Biomedical subjects

Kevin A Janes

Publications and source records attributed to Kevin A Janes.

10 recordsLinked to original sources

PubMed2006

Adenoviral vector saturates Akt pro-survival signaling and blocks insulin-mediated rescue of tumor necrosis-factor-induced apoptosis.

Recombinant adenoviruses are used extensively as delivery vectors in clinical gene therapy and in molecular biology, but little is known about how the viral carrier itself contributes to cellular responses. Here we show that infection with an E1/E3-deleted adenoviral vector (Adv) sensitizes human epithelial cells to tumor necrosis factor (TNF)-induced apoptosis. To explore the mechanism of Adv-mediated sensitization, we measured activity time courses for three protein kinases (MK2, IKK and JNK1) centrally involved in the TNF-receptor signaling network, as well as two kinases (Akt and ERK) activated by growth factors. Both the pro-apoptotic signal MK2 and the anti-apoptotic signal Akt were upregulated when Adv-infected cells were stimulated with TNF, and MK2 and Akt each contributed significantly to TNF-induced cell fate. Surprisingly, further activation of Akt in Adv-infected cells via insulin treatment did not significantly reduce apoptosis or MK2 activity. We show that the ineffectiveness of insulin-mediated anti-apoptotic signaling through Akt is due to saturation of Akt-effector substrate phosphorylation in Adv-infected cells. Normalizing Akt signaling relative to its Adv-induced baseline activity identified a global dose-response curve that relates Akt signaling to cellular survival. Thus, the background Akt activity induced by Adv limits the transmission of anti-apoptotic signals in response to further cytokine or growth-factor stimulation. The phenotypic and intracellular synergy between Adv and TNF may have implications for interpreting cellular responses in gene-therapy and laboratory applications.

Adenoviridae↗

PubMed2006

The response of human epithelial cells to TNF involves an inducible autocrine cascade.

Tumor necrosis factor (TNF) is a proinflammatory cytokine that induces conflicting pro- and antiapoptotic signals whose relative strengths determine the extent of cell death. TNF receptor (TNFR) has been studied in considerable detail, but it is not known how crosstalk among antagonistic pro- and antiapoptotic signals is achieved. Here we report an experimental and computational analysis of crosstalk between prodeath TNF and prosurvival growth factors in human epithelial cells. By applying classifier-based regression to a cytokine-signaling compendium of approximately 8000 intracellular protein measurements, we demonstrate that cells respond to TNF both directly, via activated TNF receptor, and indirectly, via the sequential release of transforming growth factor-alpha (TGF-alpha), interleukin-1alpha (IL-1alpha), and IL-1 receptor antagonist (IL-1ra). We refer to the contingent and time-varying series of extracellular signals induced by TNF as an "autocrine cascade." Time-dependent crosstalk of synergistic and antagonistic autocrine circuits may serve to link cellular responses to the local environment.

Autocrine Communication↗

PubMed2006

A biological approach to computational models of proteomic networks.

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach.

Computer Simulation↗

PubMed2006

Applying computational modeling to drug discovery and development.

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.

Animals↗

PubMed2006

Data-driven modelling of signal-transduction networks.

New technologies are permitting large-scale quantitative studies of signal-transduction networks. Such data are hard to understand completely by inspection and intuition. 'Data-driven models' help users to analyse large data sets by simplifying the measurements themselves. Data-driven modelling approaches such as clustering, principal components analysis and partial least squares can derive biological insights from large-scale experiments. These models are emerging as standard tools for systems-level research in signalling networks.

Cluster Analysis↗

PubMed2005

A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis.

Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information is processed as a network. We constructed a systems model of 7980 intracellular signaling events that directly links measurements to 1440 response outputs associated with apoptosis. The model accurately predicted multiple time-dependent apoptotic responses induced by a combination of the death-inducing cytokine tumor necrosis factor with the prosurvival factors epidermal growth factor and insulin. By capturing the role of unsuspected autocrine circuits activated by transforming growth factor-alpha and interleukin-1alpha, the model revealed new molecular mechanisms connecting signaling to apoptosis. The model derived two groupings of intracellular signals that constitute fundamental dimensions (molecular "basis axes") within the apoptotic signaling network. Projection along these axes captures the entire measured apoptotic network, suggesting that cell survival is determined by signaling through this canonical basis set.

Apoptosis↗

PubMed2005

A compendium of signals and responses triggered by prodeath and prosurvival cytokines.

Cell-signaling networks consist of proteins with a variety of functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities and biochemical activities of these signaling proteins, the ways in which they are combined into networks to process and transduce signals are poorly understood. Network-level understanding of signaling requires data on a wide variety of biochemical processes such as posttranslational modification, assembly of macromolecular complexes, enzymatic activity, and localization. No single method can gather such heterogeneous data in high throughput, and most studies of signal transduction therefore rely on series of small, discrete experiments. Inspired by the power of systematic datasets in genomics, we set out to build a systematic signaling dataset that would enable the construction of predictive models of cell-signaling networks. Here we describe the compilation and fusion of approximately 10,000 signal and response measurements acquired from HT-29 cells treated with tumor necrosis factor-alpha, a proapoptotic cytokine, in combination with epidermal growth factor or insulin, two prosurvival growth factors. Nineteen protein signals were measured over a 24-h period using kinase activity assays, quantitative immunoblotting, and antibody microarrays. Four different measurements of apoptotic response were also collected by flow cytometry for each time course. Partial least squares regression models that relate signaling data to apoptotic response data reveal which aspects of compendium construction and analysis were important for the reproducibility, internal consistency, and accuracy of the fused set of signaling measurements. We conclude that it is possible to build self-consistent compendia of cell-signaling data that can be mined computationally to yield important insights into the control of mammalian cell responses.

Amino Acid Sequence↗

PubMed2005

A multiplexed homogeneous fluorescence-based assay for protein kinase activity in cell lysates.

New methods to quantify protein kinase activities directly from complex cellular mixtures are critical for understanding biological regulatory pathways. Herein, a fluorescence-based chemosensor strategy for the direct measurement of kinase activities in crude mammalian cell lysates is described. We first designed a new fluorescent peptide reporter substrate for each target kinase. These kinase chemosensors were readily phosphorylated by recombinant target enzyme and underwent a several-fold fluorescence increase upon phosphorylation. Then, using unfractionated cell lysates, a homogeneous kinase assay was developed that was reproducible, linear and highly preferential for monitoring changes in cellular activity of the target kinase. The general protocol was developed for the kinase Akt and then easily extended to measure protein kinase A (PKA) and mitogen-activated protein kinase-associated protein kinase 2 (MK2) activities. This assay platform is immediately useful for studying protein kinase signaling in crude cellular extracts.

Biosensing Techniques↗

PubMed2004

Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data.

Biological signaling networks process extracellular cues to control important cell decisions such as death-survival, growth-quiescence, and proliferation-differentiation. After receptor activation, intracellular signaling proteins change in abundance, modification state, and enzymatic activity. Many of the proteins in signaling networks have been identified, but it is not known how signaling molecules work together to control cell decisions. To begin to address this issue, we report the use of partial least squares regression as an analytical method to glean signal-response relationships from heterogeneous multivariate signaling data collected from HT-29 human colon carcinoma cells stimulated to undergo programmed cell death. By partial least squares modeling, we relate dynamic and quantitative measurements of 20-30 intracellular signals to cell survival after treatment with tumor necrosis factor alpha (a death factor) and insulin (a survival factor). We find that partial least squares models can distinguish highly informative signals from redundant uninformative signals to generate a reduced model that retains key signaling features and signal-response relationships. In these models, measurements of biochemical characteristics, based on very different techniques (Western blots, kinase assays, etc.), are grouped together as covariates, showing that heterogenous data have been effectively fused. Importantly, informative protein predictors of cell responses are always multivariate, demonstrating the multicomponent nature of the decision process.

Apoptosis↗

PubMed2003

A high-throughput quantitative multiplex kinase assay for monitoring information flow in signaling networks: application to sepsis-apoptosis.

To treat complex human diseases effectively, a systems-level approach is needed to understand the interplay of environmental cues, intracellular signals, and cellular behaviors that underlie disease states. This approach requires high-throughput, multiplex techniques that measure quantitative temporal variations of multiple protein activities in the intracellular signaling network. Here, we describe a single microtiter-based format that simultaneously quantifies protein kinase activities in the phosphatidylinositol 3-kinase pathway (Akt), nuclear factor-kappaB pathway (IKK), and three core mitogen-activated protein kinase pathways (ERK, JNK1, MK2). These parallel high-throughput assays are stringently linear, redundantly specific, reproducible, and sensitive compared with classical low-throughput techniques. When applied to a model of sepsis-induced colon epithelial apoptosis, this approach identified a late phase of Akt activity as a critical mediator of cell survival that quantitatively contributed to the efficacy of insulin as an anti-apoptotic cue. Thus, sampling parallel nodes in the intracellular signaling network identified part of the molecular mechanism underlying the efficacy of insulin in the treatment of human sepsis.

Apoptosis↗