Data-derived modeling characterizes plasticity of MAPK signaling in melanoma

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Background

Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.

Methods

Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.

Results

Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.

Conclusions

Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00925-8.

Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate (P < 0.05) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

Transcriptomic analyses revealed the effect of Funneliformis mosseae on genes expression in Fusarium oxysporum

Soybean root rot is a typical soil-borne disease that severely affects the yield of soybean. Funneliformis mosseae is one of the arbuscular mycorrhizal fungi(AMF) dominant strains in soybean continuous cropping soil. The aim of this study was to providing an experimental basis for the study of the molecular mechanism underlying the alleviation of the obstacles associated with the continuous cropping of soybean by AMF. In this study, F. mosseae was inoculated in soil planted with soybean infected with Fusarium oxysporum. The results showed that the incidence of soybean root rot was significantly reduced after inoculation with F. mosseae. In F. mosseae-treated samples, the significantly upregulated genes encoded transmembrane protein in fungal cell membrane. The significantly downregulated genes encoded some proteins, which took part in composition of essential component of fungal cell wall; hydrolyse cellulose and hemicellulose. The DEGs in each treatment were enriched in antigen processing and presentation, carbon fixation in photosynthetic organisms, glycolysis/gluconeogenesis, the MAPK signalling pathway, protein processing in the endoplasmic reticulum and RNA degradation. Inoculation with F. mosseae could in a variety of ways to promote the growth, development of soybean and improve disease resistance. Such as help fungal build barriers to the disease resistance of host plant and enhance their pathogenicity; damaging the structure of the pathogen; protect plant tissues and so on. This study provides an experimental basis for further research on the molecular mechanism underlying the alleviation of challenges associated with the continuous cropping of soybean by AMF.

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

Computational models of melanoma

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.

Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.

Phenotype characterization of human melanoma cells resistant to dabrafenib

In the present study, the phenotype of melanoma cells resistant to dabrafenib (a B-RAF inhibitor) was investigated, to shed more light on melanoma resistance to B-RAF inhibition. Melanoma cells resistant to dabrafenib were generated using 3 different cell lines, A375, 397 and 624.38, all carrying B-RAF^V600E, and they were characterized by cytofluorometric analysis, Ion Torrent technology, immunofluorescence and biochemistry. All dabrafenib-resistant cells showed, in addition to a re-activation of MAPK signaling, morphological changes compared to their sensitive counterparts, accompanied by an increase in CD90 (mesenchymal marker) expression and a decrease in E-cadherin (epithelial marker) expression, suggesting an epithelial-to-mesenchymal-like phenotypic transition. However, melanoma cells with TGF-β1-induced epithelial-to-mesenchymal transition (EMT) were more sensitive to dabrafenib treatment compared to the sensitivity noted in the non-TGF-β1-induced EMT melanoma cells, suggesting that TGF-β1-induced EMT was not associated with dabrafenib resistance. Although dabrafenib-resistant cells exhibited increased cell motility and E-cadherin/vimentin reorganization, as expected in EMT, all of them showed unvaried E-cadherin mRNA and unchanged Snail protein levels, while Twist1 protein expression was decreased with the exception of A375 dabrafenib-resistant melanoma cells, where it was unaffected. These findings suggest a distinct active EMT-like process adopted by melanoma cells under drug exposure. Furthermore, dabrafenib-resistant cells exhibited stem cell-like features, with Oct4 translocation from the cytoplasm to peri-nuclear sites and nuclei, and increased CD20 expression. In conclusion, our data, in addition to confirming that resistance to dabrafenib is dependent on re-activation of MAPK signaling, suggest that this resistance is linked to a distinct active EMT-like process as well as stem-cell features adopted by melanoma cells.

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

Background

We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs).

Methods

We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems.

Results

We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores).

Conclusions

These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.

Computational modeling in melanoma for novel drug discovery

INTRODUCTION

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.

AREAS COVERED

This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.

EXPERT OPINION

Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Combinatorial drug screening and molecular profiling reveal diverse mechanisms of intrinsic and adaptive resistance to BRAF inhibition in V600E BRAF mutant melanomas

Over half of BRAFV600E melanomas display intrinsic resistance to BRAF inhibitors, in part due to adaptive signaling responses. In this communication we ask whether BRAFV600E melanomas share common adaptive responses to BRAF inhibition that can provide clinically relevant targets for drug combinations. We screened a panel of 12 treatment-naïve BRAFV600E melanoma cell lines with MAP Kinase pathway inhibitors in pairwise combination with 58 signaling inhibitors, assaying for synergistic cytotoxicity. We found enormous diversity in the drug combinations that showed synergy, with no two cell lines having an identical profile. Although the 6 lines most resistant to BRAF inhibition showed synergistic benefit from combination with lapatinib, the signaling mechanisms by which this combination generated synergistic cytotoxicity differed between the cell lines. We conclude that adaptive responses to inhibition of the primary oncogenic driver (BRAFV600E) are determined not only by the primary oncogenic driver but also by diverse secondary genetic and epigenetic changes ("back-seat drivers") and hence optimal drug combinations will be variable. Because upregulation of receptor tyrosine kinases is a major source of drug resistance arising from diverse adaptive responses, we propose that inhibitors of these receptors may have substantial clinical utility in combination with inhibitors of the MAP Kinase pathway.

Coordinating Role of RXRα in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling

During various inflammatory processes circulating cytokines including IL-6, IL-1β, and TNFα elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXRα playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRα gene silencing versus IL-6 induced negative gene regulation (r_s = 0.79; P<0.0001). These results concur with RXRα functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism.

During inflammation, circulating proinflammatory cytokines such as TNFα, IL-1ß, and IL-6, which are produced by, e.g., Kupffer cells, macrophages, or tumor cells, play important roles in hepatocellular signaling pathways and in the regulation of cellular homeostasis. In particular, these cytokines are responsible for the acute phase response (APR) but also for a dramatic reduction of drug detoxification capacity due to impaired expression of numerous genes coding for drug metabolic enzymes and transporters. Here we used high-throughput (phospho-)proteomic and gene expression data to investigate the impact of canonical signaling pathways in mediating IL-6-induced downregulation of drug metabolism related genes. We performed chemical inhibition perturbations to show that most of the IL-6 effects on gene expression are mediated through the MAPK and PI3K/AKT pathways. We constructed a prior knowledge network as basis for a fuzzy logic model that was trained with extensive gene expression data to identify critical regulatory nodes. Our results suggest that the nuclear receptor RXRα plays a central role, which was convincingly validated by RXRα gene silencing experiments. This work shows how computational modeling can support identifying decisive regulatory events from large-scale experimental data.

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

MOTIVATION

Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters.

RESULTS

In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

julio@iim.csic.es or saezrodriguez@ebi.ac.uk.

Network-based approaches for drug response prediction and targeted therapy development in cancer

Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.