A Regulated Double-Negative Feedback Decodes the Temporal Gradient of Input Stimulation in a Cell Signaling Network

Park, Sang Min; Shin, Sung-Young; Cho, Kwang Hyun

doi:10.1371/journal.pone.0162153

Cited by 3 publications

(5 citation statements)

References 46 publications

(65 reference statements)

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“…The concordance between the model simulations and the experimental data indicates that the behaviour results from the network topology and connections and not from specific parameters in the model. Various network motifs that elicit different responses to static and dynamic inputs have been described previously 2 , 4 , 27 . For instance, Park et al .…”

Section: Resultsmentioning

confidence: 99%

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Dynamic hydrogen peroxide levels reveal a rate-dependent sensitivity in B-cell lymphoma signaling

Witmond,

Keizer,

Kiffen

et al. 2024

Sci Rep

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Although in vivo extracellular microenvironments are dynamic, most in vitro studies are conducted under static conditions. Here, we exposed diffuse large B-cell lymphoma (DLBCL) cells to gradient increases in the concentration of hydrogen peroxide (H2O2), thereby capturing some of the dynamics of the tumour microenvironment. Subsequently, we measured the phosphorylation response of B-cell receptor (BCR) signalling proteins CD79a, SYK and PLCγ2 at a high temporal resolution via single-cell phospho-specific flow cytometry. We demonstrated that the cells respond bimodally to static extracellular H2O2, where the percentage of cells that respond is mainly determined by the concentration. Computational analysis revealed that the bimodality results from a combination of a steep dose–response relationship and cell-to-cell variability in the response threshold. Dynamic gradient inputs of varying durations indicated that the H2O2 concentration is not the only determinant of the signalling response, as cells exposed to more shallow gradients respond at lower H2O2 levels. A minimal model of the proximal BCR network qualitatively reproduced the experimental findings and uncovered a rate-dependent sensitivity to H2O2, where a lower rate of increase correlates to a higher sensitivity. These findings will bring us closer to understanding how cells process information from their complex and dynamic in vivo environments.

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Section: Resultsmentioning

confidence: 99%

“…For instance, Park et al . (2016) found that a 3-node network motif with regulated double-negative feedback could give a transient response upon a static input, but a sustained response upon a dynamic gradient input 27 . Johnson et al .…”

Section: Resultsmentioning

confidence: 99%

Dynamic hydrogen peroxide levels reveal a rate-dependent sensitivity in B-cell lymphoma signaling

Witmond,

Keizer,

Kiffen

et al. 2024

Sci Rep

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“…The ODEs of the network model were constructed mainly based on Michaelis–Menten kinetics as follows:

\begin{matrix} \frac{d false[normalEGFRa false]}{italicdt} & = k_{1} false[normalEGF false] \cdot \frac{[EGFRi] false/ K_{normalm 1}}{1 + [EGFRi] false/ K_{normalm 1} + [Cetuximab] false/ K_{normali 1}} - k_{normald 1} [EGFRa] \\ \frac{d false[normalKRASa false]}{italicdt} & = (k_{2 normala} [EGFRa] + k_{2 normalb} [normalGNB 5]) \cdot \frac{false[normalKRASi false]}{K_{normalm 2} + [KRASi]} - k_{normald 2} [KRASa] \\ \frac{d false[normalMEKa false]}{italicdt} & = k_{3} false[normalKRASa false] \cdot \frac{false[normalMEKi false]}{K_{normalm 3} + [MEKi]} - k_{normald 3} [MEKa] \\ \frac{d false[normalERKa false]}{italicdt} & = k_{4} false[normalMEKa false] \cdot \frac{false[normalERKi false]}{K_{normalm 4} + [ERKi]} - k_{normald 4} [ERKa] \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…The ODEs of the network model were constructed mainly based on Michaelis-Menten kinetics [55] as follows:…”

Section: Mathematical Modelingmentioning

confidence: 99%

Systems analysis identifies potential target genes to overcome cetuximab resistance in colorectal cancer cells

et al. 2019

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Cetuximab (CTX), a monoclonal antibody against epidermal growth factor receptor, is being widely used for colorectal cancer (CRC) with wild‐type (WT) KRAS. However, its responsiveness is still very limited and WT KRAS is not enough to indicate such responsiveness. Here, by analyzing the gene expression data of CRC patients treated with CTX monotherapy, we have identified DUSP4, ETV5, GNB5, NT5E, and PHLDA1 as potential targets to overcome CTX resistance. We found that knockdown of any of these five genes can increase CTX sensitivity in KRAS WT cells. Interestingly, we further found that GNB5 knockdown can increase CTX sensitivity even for KRAS mutant cells. We unraveled that GNB5 overexpression contributes to CTX resistance by modulating the Akt signaling pathway from experiments and mathematical simulation. Overall, these results indicate that GNB5 might be a promising target for combination therapy with CTX irrespective of KRAS mutation.

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“…[42] Another study observed that stimulating cultured neurons with different temporal gradients of brain-derived neurotrophic factor (BDNF) produced different cellular responses that resulted from interconnected positive and negative feedback loops. [43] By identifying such circuits within a larger complex network, systems biology can uncover the critical circuits to effectively target with new drugs, as well as the circuits that are involved in the responses to currently used drugs. Additionally, because systems biology reveals dynamic properties of networks, this approach can guide the kinetics of drug delivery.…”

Section: Network Dynamics and Drug Resistancementioning

confidence: 99%

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering

et al. 2020

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Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these “undruggable” targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

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A Regulated Double-Negative Feedback Decodes the Temporal Gradient of Input Stimulation in a Cell Signaling Network

Cited by 3 publications

References 46 publications

Dynamic hydrogen peroxide levels reveal a rate-dependent sensitivity in B-cell lymphoma signaling

Dynamic hydrogen peroxide levels reveal a rate-dependent sensitivity in B-cell lymphoma signaling

Systems analysis identifies potential target genes to overcome cetuximab resistance in colorectal cancer cells

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering

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