Boolean Modeling Reveals the Necessity of Transcriptional Regulation for Bistability in PC12 Cell Differentiation

Extracellular Signal-Regulated Kinases: One Pathway, Multiple Fates

This comprehensive review delves into the multifaceted aspects of ERK signaling and the intricate mechanisms underlying distinct cellular fates. ERK1 and ERK2 (ERK) govern proliferation, transformation, epithelial-mesenchymal transition, differentiation, senescence, or cell death, contingent upon activation strength, duration, and context. The biochemical mechanisms underlying these outcomes are inadequately understood, shaped by signaling feedback and the spatial localization of ERK activation. Generally, ERK activation aligns with the Goldilocks principle in cell fate determination. Inadequate or excessive ERK activity hinders cell proliferation, while balanced activation promotes both cell proliferation and survival. Unraveling the intricacies of how the degree of ERK activation dictates cell fate requires deciphering mechanisms encompassing protein stability, transcription factors downstream of ERK, and the chromatin landscape.

Target Control of Asynchronous Boolean Networks

We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process

BACKGROUND

In cell signaling pathways, proteins interact with each other to determine cell fate in response to either cell-extrinsic (micro-environmental) or intrinsic cues. One of the well-studied pathways, the mitogen-activated protein kinase (MAPK) signaling pathway, regulates cell processes such as differentiation, proliferation, apoptosis, and survival in response to various micro-environmental stimuli in eukaryotes. Upon micro-environmental stimulus, receptors on the cell membrane become activated. Activated receptors initiate a cascade of protein activation in the MAPK pathway. This activation involves protein binding, creating scaffold proteins, which are known to facilitate effective MAPK signaling transduction.

RESULTS

This paper presents a novel mathematical model of a cell signaling pathway coordinated by protein scaffolding. The model is based on the extended Boolean network approach with stochastic processes. Protein production or decay in a cell was modeled considering the stochastic process, whereas the protein-protein interactions were modeled based on the extended Boolean network approach. Our model fills a gap in the binary set applied to previous models. The model simultaneously considers the stochastic process directly. Using the model, we simulated a simplified mitogen-activated protein kinase (MAPK) signaling pathway upon stimulation of both a single receptor at the initial time and multiple receptors at several time points. Our simulations showed that the signal is amplified as it travels down to the pathway from the receptor, generating substantially amplified downstream ERK activity. The noise generated by the stochastic process of protein self-activity in the model was also amplified as the signaling propagated through the pathway.

CONCLUSIONS

The signaling transduction in a simplified MAPK signaling pathway could be explained by a mathematical model based on the extended Boolean network model with a stochastic process. The model simulations demonstrated signaling amplifications when it travels downstream, which was already observed in experimental settings. We also highlight the importance of stochastic activity in regulating protein inactivation.

Regulation of nuclear actin levels and MRTF/SRF target gene expression during PC6.3 cell differentiation

Actin has important functions in both cytoplasm and nucleus of the cell, with active nuclear transport mechanisms maintaining the cellular actin balance. Nuclear actin levels are subject to regulation during many cellular processes from cell differentiation to cancer. Here we show that nuclear actin levels increase upon differentiation of PC6.3 cells towards neuron-like cells. Photobleaching experiments demonstrate that this increase is due to decreased nuclear export of actin during cell differentiation. Increased nuclear actin levels lead to decreased nuclear localization of MRTF-A, a well-established transcription cofactor of SRF. In line with MRTF-A localization, transcriptomics analysis reveals that MRTF/SRF target gene expression is first transiently activated, but then substantially downregulated during PC6.3 cell differentiation. This study therefore describes a novel cellular context, where regulation of nuclear actin is utilized to tune MRTF/SRF target gene expression during cell differentiation.

ERK-dependent induction of the immediate-early gene Egr1 and the late gene Gpr50 contribute to two distinct phases of PACAP Gs-GPCR signaling for neuritogenesis

Gs-coupled GPCR-stimulated neuritogenesis in PC12 and NS-1 - cells depends on activation of the MAP kinase ERK. Here, we examine changes in ERK activation (phosphorylation), and the time course of ERK-dependent gene induction, to seek transcriptional determinants for this process. Quenching of ERK activation by inhibition of MEK with U0126 at any time point for at least 24 h following addition of PACAP resulted in arrest of neurite formation. Changes in the transcriptome profile throughout this time period revealed at least two phases of gene induction: an early phase dominated by induction of immediate-early genes, and a later phase of gene induction after 4-6 h of exposure to PACAP with persistent elevation of phospho-ERK levels. Genes induced by PACAP in both phases consisted in those whose induction was dependent on ERK (i.e., blocked by U0126), and some whose induction was blocked by the protein kinase A inhibitor H89. ERK-dependent "late gene" transcripts included Gpr50, implicated earlier in facilitation of NGF-induced neurite formation in NS-1 cells. Gpr50 induction by PACAP, but not NGF, was dependent on the guanine nucleotide exchange factor RapGEF2, which has been shown to be required for PACAP-induced neuritogenesis in NS-1 cells. Expression of a Gpr50-directed shRNA lowered basal levels of Gpr50 mRNA and attenuated Gpr50 mRNA and GPR50 protein induction by PACAP, with a corresponding attenuation of PACAP-induced neuritogenesis. Gs-GPCR-stimulated neuritogenesis first requires immediate-early gene induction, including that of Egr1 (Zif268/NGF1A/Krox24) as previously reported. This early phase of gene induction, however, is insufficient to maintain the neuritogenic process without ERK-dependent induction of additional late genes, including Gpr50, upon continuous exposure to neurotrophic neuropeptide. Early (Egr1) and late (Gpr50) gene induction by NGF, like that for PACAP, was inhibited by U0126, but was independent of RapGEF2, confirming distinct modes of ERK activation by Gs-coupled GPCRs and neurotrophic tyrosine receptor kinases, converging on a final common ERK-dependent signaling pathway for neuritogenesis.

Towards Optimal Decomposition of Boolean Networks

In recent years, great efforts have been made to analyze biological systems to understand the long-run behaviors. As a well-established formalism for modelling real-life biological systems, Boolean networks (BNs) allow their representation and analysis using formal reasoning and tools. Most biological systems are robust-they can withstand the loss of links and cope with external environmental perturbations. Hence, the BNs used to model such systems are necessarily large and dense, and yet modular. However, existing analysis methods only work well on networks of moderate size. Thus, there is a great need for efficient methods that can handle large-scale BNs and for doing so it is inevitable to exploit both the structural and dynamic properties of the networks. In this paper, we propose a method towards the optimal decomposition of BNs to balance the relation between the structure and dynamics of a network. We show that our method can greatly improve the existing decomposition-based attractor detection by analyzing a number of large real-life biological networks.

Lac Operon Boolean Models: Dynamical Robustness and Alternative Improvements

In Veliz-Cuba and Stigler 2011, Boolean models were proposed for the lac operon in Escherichia coli capable of reproducing the operon being OFF, ON and bistable for three (low, medium and high) and two (low and high) parameters, representing the concentration ranges of lactose and glucose, respectively. Of these 6 possible combinations of parameters, 5 produce results that match with the biological experiments of Ozbudak et al., 2004. In the remaining one, the models predict the operon being OFF while biological experiments show a bistable behavior. In this paper, we first explore the robustness of two such models in the sense of how much its attractors change against any deterministic update schedule. We prove mathematically that, in cases where there is no bistability, all the dynamics in both models lack limit cycles while, when bistability appears, one model presents 30% of its dynamics with limit cycles while the other only 23%. Secondly, we propose two alternative improvements consisting of biologically supported modifications; one in which both models match with Ozbudak et al., 2004 in all 6 combinations of parameters and, the other one, where we increase the number of parameters to 9, matching in all these cases with the biological experiments of Ozbudak et al., 2004.

FAM72, Glioblastoma Multiforme (GBM) and Beyond

Simple Summary

Glioblastoma multiforme (GBM) is a serious and aggressive cancer disease that has not allowed scientists to rest for decades. In this review, we consider the new gene pair |-SRGAP2–FAM72-| and discuss its role in the cell cycle and the possibility of defining new therapeutic approaches for the treatment of GBM and other cancers via this gene pair |-SRGAP2–FAM72-|.

Abstract

Neural stem cells (NSCs) offer great potential for regenerative medicine due to their excellent ability to differentiate into various specialized cell types of the brain. In the central nervous system (CNS), NSC renewal and differentiation are under strict control by the regulation of the pivotal SLIT-ROBO Rho GTPase activating protein 2 (SRGAP2)—Family with sequence similarity 72 (FAM72) master gene (i.e., |-SRGAP2–FAM72-|) via a divergent gene transcription activation mechanism. If the gene transcription control unit (i.e., the intergenic region of the two sub-gene units, SRGAP2 and FAM72) gets out of control, NSCs may transform into cancer stem cells and generate brain tumor cells responsible for brain cancer such as glioblastoma multiforme (GBM). Here, we discuss the surveillance of this |-SRGAP2–FAM72-| master gene and its role in GBM, and also in light of FAM72 for diagnosing various types of cancers outside of the CNS.

An Efficient Approach Towards the Source-Target Control of Boolean Networks

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimized if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach.

SIRT2 suppresses expression of inflammatory factors via Hsp90-glucocorticoid receptor signalling

SIRT2 is a NAD⁺‐dependent deacetylase that deacetylates a diverse array of protein substrates and is involved in many cellular processes, including regulation of inflammation. However, its precise role in the inflammatory process has not completely been elucidated. Here, we identify heat‐shock protein 90α (Hsp90α) as novel substrate of SIRT2. Functional investigation suggests that Hsp90 is deacetylated by SIRT2, such that overexpression and knock‐down of SIRT2 altered the acetylation level of Hsp90. This subsequently resulted in disassociation of Hsp90 with glucocorticoid receptor (GR), and translocation of GR to the nucleus. This observation was further confirmed by glucocorticoid response element (GRE)‐driven reporter assay. Nuclear translocation of GR induced by SIRT2 overexpression repressed the expression of inflammatory cytokines, which were even more prominent under lipopolysaccharide (LPS) stimulation. Conversely, SIRT2 knock‐down resulted in the up‐regulation of cytokine expression. Mutation analysis indicated that deacetylation of Hsp90 at K294 is critical for SIRT2‐mediated regulation of cytokine expression. These data suggest that SIRT2 reduces the extent of LPS‐induced inflammation by suppressing the expression of inflammatory factors via SIRT2‐Hsp90‐GR axis.

Algorithms for the Sequential Reprogramming of Boolean Networks

Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply to the initial state. However, it has been shown, and is proved in this article, that waiting between perturbations so that the update dynamics of the system prepares the ground, allows for new reprogramming strategies. To identify such sequential perturbations, we consider a qualitative model of regulatory networks, and rely on Binary Decision Diagrams to model their dynamics and the putative perturbations. Our method establishes a set identification of sequential perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply an implementation for temporary perturbations on models from the literature, illustrating that we are able to derive sequential perturbations to achieve trans-differentiation.

Controlling large Boolean networks with single-step perturbations

Motivation

The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another ‘desired’ attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks.

Results

We develop a ‘divide-and-conquer’ approach by decomposing the network into smaller partitions, computing the minimal control on the projection of the attractors to these partitions and then composing the results to obtain Cmin for the whole network. We implement our method and test it on various real-life biological networks to demonstrate its applicability and efficiency.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Novel Divergent Gene Transcription Paradigm-the Decisive, Brain-Specific, Neural |-Srgap2-Fam72a-| Master Gene Paradigm

Brain development and repair largely depend on neural stem cells (NSCs). Here, we suggest that two genes, i.e., Srgap2 (SLIT-ROBO Rho GTPase-activating protein 2) and Fam72a (family with sequence similarity to 72, member A), constitute a single, NSC-specific, |-Srgap2-Fam72a-| master gene pair co-existing in reciprocal functional dependency. This gene pair has a dual, commonly used, intergenic region (IGR) promotor, which is a prerequisite in controlling human brain plasticity. We applied fluorescence cellular microscopy and fluorescence-activated cell sorting (FACS) to assess rat |-Srgap2-Fam72a-| master gene IGR promotor activity upon stimulation with two contrary growth factors: nerve growth factor (Ngf, a differentiation growth factor) and epidermal growth factor (Egf, a mitotic growth factor). We found that Ngf and Egf acted on the same IGR gene promotor element of the |-Srgap2-Fam72a-| master gene to mediate cell differentiation and proliferation, respectively. Ngf mediated Srgap2 expression and neuronal survival and differentiation while Egf activated Fam72a transcription and cell proliferation. Our data provide new insights into the specific regulation of the |-Srgap2-Fam72a-| master gene with its dual IGR promotor that controls two reverse-oriented functional-dependent genes located on opposite DNA strands. This structure represents a novel paradigm for controlling transcription of divergent genes in regulating NSC gene expression. This paradigm may allow for novel therapeutic approaches to restore or improve higher cognitive functions and cure cancers.

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic.

Specific role of RhoC in tumor invasion and metastasis

Rho GTPases are regulators of many cellular functions and are often dysregulated in cancer. However, the precise role of Rho proteins for tumor development is not well understood. In breast cancer, overexpression of RhoC is linked with poor prognosis. Here, we aim to compare the function of RhoC and its homolog family member RhoA in breast cancer progression. We established stable breast epithelial cell lines with inducible expression of RhoA and RhoC, respectively. Moreover, we made use of Rho-activating bacterial toxins (Cytotoxic Necrotizing Factors) to stimulate the endogenous pool of Rho GTPases in benign breast epithelial cells and simultaneously knocked down specific Rho proteins. Whereas activation of Rho GTPases was sufficient to induce an invasive phenotype in three-dimensional culture systems, overexpression of RhoA or RhoC were not. However, RhoC but not RhoA was required for invasion, whereas RhoA and RhoC equally regulated proliferation. We further identified downstream target genes of RhoC involved in invasion and identified PTGS2 (COX-2) being preferentially upregulated by RhoC. Consistently, the COX-2 inhibitor Celecoxib blocked the invasive phenotype induced by the Rho-activating toxins.

An immediate-late gene expression module decodes ERK signal duration

The RAF‐MEK‐ERK signalling pathway controls fundamental, often opposing cellular processes such as proliferation and apoptosis. Signal duration has been identified to play a decisive role in these cell fate decisions. However, it remains unclear how the different early and late responding gene expression modules can discriminate short and long signals. We obtained both protein phosphorylation and gene expression time course data from HEK293 cells carrying an inducible construct of the proto‐oncogene RAF. By mathematical modelling, we identified a new gene expression module of immediate–late genes (ILGs) distinct in gene expression dynamics and function. We find that mRNA longevity enables these ILGs to respond late and thus translate ERK signal duration into response amplitude. Despite their late response, their GC‐rich promoter structure suggested and metabolic labelling with 4SU confirmed that transcription of ILGs is induced immediately. A comparative analysis shows that the principle of duration decoding is conserved in PC12 cells and MCF7 cells, two paradigm cell systems for ERK signal duration. Altogether, our findings suggest that ILGs function as a gene expression module to decode ERK signal duration.

The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells

The intricate dynamic control and plasticity of RAS to ERK mitogenic, survival and apoptotic signalling has mystified researches for more than 30 years. Therapeutics targeting the oncogenic aberrations within this pathway often yield unsatisfactory, even undesired results, as in the case of paradoxical ERK activation in response to RAF inhibition. A direct approach of inhibiting single oncogenic proteins misses the dynamic network context governing the network signal processing. In this review, we discuss the signalling behaviour of RAS and RAF proteins in normal and in cancer cells, and the emerging systems-level properties of the RAS-to-ERK signalling network. We argue that to understand the dynamic complexities of this control system, mathematical models including mechanistic detail are required. Looking into the future, these dynamic models will build the foundation upon which more effective, rational approaches to cancer therapy will be developed.