Boolean modeling of transcriptome data reveals novel modes of heterotrimeric G-protein action.
Ontology highlight
ABSTRACT:
SUBMITTER: Pandey S
PROVIDER: S-EPMC2913393 | biostudies-literature | 2010 Jun
REPOSITORIES: biostudies-literature
Publications
Boolean modeling of transcriptome data reveals novel modes of heterotrimeric G-protein action.
Pandey Sona S Wang Rui-Sheng RS Wilson Liza L Li Song S Zhao Zhixin Z Gookin Timothy E TE Assmann Sarah M SM Albert Réka R
Molecular systems biology 20100601
Heterotrimeric G-proteins mediate crucial and diverse signaling pathways in eukaryotes. Here, we generate and analyze microarray data from guard cells and leaves of G-protein subunit mutants of the model plant Arabidopsis thaliana, with or without treatment with the stress hormone, abscisic acid. Although G-protein control of the transcriptome has received little attention to date in any system, transcriptome analysis allows us to search for potentially uncommon yet significant signaling mechani ...[more]
Similar Datasets
Project description:Insomnia and, more generally, lack of sleep are on the rise. Traditionally treated by classical hypnotics, such as benzodiazepines and Z drugs, which both act on the GABAA receptor, and other modalities, including nondrug therapies, such as cognitive behavioural therapy, there is a range of new hypnotics which are being developed or have recently received market approval. Suvorexant and the like target the orexin/hypocretin system: they should have less side effects in terms of drug-drug interactions with e.g. alcohol, less memory impairment and dependence potential compared to classical hypnotics.
| S-EPMC7015741 | biostudies-literature
Project description:Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.
| S-EPMC8564392 | biostudies-literature
Project description:BACKGROUND & AIMS:Legalon® SIL (SIL) is a chemically hydrophilized version of silibinin that has exhibited high antiviral effectiveness against hepatitis C virus (HCV). Its main mode of action (MOA) remains unclear, with contradicting in vitro studies supporting either suppression of entry and cell-to-cell spread or suppression of viral RNA synthesis as the main MOA. We sought to provide new insights into SIL's MOA in HCV genotype-1/4 patients receiving intravenous SIL monotherapy for 7 days via mathematical modeling. METHODS:Changes in HCV RNA in 25 patients receiving 10, 15, or 20mg/kg/day of SIL were analyzed and modeled using viral kinetic methods. RESULTS:In 15 patients, the virus declined in a biphasic manner, in which a sharp drop between days 0 and 2 was followed by a slower second phase of decline. In 10 patients, the initial decline was weaker and the virus declined in a single phase over the 7-day period. The blocking production effectiveness, ?, was dose-dependent with mean ?=0.49 and 0.89 in the 10 or 15 and 20mg/kg/day dosing groups, respectively (p=0.02). The effectiveness of blocking viral infection, ?, was estimated as 0.60 with no significant differences across dosing groups. For all patients, the mean rate of viral load decline measured between days 2 and 7 was high (0.3 log(10)IU/ml/day), i.e., 4-fold higher than typically observed during the 2nd phase of (pegylated)-interferon-?±ribavirin treatment. CONCLUSIONS:Modeling HCV kinetics in vivo suggests that SIL may block both viral infection and viral production/release with its main dose-dependent effect being blocking viral production/release.
| S-EPMC3328661 | biostudies-literature
Project description:The prefoldin complex (PFDc) was identified in humans as a co-chaperone of the cytosolic chaperonin T-COMPLEX PROTEIN RING COMPLEX (TRiC)/CHAPERONIN CONTAINING TCP-1 (CCT). PFDc is conserved in eukaryotes and is composed of subunits PFD1-6, and PFDc-TRiC/CCT folds actin and tubulins. PFDs also participate in a wide range of cellular processes, both in the cytoplasm and in the nucleus, and their malfunction causes developmental alterations and disease in animals and altered growth and environmental responses in yeast and plants. Genetic analyses in yeast indicate that not all of their functions require the canonical complex. The lack of systematic genetic analyses in plants and animals, however, makes it difficult to discern whether PFDs participate in a process as the canonical complex or in alternative configurations, which is necessary to understand their mode of action. To tackle this question, and on the premise that the canonical complex cannot be formed if one subunit is missing, we generated an Arabidopsis (Arabidopsis thaliana) mutant deficient in the six PFDs and compared various growth and environmental responses with those of the individual mutants. In this way, we demonstrate that the PFDc is required for seed germination, to delay flowering, or to respond to high salt stress or low temperature, whereas at least two PFDs redundantly attenuate the response to osmotic stress. A coexpression analysis of differentially expressed genes in the sextuple mutant identified several transcription factors, including ABA INSENSITIVE 5 (ABI5) and PHYTOCHROME-INTERACTING FACTOR 4, acting downstream of PFDs. Furthermore, the transcriptomic analysis allowed assigning additional roles for PFDs, for instance, in response to higher temperature.
| S-EPMC8566299 | biostudies-literature
Project description:The nerve growth factor NGF has been shown to cause cell fate decisions towards either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Using dynamic proteome and transcriptome data from NGF-stimulated PC12 cells over a time span of 24 hours we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Optimal model topology was achieved by introducing multiple time scales for fast cellular signaling and slower gene response and subsequent fitting to the experimental data. The integrated model confirmed the parallel use of MEK/ERK, AKT/PI3K and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK/JUN inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that sustained transcriptional feedback is necessary to induce bistability in the cell fate switch. De novo Gene expression was necessary to activate autocrine feedback that caused integrin signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision towards differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation. To eludicate the transcriptome response of PC12 cells time-resolved gene expression data of NGF or EGF simulated PC12 were recorded at t = [0.5h 1h, 2h, 3h, 4h, 5h, 6h, 8h, 12h, 24h, 48h] for NGF and at t=[1h, 2h, 3h, 4h, 6h, 8h, 12h, 24h]. Control time points of unstimulated PC12 cells were taken at t=[0h, 2h, 4h, 6h, 8h, 24h, 48h]. Additionally, the MEK-inhibitor UO126 was added together with NGF and the gene response was measured at t=[1h, 2h, 4h, 6h, 8h, 12h, 24h, 48h] and for UO126 alone at t=[1h, 2h, 4h, 8h, 12h, 48h]. Total RNA was isolated, labeled and hybridized to an Illumina ratRef-12 bead array (Illumina, San Diego, CA, USA) according to the manufacturerâ??s protocol.
2016-03-23 | E-GEOD-74327 | biostudies-arrayexpress
Project description:A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.
| S-EPMC6167558 | biostudies-literature
Project description:The nerve growth factor NGF has been shown to cause cell fate decisions toward either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT, or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Comparing the time-resolved transcriptome response of NGF- or EGF-stimulated PC12 cells over 24 h in combination with protein and phenotype data we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Network topology was optimized by fitting the model to time-resolved transcriptome data under MEK, PI3K, or JNK inhibition. The integrated model confirmed the parallel use of MAPK/ERK, PI3K/AKT, and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that positive transcriptional feedback induces bistability in the cell fate switch. De novo gene expression was necessary to activate autocrine feedback that caused Urokinase-Type Plasminogen Activator (uPA) Receptor signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision toward differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation.
| S-EPMC4830832 | biostudies-literature
Project description:Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.
| S-EPMC6531050 | biostudies-literature
Project description:The nerve growth factor NGF has been shown to cause cell fate decisions towards either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Using dynamic proteome and transcriptome data from NGF-stimulated PC12 cells over a time span of 24 hours we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Optimal model topology was achieved by introducing multiple time scales for fast cellular signaling and slower gene response and subsequent fitting to the experimental data. The integrated model confirmed the parallel use of MEK/ERK, AKT/PI3K and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK/JUN inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that sustained transcriptional feedback is necessary to induce bistability in the cell fate switch. De novo Gene expression was necessary to activate autocrine feedback that caused integrin signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision towards differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation.
2016-03-23 | GSE74327 | GEO
Project description:As opposed to the standard tolerogenic apoptosis, immunogenic cell death (ICD) constitutes a type of cellular demise that elicits an adaptive immune response. ICD has been characterized in malignant cells following cytotoxic interventions, such as chemotherapy or radiotherapy. Briefly, ICD of cancer cells releases some stress/danger signals that attract and activate dendritic cells (DCs). The latter can then engulf and cross-present tumor antigens to T lymphocytes, thus priming a cancer-specific immunity. This series of reactions works as a positive feedback loop where the antitumor immunity further improves the therapeutic efficacy by targeting cancer cells spared by the cytotoxic agent. However, not all chemotherapeutic drugs currently approved for cancer treatment are able to stimulate bona fide ICD: some commonly used agents, such as cisplatin or 5-fluorouracil, are unable to activate all features of ICD. Therefore, a better characterization of the process could help identify some gene or protein candidates to target pharmacologically and suggest combinations of drugs that would favor/increase antitumor immune response. To this end, we have built a mathematical model of the major cell types that intervene in ICD, namely cancer cells, DCs, CD8+ and CD4+ T cells. Our model not only integrates intracellular mechanisms within each individual cell entity, but also incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell types. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type. With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, our model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response. All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and parameter sensitivity analyses.
| S-EPMC7690454 | biostudies-literature