Promoter-level expression clustering identifies time development of transcriptional regulatory cascades initiated by ErbB receptors in breast cancer cells

An ErbB Lineage Co-Regulon Harbors Potentially Co-Druggable Targets for Multimodal Precision Therapy in Head and Neck Squamous Cell Carcinoma

The ErbB lineage of oncogenic receptor tyrosine kinases is frequently overexpressed in head and neck squamous cell carcinomas. A common co-regulon triggered by the ErbB proteins; involving shared signaling circuitries; may harbor co-druggable targets or response biomarkers for potential future multimodal precision therapy in ErbB-driven head and neck squamous cell carcinoma. We here present a cohort-based; genome-wide analysis of 488 head and neck squamous cell carcinomas curated as part of The Cancer Genome Atlas Project to characterize genes that are significantly positively co-regulated with the four ErbB proteins and those that are shared among all ErbBs denoting a common ErbB co-regulon. Significant positive gene correlations involved hundreds of genes that were co-expressed with the four ErbB family members (q < 0.05). A common; overlapping co-regulon consisted of a core set of 268 genes that were uniformly co-regulated with all four ErbB genes and highly enriched for functions in chromatin organization and histone modifications. This high-priority set of genes contained ten putative antineoplastic drug-gene interactions. The nature and directionality of these ten drug-gene associations was an inhibiting interaction for seven (PIK3CB; PIK3C2B; HDAC4; FRK; PRKCE; EPHA4; and DYRK1A) of them in which the drug decreases the biological activity or expression of the gene target. For three (CHD4; ARID1A; and PBRM1) of the associations; the directionality of the interaction was such that the gene predicted sensitivit y to the drug suggesting utility as potential response biomarkers. Drug-gene interactions that predicted the gene product to be reduced by the drug included a variety of potential targeted molecular agent classes. This unbiased genome-wide analysis identified a target-rich environment for multimodal therapeutic approaches in tumors that are putatively ErbB-driven. The results of this study require preclinical validation before ultimately devising lines of combinatorial treatment strategies for ErbB-dependent head and neck squamous cell carcinomas that incorporate these findings.

Ten simple rules for organizing a special session at a scientific conference

Special sessions are important parts of scientific meetings and conferences: They gather together researchers and students interested in a specific topic and can strongly contribute to the success of the conference itself. Moreover, they can be the first step for trainees and students to the organization of a scientific event. Organizing a special session, however, can be uneasy for beginners and students. Here, we provide ten simple rules to follow to organize a special session at a scientific conference.

Trans-omics analysis of insulin action reveals a cell growth subnetwork which co-regulates anabolic processes

Insulin signaling promotes anabolic metabolism to regulate cell growth through multi-omic interactions. To obtain a comprehensive view of the cellular responses to insulin, we constructed a trans-omic network of insulin action in Drosophila cells that involves the integration of multi-omic data sets. In this network, 14 transcription factors, including Myc, coordinately upregulate the gene expression of anabolic processes such as nucleotide synthesis, transcription, and translation, consistent with decreases in metabolites such as nucleotide triphosphates and proteinogenic amino acids required for transcription and translation. Next, as cell growth is required for cell proliferation and insulin can stimulate proliferation in a context-dependent manner, we integrated the trans-omic network with results from a CRISPR functional screen for cell proliferation. This analysis validates the role of a Myc-mediated subnetwork that coordinates the activation of genes involved in anabolic processes required for cell growth.

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A trans-omic network of insulin action in Drosophila cells was constructed

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Insulin co-regulates various anabolic processes in a time-dependent manner

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The trans-omic network and a CRISPR screen for cell proliferation were integrated

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A Myc-mediated subnetwork promoting anabolic processes is required for cell growth

A systematic analysis of signaling reactivation and drug resistance

Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore output signaling. Complete signaling reactivation can only occur when at least two routes, an activating and inhibitory, connect an inhibited upstream protein to a downstream output. Irrespective of the network topology, drug-induced overexpression or increase in target dimerization can restore or even paradoxically increase downstream pathway activity. Kinase dimerization cooperates with inhibitor-mediated alleviation of negative feedback. Our findings inform drug development by considering network context and optimizing the design drug combinations. As an example, we predict and experimentally confirm specific combinations of RAF inhibitors that block mutant NRAS signaling.

Kholodenko et al. uncover signaling network circuitries and molecular mechanisms necessary and sufficient for complete reactivation or overshoot of steady-state signaling after kinase inhibitor treatment. The two means to revive signaling output fully are through network topology or reactivation of the kinase activity of the primary drug target. Blocking RAF dimer activity by a combination of type I½ and type II RAF inhibitors efficiently blocks mutant NRAS-driven ERK signaling.

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway

Simple Summary

Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.

Abstract

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

Trans-omic Analysis Reveals ROS-Dependent Pentose Phosphate Pathway Activation after High-Frequency Electrical Stimulation in C2C12 Myotubes

Skeletal muscle adaptation is mediated by cooperative regulation of metabolism, signal transduction, and gene expression. However, the global regulatory mechanism remains unclear. To address this issue, we performed electrical pulse stimulation (EPS) in differentiated C2C12 myotubes at low and high frequency, carried out metabolome and transcriptome analyses, and investigated phosphorylation status of signaling molecules. EPS triggered extensive and specific changes in metabolites, signaling phosphorylation, and gene expression during and after EPS in a frequency-dependent manner. We constructed trans-omic network by integrating these data and found selective activation of the pentose phosphate pathway including metabolites, upstream signaling molecules, and gene expression of metabolic enzymes after high-frequency EPS. We experimentally validated that activation of these molecules after high-frequency EPS was dependent on reactive oxygen species (ROS). Thus, the trans-omic analysis revealed ROS-dependent activation in signal transduction, metabolome, and transcriptome after high-frequency EPS in C2C12 myotubes, shedding light on possible mechanisms of muscle adaptation.

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We performed electrical pulse stimulation in differentiated C2C12 myotubes

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We constructed trans-omic network after high-frequency electrical pulse stimulation

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Trans-omic network integrates metabolome, transcriptome, and signaling molecules

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We identified ROS-dependent pentose phosphate pathway activation

Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights

Motivation

To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data.

Results

To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data.

Availability and implementation

DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy).

Supplementary information

Supplementary data are available at Bioinformatics online.

Over-expression of EGFR regulated by RARA contributes to 5-FU resistance in colon cancer

A promising new strategy for cancer therapy is to target the autophagic pathway. However, comprehensive characterization of autophagy genes and their clinical relevance in cancer is still lacking. Here, we systematically characterized alterations of autophagy genes in multiple cancer lines by analyzing data from The Cancer Genome Atlas and CellMiner database. Interactions between autophagy genes and clinically actionable genes (CAGs) were identified by analyzing co-expression, protein-protein interactions (PPIs) and transcription factor (TF) data. A key subnetwork was identified that included 18 autophagy genes and 22 CAGs linked by 28 PPI pairs and 1 TF-target pair, which was EGFR targeted by RARA. Alterations in the expression of autophagy genes were associated with patient survival in multiple cancer types. RARA and EGFR were associated with worse survival in colorectal cancer patients. The regulatory role of EGFR in 5-FU resistance was validated in colon cancer cells in vivo and in vitro. EGFR contributed to 5-FU resistance in colon cancer cells through autophagy induction, and EGFR overexpression in 5-FU resistant colon cancer was regulated by RARA. The present study provides a comprehensive analysis of autophagy in different cancer cell lines and highlights the potential clinical utility of targeting autophagy genes.

NET-CAGE characterizes the dynamics and topology of human transcribed cis-regulatory elements

Promoters and enhancers are key cis-regulatory elements, but how they operate to generate cell type-specific transcriptomes is not fully understood. We developed a simple and robust method, native elongating transcript-cap analysis of gene expression (NET-CAGE), to sensitively detect 5' ends of nascent RNAs in diverse cells and tissues, including unstable transcripts such as enhancer-derived RNAs. We studied RNA synthesis and degradation at the transcription start site level, characterizing the impact of differential promoter usage on transcript stability. We quantified transcription from cis-regulatory elements without the influence of RNA turnover, and show that enhancer-promoter pairs are generally activated simultaneously on stimulation. By integrating NET-CAGE data with chromatin interaction maps, we show that cis-regulatory elements are topologically connected according to their cell type specificity. We identified new enhancers with high sensitivity, and delineated primary locations of transcription within super-enhancers. Our NET-CAGE dataset derived from human and mouse cells expands the FANTOM5 atlas of transcribed enhancers, with broad applicability to biomedical research.

Entropy in Cell Biology: Information Thermodynamics of a Binary Code and Szilard Engine Chain Model of Signal Transduction

A model of signal transduction from the perspective of informational thermodynamics has been reported in recent studies, and several important achievements have been obtained. The first achievement is that signal transduction can be modelled as a binary code system, in which two forms of signalling molecules are utilised in individual steps. The second is that the average entropy production rate is consistent during the signal transduction cascade when the signal event number is maximised in the model. The third is that a Szilard engine can be a single-step model in the signal transduction. This article reviews these achievements and further introduces a new chain of Szilard engines as a biological reaction cascade (BRC) model. In conclusion, the presented model provides a way of computing the channel capacity of a BRC.

YAP-TEAD signaling promotes basal cell carcinoma development via a c-JUN/AP1 axis

The mammalian Hippo signaling pathway, through its effectors YAP and TAZ, coerces epithelial progenitor cell expansion for appropriate tissue development or regeneration upon damage. Its ability to drive rapid tissue growth explains why many oncogenic events frequently exploit this pathway to promote cancer phenotypes. Indeed, several tumor types including basal cell carcinoma (BCC) show genetic aberrations in the Hippo (or YAP/TAZ) regulators. Here, we uncover that while YAP is dispensable for homeostatic epidermal regeneration, it is required for BCC development. Our clonal analyses further demonstrate that the few emerging Yap-null dysplasia have lower fitness and thus are diminished as they progress to invasive BCC Mechanistically, YAP depletion in BCC tumors leads to effective impairment of the JNK-JUN signaling, a well-established tumor-driving cascade. Importantly, in this context, YAP does not influence canonical Wnt or Hedgehog signaling. Overall, we reveal Hippo signaling as an independent promoter of BCC pathogenesis and thereby a viable target for drug-resistant BCC.

Analysis of Cell Signal Transduction Based on Kullback-Leibler Divergence: Channel Capacity and Conservation of Its Production Rate during Cascade

Kullback–Leibler divergence (KLD) is a type of extended mutual entropy, which is used as a measure of information gain when transferring from a prior distribution to a posterior distribution. In this study, KLD is applied to the thermodynamic analysis of cell signal transduction cascade and serves an alternative to mutual entropy. When KLD is minimized, the divergence is given by the ratio of the prior selection probability of the signaling molecule to the posterior selection probability. Moreover, the information gain during the entire channel is shown to be adequately described by average KLD production rate. Thus, this approach provides a framework for the quantitative analysis of signal transduction. Moreover, the proposed approach can identify an effective cascade for a signaling network.

Prediction and identification of transcriptional regulatory elements at the lung cancer-specific DKK1 locus

The glycoprotein dickkopf 1 (DKK1) is highly expressed in lung cancer cell lines and tissues. Our previous study demonstrated that DKK1 promoter activity is low in lung cancer cell lines. This may be because it lacks the necessary transcriptional regulatory elements (TREs) required for higher activity levels. However, it is difficult to computationally predict functionally significant TREs, as TREs from different locations can affect large segments of distant DNA. The Encyclopedia of DNA Elements project features multiple integrated technologies and approaches for the discovery and definition of functional elements, including enhancer elements and enhancer-blocking insulators. In the present study, DNase I hypersensitive sites and histone modifications of DKK1 were investigated in the A549 lung cancer cell line using the UCSC Genome Browser. A set of cis-acting enhancer elements were identified by a dual-luciferase reporter gene assay system to increase activity of the DKK1 promoter with lung cancer specificity. To the best of our knowledge, these data provide the first insight into the role of the DKK1 locus in lung cancer, and confirm the contribution of intronic cis-acting elements to the regulation of DKK1 expression, providing a new insight into gene regulation in lung cancer, which could inform the development of targeted therapy.

The Conservation of Average Entropy Production Rate in a Model of Signal Transduction: Information Thermodynamics Based on the Fluctuation Theorem

Cell signal transduction is a non-equilibrium process characterized by the reaction cascade. This study aims to quantify and compare signal transduction cascades using a model of signal transduction. The signal duration was found to be linked to step-by-step transition probability, which was determined using information theory. By applying the fluctuation theorem for reversible signal steps, the transition probability was described using the average entropy production rate. Specifically, when the signal event number during the cascade was maximized, the average entropy production rate was found to be conserved during the entire cascade. This approach provides a quantitative means of analyzing signal transduction and identifies an effective cascade for a signaling network.

Information Thermodynamics Derives the Entropy Current of Cell Signal Transduction as a Model of a Binary Coding System

The analysis of cellular signaling cascades based on information thermodynamics has recently developed considerably. A signaling cascade may be considered a binary code system consisting of two types of signaling molecules that carry biological information, phosphorylated active, and non-phosphorylated inactive forms. This study aims to evaluate the signal transduction step in cascades from the viewpoint of changes in mixing entropy. An increase in active forms may induce biological signal transduction through a mixing entropy change, which induces a chemical potential current in the signaling cascade. We applied the fluctuation theorem to calculate the chemical potential current and found that the average entropy production current is independent of the step in the whole cascade. As a result, the entropy current carrying signal transduction is defined by the entropy current mobility.

Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization

Feedback control is a key mechanism in signal transduction, intimately involved in regulating the outcome of the cellular response. Here, we report a novel mechanism by which PHLDA1, Pleckstrin homology-like domain, family A, member 1, negatively regulates ErbB receptor signaling by inhibition of receptor oligomerization. We have found that the ErbB3 ligand, heregulin, induces PHILDA1 expression in MCF-7 cells. Transcriptionally-induced PHLDA1 protein directly binds to ErbB3, whereas knockdown of PHLDA1 increases complex formation between ErbB3 and ErbB2. To provide insight into the mechanism for our time-course and single-cell experimental observations, we performed a systematic computational search of network topologies of the mathematical models based on receptor dimer-tetramer formation in the ErbB activation processes. Our results indicate that only a model in which PHLDA1 inhibits formation of both dimers and tetramer can explain the experimental data. Predictions made from this model were further validated by single-molecule imaging experiments. Our studies suggest a unique regulatory feature of PHLDA1 to inhibit the ErbB receptor oligomerization process and thereby control the activity of receptor signaling network.

Deep Cap Analysis of Gene Expression (CAGE): Genome-Wide Identification of Promoters, Quantification of Their Activity, and Transcriptional Network Inference

Among the most significant findings of the post-genomic era, the discovery of pervasive transcription of mammalian genomes has tremendously modified our understanding of the genome output seen as RNA molecules. The increased focus on non-protein-coding genomic regions together with concomitant technological innovations has led to rapid discovery of numerous noncoding transcripts (ncRNAs). Biological relevance and functional roles of the vast majority of these ncRNAs remain largely unknown.The cap analysis of gene expression (CAGE) technology allows accurate transcript detection and quantification without relying on preexisting transcript models. In combination with complementary data sets, generated using other technologies, it has been shown as an efficient approach for exploring transcriptome complexity.Here, we describe the use of CAGE for the identification of novel noncoding transcripts in mammalian cells providing detailed information for basic data processing and advanced bioinformatics analyses.

Pseudotemporal Ordering of Single Cells Reveals Metabolic Control of Postnatal β Cell Proliferation

Pancreatic β cell mass for appropriate blood glucose control is established during early postnatal life. β cell proliferative capacity declines postnatally, but the extrinsic cues and intracellular signals that cause this decline remain unknown. To obtain a high-resolution map of β cell transcriptome dynamics after birth, we generated single-cell RNA-seq data of β cells from multiple postnatal time points and ordered cells based on transcriptional similarity using a new analytical tool. This analysis captured signatures of immature, proliferative β cells and established high expression of amino acid metabolic, mitochondrial, and Srf/Jun/Fos transcription factor genes as their hallmark feature. Experimental validation revealed high metabolic activity in immature β cells and a role for reactive oxygen species and Srf/Jun/Fos transcription factors in driving postnatal β cell proliferation and mass expansion. Our work provides the first high-resolution molecular characterization of state changes in postnatal β cells and paves the way for the identification of novel therapeutic targets to stimulate β cell regeneration.

Aberrant control of NF-κB in cancer permits transcriptional and phenotypic plasticity, to curtail dependence on host tissue: molecular mode

The role of the transcription factor NF-κB in shaping the cancer microenvironment is becoming increasingly clear. Inflammation alters the activity of enzymes that modulate NF-κB function, and causes extensive changes in genomic chromatin that ultimately drastically alter cell-specific gene expression. NF-κB regulates the expression of cytokines and adhesion factors that control interactions among adjacent cells. As such, NF-κB fine tunes tissue cellular composition, as well as tissues' interactions with the immune system. Therefore, NF-κB changes the cell response to hormones and to contact with neighboring cells. Activating NF-κB confers transcriptional and phenotypic plasticity to a cell and thereby enables profound local changes in tissue function and composition. Research suggests that the regulation of NF-κB target genes is specifically altered in cancer. Such alterations occur not only due to mutations of NF-κB regulatory proteins, but also because of changes in the activity of specific proteostatic modules and metabolic pathways. This article describes the molecular mode of NF-κB regulation with a few characteristic examples of target genes.

BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

Genome-wide co-localization of active EGFR and downstream ERK pathway kinases mirrors mitogen-inducible RNA polymerase 2 genomic occupancy

Genome-wide mechanisms that coordinate expression of subsets of functionally related genes are largely unknown. Recent studies show that receptor tyrosine kinases and components of signal transduction cascades including the extracellular signal-regulated protein kinase (ERK), once thought to act predominantly in the vicinity of plasma membrane and in the cytoplasm, can be recruited to chromatin encompassing transcribed genes. Genome-wide distribution of these transducers and their relationship to transcribing RNA polymerase II (Pol2) could provide new insights about co-regulation of functionally related gene subsets. Chromatin immunoprecipitations (ChIP) followed by deep sequencing, ChIP-Seq, revealed that genome-wide binding of epidermal growth factor receptor, EGFR and ERK pathway components at EGF-responsive genes was highly correlated with characteristic mitogen-induced Pol2-profile. Endosomes play a role in intracellular trafficking of proteins including their nuclear import. Immunofluorescence revealed that EGF-activated EGFR, MEK1/2 and ERK1/2 co-localize on endosomes. Perturbation of endosome internalization process, through the depletion of AP2M1 protein, resulted in decreased number of the EGFR containing endosomes and inhibition of Pol2, EGFR/ERK recruitment to EGR1 gene. Thus, mitogen-induced co-recruitment of EGFR/ERK components to subsets of genes, a kinase module possibly pre-assembled on endosome to synchronize their nuclear import, could coordinate genome-wide transcriptional events to ensure effective cell proliferation.

HER-3 targeting alters the dimerization pattern of ErbB protein family members in breast carcinomas

Breast carcinogenesis is a multi-step process in which membrane receptor tyrosine kinases are crucial participants. Lots of research has been done on epidermal growth factor receptor (EGFR) and HER-2 with important clinical results. However, breast cancer patients present intrinsic or acquired resistance to available HER-2-directed therapies, mainly due to HER-3. Using new techniques, such as proximity ligation assay, herein we evaluate the dimerization pattern of HER-3 and the importance of context-dependent dimer formation between HER-3 and other HER protein family members. Additionally, we show that the efficacy of novel HER-3 targeting agents can be better predicted in certain breast cancer patient sub-groups based on the dimerization pattern of HER protein family members. Moreover, this model was also evaluated and reproduced in human paraffin-embedded breast cancer tissues.