for the other half, where conformational changes become rate-limiting, a number of promising methods are being developed for ka calculations. Notably, the binding kinetics of disordered proteins are receiving growing attention, with 'dock-and-coalesce' emerging as a general mechanism. Progress too has been made in the modeling of protein association kinetics under conditions mimicking the heterogeneous, crowded environments of cells, an endeavor that should ultimately lead to a better understanding of cellular functions.

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

Understanding cancer mechanisms through network dynamics

Cancer is a complex, multifaceted disease. Cellular systems are perturbed both during the onset and development of cancer, and the behavioural change of tumour cells usually involves a broad range of dynamic variations. To an extent, the difficulty of monitoring the systemic change has been alleviated by recent developments in the high-throughput technologies. At both the genomic as well as proteomic levels, the technological advances in microarray and mass spectrometry, in conjunction with computational simulations and the construction of human interactome maps have facilitated the progress of identifying disease-associated genes. On a systems level, computational approaches developed for network analysis are becoming especially useful for providing insights into the mechanism behind tumour development and metastasis. This review emphasizes network approaches that have been developed to study cancer and provides an overview of our current knowledge of protein-protein interaction networks, and how their systemic perturbation can be analysed by two popular network simulation methods: Boolean network and ordinary differential equations.

A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation.

Cell type specificity of signaling: view from membrane receptors distribution and their downstream transduction networks

Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities. To overview the specificity of cell signaling, we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types. Six percent receptors showed cell-type-specific expression, and 4% signaling networks presented enriched cell-specific proteins induced by the receptors. We introduced a concept of "response context" to annotate the cell-type dependent signaling networks. We found that most cells respond similarly to the same stimulus, as the "response contexts" presented high functional similarity. Despite this, the subtle spatial diversity can be observed from the difference in network architectures. The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells, which indicated cellular-context dependent signaling patterns are elaborately spatially organized. Likewise, in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells. However, glioma emerged hyper-activated transduction mechanism in malignant state. Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism. This work represents an effort to decipher context-specific signaling network from spatial dimension. Our results indicated that although a majority of cells engage general signaling response with subtle differences, the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms, but also help understand cell signaling in disease.

Rate constants and mechanisms of intrinsically disordered proteins binding to structured targets

The binding of intrinsically disordered proteins (IDPs) to structured targets is gaining increasing attention. Here we review experimental and computational studies on the binding kinetics of IDPs. Experiments have yielded both the binding rate constants and the binding mechanisms, the latter via mutation and deletion studies and NMR techniques. Most computational studies have aimed at qualitative understanding of the binding rate constants or at mapping the free energy surfaces after the IDPs are engaged with their targets. The experiments and computation show that IDPs generally gain structures after they are engaged with their targets; that is, interactions with the targets facilitate the IDPs' folding. It also seems clear that the initial contact of an IDP with the target is formed by just a segment, not the entire IDP. The docking of one segment to its sub-site followed by coalescing of other segments around the corresponding sub-sites emerges as a recurring feature in the binding of IDPs. Such a dock-and-coalesce model forms the basis for quantitative calculation of binding rate constants. For both disordered and ordered proteins, strong electrostatic attraction with their targets can enhance the binding rate constants by several orders of magnitude. There are now tremendous opportunities in narrowing the gap in our understanding of IDPs relative to ordered proteins with regard to binding kinetics.

Toxicogenomics: the challenges and opportunities to identify biomarkers, signatures and thresholds to support mode-of-action

Toxicogenomics (TGx) can be defined as the application of "omics" techniques to toxicology and risk assessment. By identifying molecular changes associated with toxicity, TGx data might assist hazard identification and investigate causes. Early technical challenges were evaluated and addressed by consortia (e.g. ISLI/HESI and the Microarray Quality Control consortium), which demonstrated that TGx gave reliable and reproducible information. The MAQC also produced "best practice on signature generation" after conducting an extensive evaluation of different methods on common datasets. Two findings of note were the need for methods that control batch variability, and that the predictive ability of a signature changes in concert with the variability of the endpoint. The key challenge remaining is data interpretation, because TGx can identify molecular changes that are causal, associated with or incidental to toxicity. Application of Bradford Hill's tests for causation, which are used to build mode of action (MOA) arguments, can produce reasonable hypotheses linking altered pathways to phenotypic changes. However, challenges in interpretation still remain: are all pathway changes equal, which are most important and plausibly linked to toxicity? Therefore the expert judgement of the toxicologist is still needed. There are theoretical reasons why consistent alterations across a metabolic pathway are important, but similar changes in signalling pathways may not alter information flow. At the molecular level thresholds may be due to the inherent properties of the regulatory network, for example switch-like behaviours from some network motifs (e.g. positive feedback) in the perturbed pathway leading to the toxicity. The application of systems biology methods to TGx data can generate hypotheses that explain why a threshold response exists. However, are we adequately trained to make these judgments? There is a need for collaborative efforts between regulators, industry and academia to properly define how these technologies can be applied using appropriate case-studies.

Kinetics in signal transduction pathways involving promiscuous oligomerizing receptors can be determined by receptor specificity: apoptosis induction by TRAIL

Here we show by computer modeling that kinetics and outcome of signal transduction in case of hetero-oligomerizing receptors of a promiscuous ligand largely depend on the relative amounts of its receptors. Promiscuous ligands can trigger the formation of nonproductive receptor complexes, which slows down the formation of active receptor complexes and thus can block signal transduction. Our model predicts that increasing the receptor specificity of the ligand without changing its binding parameters should result in faster receptor activation and enhanced signaling. We experimentally validated this hypothesis using the cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its four membrane-bound receptors as an example. Bypassing ligand-induced receptor hetero-oligomerization by receptor-selective TRAIL variants enhanced the kinetics of receptor activation and augmented apoptosis. Our results suggest that control of signaling pathways by promiscuous ligands could result in apparent slow biological kinetics and blocking signal transmission. By modulating the relative amount of the different receptors for the ligand, signaling processes like apoptosis can be accelerated or decelerated and even inhibited. It also implies that more effective treatments using protein therapeutics could be achieved simply by altering specificity.

Substrate-dependent control of MAPK phosphorylation in vivo

Shvartsman and colleagues demonstrate that downstream nuclear substrates can regulate Drosophila MAPK phosphorylation levels by counteracting cytoplasmic phosphatases, providing in vivo evidence for significant retroactivity in a cellular signaling system.

Phosphorylation of the mitogen-activated protein kinase (MAPK) is essential for its enzymatic activity and ability to control multiple substrates inside a cell. According to the current models, control of MAPK phosphorylation is independent of its substrates, which are viewed as mere sensors of MAPK activity. Contrary to this modular view of MAPK signaling, our studies in the Drosophila embryo demonstrate that substrates can regulate the level of MAPK phosphorylation in vivo. We demonstrate that a twofold change in the gene dosage of a single substrate can induce a significant change in the phosphorylation level of MAPK and in the conversion of other substrates. Our results support a model where substrates of MAPK counteract its dephosphorylation by phosphatases. Substrate-dependent control of MAPK phosphorylation is a manifestation of a more general retroactive effect that should be intrinsic to all networks with covalent modification cycles.

Intrinsically disordered proteins: regulation and disease

Intrinsically disordered proteins (IDPs) are enriched in signaling and regulatory functions because disordered segments permit interaction with several proteins and hence the re-use of the same protein in multiple pathways. Understanding IDP regulation is important because altered expression of IDPs is associated with many diseases. Recent studies show that IDPs are tightly regulated and that dosage-sensitive genes encode proteins with disordered segments. The tight regulation of IDPs may contribute to signaling fidelity by ensuring that IDPs are available in appropriate amounts and not present longer than needed. The altered availability of IDPs may result in sequestration of proteins through non-functional interactions involving disordered segments (i.e., molecular titration), thereby causing an imbalance in signaling pathways. We discuss the regulation of IDPs, address implications for signaling, disease and drug development, and outline directions for future research.

Raf family kinases: old dogs have learned new tricks

First identified in the early 1980s as retroviral oncogenes, the Raf proteins have been the objects of intense research. The discoveries 10 years later that the Raf family members (Raf-1, B-Raf, and A-Raf) are bona fide Ras effectors and upstream activators of the ubiquitous ERK pathway increased the interest in these proteins primarily because of the central role that this cascade plays in cancer development. The important role of Raf in cancer was corroborated in 2002 with the discovery of B-Raf genetic mutations in a large number of tumors. This led to intensified drug development efforts to target Raf signaling in cancer. This work yielded not only recent clinical successes but also surprising insights into the regulation of Raf proteins by homodimerization and heterodimerization. Surprising insights also came from the hunt for new Raf targets. Although MEK remains the only widely accepted Raf substrate, new kinase-independent roles for Raf proteins have emerged. These include the regulation of apoptosis by suppressing the activity of the proapoptotic kinases, ASK1 and MST2, and the regulation of cell motility and differentiation by controlling the activity of Rok-α. In this review, we discuss the regulation of Raf proteins and their role in cancer, with special focus on the interacting proteins that modulate Raf signaling. We also describe the new pathways controlled by Raf proteins and summarize the successes and failures in the development of efficient anticancer therapies targeting Raf. Finally, we also argue for the necessity of more systemic approaches to obtain a better understanding of how the Ras-Raf signaling network generates biological specificity.

Exploring the spatial and temporal organization of a cell's proteome

To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and spatial distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteome's spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteome's organization into a spatially explicit, predictive model of cellular processes.

Tunable signal processing in synthetic MAP kinase cascades

The flexibility of MAPK cascade responses enables regulation of a vast array of cell fate decisions, but elucidating the mechanisms underlying this plasticity is difficult in endogenous signaling networks. We constructed insulated mammalian MAPK cascades in yeast to explore how intrinsic and extrinsic perturbations affect the flexibility of these synthetic signaling modules. Contrary to biphasic dependence on scaffold concentration, we observe monotonic decreases in signal strength as scaffold concentration increases. We find that augmenting the concentration of sequential kinases can enhance ultrasensitivity and lower the activation threshold. Further, integrating negative regulation and concentration variation can decouple ultrasensitivity and threshold from the strength of the response. Computational analyses show that cascading can generate ultrasensitivity and that natural cascades with different kinase concentrations are innately biased toward their distinct activation profiles. This work demonstrates that tunable signal processing is inherent to minimal MAPK modules and elucidates principles for rational design of synthetic signaling systems.

PLC regulation: emerging pictures for molecular mechanisms

Phosphoinositide-specific phospholipase C (PLC) enzymes are common signalling components linked to the activation of most cellular receptors. All PLC families are complex, modular, multi-domain proteins and together cover a broad spectrum of regulatory interactions, including direct binding to G protein subunits, small GTPases from Rho and Ras families, receptor and non-receptor tyrosine kinases and lipid components of cellular membranes. Recent structural determinations of PLC components and their complexes with regulatory proteins and direct mechanistic studies, together with earlier work, have provided the foundation to propose molecular mechanisms that stringently regulate PLC activity.

Multiple decisive phosphorylation sites for the negative feedback regulation of SOS1 via ERK

EGF-induced activation of ERK has been extensively studied by both experimental and theoretical approaches. Here, we used a simulation model based mostly on experimentally determined parameters to study the ERK-mediated negative feedback regulation of the Ras guanine nucleotide exchange factor, son of sevenless (SOS). Because SOS1 is phosphorylated at multiple serine residues upon stimulation, we evaluated the role of the multiplicity by building two simulation models, which we termed the decisive and cooperative phosphorylation models. The two models were constrained by the duration of Ras activation and basal phosphorylation level of SOS1. Possible solutions were found only in the decisive model wherein at least three, and probably more than four, phosphorylation sites decisively suppress the SOS activity. Thus, the combination of experimental approaches and the model analysis has suggested an unexpected role of multiple phosphorylations of SOS1 in the negative regulation.

Impaired binding of 14-3-3 to C-RAF in Noonan syndrome suggests new approaches in diseases with increased Ras signaling

The Ras-RAF-mitogen-activated protein kinase (Ras-RAF-MAPK) pathway is overactive in many cancers and in some developmental disorders. In one of those disorders, namely, Noonan syndrome, nine activating C-RAF mutations cluster around Ser(259), a regulatory site for inhibition by 14-3-3 proteins. We show that these mutations impair binding of 14-3-3 proteins to C-RAF and alter its subcellular localization by promoting Ras-mediated plasma membrane recruitment of C-RAF. By presenting biophysical binding data, the 14-3-3/C-RAFpS(259) crystal structure, and cellular analyses, we indicate a mechanistic link between a well-described human developmental disorder and the impairment of a 14-3-3/target protein interaction. As a broader implication of these findings, modulating the C-RAFSer(259)/14-3-3 protein-protein interaction with a stabilizing small molecule may yield a novel potential approach for treatment of diseases resulting from an overactive Ras-RAF-MAPK pathway.

Mechanical signals control SOX-9, VEGF, and c-Myc expression and cell proliferation during inflammation via integrin-linked kinase, B-Raf, and ERK1/2-dependent signaling in articular chondrocytes

Introduction

The importance of mechanical signals in normal and inflamed cartilage is well established. Chondrocytes respond to changes in the levels of proinflammatory cytokines and mechanical signals during inflammation. Cytokines like interleukin (IL)-1β suppress homeostatic mechanisms and inhibit cartilage repair and cell proliferation. However, matrix synthesis and chondrocyte (AC) proliferation are upregulated by the physiological levels of mechanical forces. In this study, we investigated intracellular mechanisms underlying reparative actions of mechanical signals during inflammation.

Methods

ACs isolated from articular cartilage were exposed to low/physiologic levels of dynamic strain in the presence of IL-1β. The cell extracts were probed for differential activation/inhibition of the extracellular signal-regulated kinase 1/2 (ERK1/2) signaling cascade. The regulation of gene transcription was examined by real-time polymerase chain reaction.

Results

Mechanoactivation, but not IL-1β treatment, of ACs initiated integrin-linked kinase activation. Mechanical signals induced activation and subsequent C-Raf-mediated activation of MAP kinases (MEK1/2). However, IL-1β activated B-Raf kinase activity. Dynamic strain did not induce B-Raf activation but instead inhibited IL-1β-induced B-Raf activation. Both mechanical signals and IL-1β induced ERK1/2 phosphorylation but discrete gene expression. ERK1/2 activation by mechanical forces induced SRY-related protein-9 (SOX-9), vascular endothelial cell growth factor (VEGF), and c-Myc mRNA expression and AC proliferation. However, IL-1β did not induce SOX-9, VEGF, and c-Myc gene expression and inhibited AC cell proliferation. More importantly, SOX-9, VEGF, and Myc gene transcription and AC proliferation induced by mechanical signals were sustained in the presence of IL-1β.

Conclusions

The findings suggest that mechanical signals may sustain their effects in proinflammatory environments by regulating key molecules in the MAP kinase signaling cascade. Furthermore, the findings point to the potential of mechanosignaling in cartilage repair during inflammation.

Differential effects of myopathy-associated caveolin-3 mutants on growth factor signaling

Caveolin-3 is an important scaffold protein of cholesterol-rich caveolae. Mutations of caveolin-3 cause hereditary myopathies that comprise remarkably different pathologies. Growth factor signaling plays an important role in muscle physiology; it is influenced by caveolins and cholesterol-rich rafts and might thus be affected by caveolin-3 dysfunction. Prompted by the observation of a marked chronic peripheral neuropathy in a patient suffering from rippling muscle disease due to the R26Q caveolin-3 mutation and because TrkA is expressed by neuronal cells and skeletal muscle fibers, we performed a detailed comparative study on the effect of pathogenic caveolin-3 mutants on the signaling and trafficking of the TrkA nerve growth factor receptor and, for comparison, of the epidermal growth factor receptor. We found that the R26Q mutant slightly and the P28L strongly reduced nerve growth factor signaling in TrkA-transfected cells. Surface biotinylation experiments revealed that the R26Q caveolin-3 mutation markedly reduced the internalization of TrkA, whereas the P28L did not. Moreover, P28L expression led to increased, whereas R26Q expression decreased, epidermal growth factor signaling. Taken together, we found differential effects of the R26Q and P28L caveolin-3 mutants on growth factor signaling. Our findings are of clinical interest because they might help explain the remarkable differences in the degree of muscle lesions caused by caveolin-3 mutations and also the co-occurrence of peripheral neuropathy in the R26Q caveolinopathy case presented.