Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

Beyond PDE4 inhibition: A comprehensive review on downstream cAMP signaling in the central nervous system

Cyclic adenosine monophosphate (cAMP) is a key second messenger that regulates signal transduction pathways pivotal for numerous biological functions. Intracellular cAMP levels are spatiotemporally regulated by their hydrolyzing enzymes called phosphodiesterases (PDEs). It has been shown that increased cAMP levels in the central nervous system (CNS) promote neuroplasticity, neurotransmission, neuronal survival, and myelination while suppressing neuroinflammation. Thus, elevating cAMP levels through PDE inhibition provides a therapeutic approach for multiple CNS disorders, including multiple sclerosis, stroke, spinal cord injury, amyotrophic lateral sclerosis, traumatic brain injury, and Alzheimer's disease. In particular, inhibition of the cAMP-specific PDE4 subfamily is widely studied because of its high expression in the CNS. So far, the clinical translation of full PDE4 inhibitors has been hampered because of dose-limiting side effects. Hence, focusing on signaling cascades downstream activated upon PDE4 inhibition presents a promising strategy, offering novel and pharmacologically safe targets for treating CNS disorders. Yet, the underlying downstream signaling pathways activated upon PDE(4) inhibition remain partially elusive. This review provides a comprehensive overview of the existing knowledge regarding downstream mediators of cAMP signaling induced by PDE4 inhibition or cAMP stimulators. Furthermore, we highlight existing gaps and future perspectives that may incentivize additional downstream research concerning PDE(4) inhibition, thereby providing novel therapeutic approaches for CNS disorders.

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.

Mechanisms of Melanoma Progression and Treatment Resistance: Role of Cancer Stem-like Cells

Melanoma is the third most common type of skin cancer, characterized by its heterogeneity and propensity to metastasize to distant organs. Melanoma is a heterogeneous tumor, composed of genetically divergent subpopulations, including a small fraction of melanoma-initiating cancer stem-like cells (CSCs) and many non-cancer stem cells (non-CSCs). CSCs are characterized by their unique surface proteins associated with aberrant signaling pathways with a causal or consequential relationship with tumor progression, drug resistance, and recurrence. Melanomas also harbor significant alterations in functional genes (BRAF, CDKN2A, NRAS, TP53, and NF1). Of these, the most common are the BRAF and NRAS oncogenes, with 50% of melanomas demonstrating the BRAF mutation (BRAFV600E). While the successful targeting of BRAFV600E does improve overall survival, the long-term efficacy of available therapeutic options is limited due to adverse side effects and reduced clinical efficacy. Additionally, drug resistance develops rapidly via mechanisms involving fast feedback re-activation of MAPK signaling pathways. This article updates information relevant to the mechanisms of melanoma progression and resistance and particularly the mechanistic role of CSCs in melanoma progression, drug resistance, and recurrence.

Targeting the RAS/RAF/MAPK pathway for cancer therapy: from mechanism to clinical studies

Metastatic dissemination of solid tumors, a leading cause of cancer-related mortality, underscores the urgent need for enhanced insights into the molecular and cellular mechanisms underlying metastasis, chemoresistance, and the mechanistic backgrounds of individuals whose cancers are prone to migration. The most prevalent signaling cascade governed by multi-kinase inhibitors is the mitogen-activated protein kinase (MAPK) pathway, encompassing the RAS-RAF-MAPK kinase (MEK)-extracellular signal-related kinase (ERK) pathway. RAF kinase is a primary mediator of the MAPK pathway, responsible for the sequential activation of downstream targets, such as MEK and the transcription factor ERK, which control numerous cellular and physiological processes, including organism development, cell cycle control, cell proliferation and differentiation, cell survival, and death. Defects in this signaling cascade are associated with diseases such as cancer. RAF inhibitors (RAFi) combined with MEK blockers represent an FDA-approved therapeutic strategy for numerous RAF-mutant cancers, including melanoma, non-small cell lung carcinoma, and thyroid cancer. However, the development of therapy resistance by cancer cells remains an important barrier. Autophagy, an intracellular lysosome-dependent catabolic recycling process, plays a critical role in the development of RAFi resistance in cancer. Thus, targeting RAF and autophagy could be novel treatment strategies for RAF-mutant cancers. In this review, we delve deeper into the mechanistic insights surrounding RAF kinase signaling in tumorigenesis and RAFi-resistance. Furthermore, we explore and discuss the ongoing development of next-generation RAF inhibitors with enhanced therapeutic profiles. Additionally, this review sheds light on the functional interplay between RAF-targeted therapies and autophagy in cancer.

Possible correlated signaling pathways with chronic urate nephropathy: A review

Hyperuricemia nephropathy, also known as gouty nephropathy, refers to renal damage induced by hyperuricemia caused by excessive production of serum uric acid or low excretion of uric acid. the persistence of symptoms will lead to changes in renal tubular phenotype and accelerate the progress of renal fibrosis. The existence and progressive aggravation of symptoms will bring a heavy burden to patients, their families and society, affect their quality of life and reduce their well-being. With the increase of reports on hyperuricemia nephropathy, the importance of related signal pathways in the pathogenesis of hyperuricemia nephropathy is becoming more and more obvious, but most studies are limited to the upper and lower mediating relationship between 1 or 2 signal pathways. The research on the comprehensiveness of signal pathways and the breadth of crosstalk between signal pathways is limited. By synthesizing the research results of signal pathways related to hyperuricemia nephropathy in recent years, this paper will explore the specific mechanism of hyperuricemia nephropathy, and provide new ideas and methods for the treatment of hyperuricemia nephropathy based on a variety of signal pathway crosstalk and personal prospects.

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

Dysregulated Signalling Pathways Driving Anticancer Drug Resistance

One of the leading causes of death worldwide, in both men and women, is cancer. Despite the significant development in therapeutic strategies, the inevitable emergence of drug resistance limits the success and impedes the curative outcome. Intrinsic and acquired resistance are common mechanisms responsible for cancer relapse. Several factors crucially regulate tumourigenesis and resistance, including physical barriers, tumour microenvironment (TME), heterogeneity, genetic and epigenetic alterations, the immune system, tumour burden, growth kinetics and undruggable targets. Moreover, transforming growth factor-beta (TGF-β), Notch, epidermal growth factor receptor (EGFR), integrin-extracellular matrix (ECM), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR), wingless-related integration site (Wnt/β-catenin), Janus kinase/signal transducers and activators of transcription (JAK/STAT) and RAS/RAF/mitogen-activated protein kinase (MAPK) signalling pathways are some of the key players that have a pivotal role in drug resistance mechanisms. To guide future cancer treatments and improve results, a deeper comprehension of drug resistance pathways is necessary. This review covers both intrinsic and acquired resistance and gives a comprehensive overview of recent research on mechanisms that enable cancer cells to bypass barriers put up by treatments, and, like "satellite navigation", find alternative routes by which to carry on their "journey" to cancer progression.

Emerging maps of allosteric regulation in cellular networks

Allosteric regulation is classically defined as action at a distance, where a perturbation outside of a protein active site affects function. While this definition has motivated many studies of allosteric mechanisms at the level of protein structure, translating these insights to the allosteric regulation of entire cellular processes - and their crosstalk - has received less attention, despite the broad importance of allostery for cellular regulation foreseen by Jacob and Monod. Here, we revisit an evolutionary model for the widespread emergence of allosteric regulation in colocalized proteins, describe supporting evidence, and discuss emerging advances in mapping allostery in cellular networks that link precise and often allosteric perturbations at the molecular level to functional changes at the pathway and systems levels.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.