Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851.

Introduction: Pseudomonas aeruginosa infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, P. aeruginosa is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil. Methods: One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems. Results and discussion: Therefore, this research consists of building a Boolean model of the gene regulatory network of P. aeruginosa CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.

The flowering transition pathways converge into a complex gene regulatory network that underlies the phase changes of the shoot apical meristem in Arabidopsis thaliana.

Post-embryonic plant development is characterized by a period of vegetative growth during which a combination of intrinsic and extrinsic signals triggers the transition to the reproductive phase. To understand how different flowering inducing and repressing signals are associated with phase transitions of the Shoot Apical Meristem (SAM), we incorporated available data into a dynamic gene regulatory network model for Arabidopsis thaliana. This Flowering Transition Gene Regulatory Network (FT-GRN) formally constitutes a dynamic system-level mechanism based on more than three decades of experimental data on flowering. We provide novel experimental data on the regulatory interactions of one of its twenty-three components: a MADS-box transcription factor XAANTAL2 (XAL2). These data complement the information regarding flowering transition under short days and provides an example of the type of questions that can be addressed by the FT-GRN. The resulting FT-GRN is highly connected and integrates developmental, hormonal, and environmental signals that affect developmental transitions at the SAM. The FT-GRN is a dynamic multi-stable Boolean system, with 223 possible initial states, yet it converges into only 32 attractors. The latter are coherent with the expression profiles of the FT-GRN components that have been experimentally described for the developmental stages of the SAM. Furthermore, the attractors are also highly robust to initial states and to simulated perturbations of the interaction functions. The model recovered the meristem phenotypes of previously described single mutants. We also analyzed the attractors landscape that emerges from the postulated FT-GRN, uncovering which set of signals or components are critical for reproductive competence and the time-order transitions observed in the SAM. Finally, in the context of such GRN, the role of XAL2 under short-day conditions could be understood. Therefore, this model constitutes a robust biological module and the first multi-stable, dynamical systems biology mechanism that integrates the genetic flowering pathways to explain SAM phase transitions.

Dynamical Analysis of a Boolean Network Model of the Oncogene Role of lncRNA ANRIL and lncRNA UFC1 in Non-Small Cell Lung Cancer.

Long non-coding RNA (lncRNA) such as ANRIL and UFC1 have been verified as oncogenic genes in non-small cell lung cancer (NSCLC). It is well known that the tumor suppressor microRNA-34a (miR-34a) is downregulated in NSCLC. Furthermore, miR-34a induces senescence and apoptosis in breast, glioma, cervical cancer including NSCLC by targeting Myc. Recent evidence suggests that these two lncRNAs act as a miR-34a sponge in corresponding cancers. However, the biological functions between these two non-coding RNAs (ncRNAs) have not yet been studied in NSCLC. Therefore, we present a Boolean model to analyze the gene regulation between these two ncRNAs in NSCLC. We compared our model to several experimental studies involving gain- or loss-of-function genes in NSCLC cells and achieved an excellent agreement. Additionally, we predict three positive circuits involving miR-34a/E2F1/ANRIL, miR-34a/E2F1/UFC1, and miR-34a/Myc/ANRIL. Our circuit- perturbation analysis shows that these circuits are important for regulating cell-fate decisions such as senescence and apoptosis. Thus, our Boolean network permits an explicit cell-fate mechanism associated with NSCLC. Therefore, our results support that ANRIL and/or UFC1 is an attractive target for drug development in tumor growth and aggressive proliferation of NSCLC, and that a valuable outcome can be achieved through the miRNA-34a/Myc pathway.

CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19.

In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-ß and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-ß and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-γ pathway, or the inhibition of TGF-ß or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects.

Towards DNA-damage induced autophagy: A Boolean model of p53-induced cell fate mechanisms.

How a cell determines a given phenotype upon damaged DNA is an open problem. Cell fate decisions happen at cell cycle checkpoints and it is becoming clearer that the p53 pathway is a major regulator of cell fate decisions involving apoptosis or senescence upon DNA damage, especially at G1/S. However, recent results suggest that this pathway is also involved in autophagy induction upon DNA damage. To our knowledge, in this work we propose the first model of the DNA damage-induced G1/S checkpoint contemplating the decision between three phenotypes: apoptosis, senescence, and autophagy. The Boolean model is proposed based on experiments with U87 glioblastoma cells using the transfection of miR-16 that can induce a DNA damage response. The wild-type case of the model shows that DNA damage induces the checkpoint and the coexistence of the three phenotypes (tristable dynamics), each with a different probability. We also predict that the positive feedback involving ATM, miR-16, and Wip1 has an influence on the tristable state. The model predictions were compared to experiments of gain and loss of function in other three different cell lines (MCF-7, A549, and U2OS) presenting agreement. For p53-deficient cell lines such as HeLa, H1299, and PC-3, our model contemplates the experimental observation that the alternative AMPK pathway can compensate this deficiency. We conclude that at the G1/S checkpoint the p53 pathway (or, in its absence, the AMPK pathway) can regulate the induction of different phenotypes in a stochastic manner in the U87 cell line and others.

An Integrative Network Modeling Approach to T CD4 Cell Activation.

The adaptive immune response is initiated by the interaction of the T cell antigen receptor/CD3 complex (TCR) with a cognate peptide bound to a MHC molecule. This interaction, along with the activity of co-stimulatory molecules and cytokines in the microenvironment, enables cells to proliferate and produce soluble factors that stimulate other branches of the immune response for inactivation of infectious agents. The intracellular activation signals are reinforced, amplified and diversified by a complex network of biochemical interactions, and includes the activity of molecules that modulate the activation process and stimulate the metabolic changes necessary for fulfilling the cell energy demands. We present an approach to the analysis of the main early signaling events of T cell activation by proposing a concise 46-node hybrid Boolean model of the main steps of TCR and CD28 downstream signaling, encompassing the activity of the anergy factor Ndrg1, modulation of activation by CTLA-4, and the activity of the nutrient sensor AMPK as intrinsic players of the activation process. The model generates stable states that reflect the overcoming of activation signals and induction of anergy by the expression of Ndrg1 in the absence of co-stimulation. The model also includes the induction of CTLA-4 upon activation and its competition with CD28 for binding to the co-stimulatory CD80/86 molecules, leading to stable states that reflect the activation arrest. Furthermore, the model integrates the activity of AMPK to the general pathways driving differentiation to functional cell subsets (Th1, Th2, Th17, and Treg). Thus, the network topology incorporates basic mechanism associated to activation, regulation and induction of effector cell phenotypes. The model puts forth a conceptual framework for the integration of functionally relevant processes in the analysis of the T CD4 cell function.

ATM/miR-34a-5p axis regulates a p21-dependent senescence-apoptosis switch in non-small cell lung cancer: a Boolean model of G1/S checkpoint regulation.

MicroRNA-34a-5p regulates the G1/S checkpoint in non-small cell lung cancer (NSCLC) cells. Forced expression of miR-34a-5p enhances p21 expression and promotes cellular senescence, whereas knockout of miR-34a-5p decreases senescence and increases apoptosis. This suggests that p21 is the main effector of a senescence-apoptosis switch in NSCLC cells; however, the molecular mechanisms controlling this switch are unclear. In this work, we propose a Boolean model of G1/S checkpoint regulation, contemplating the regulatory influences of p21 by miR-34a-5p. The predicted probabilities of our model are in excellent agreement with experimental data. Our model supports that p21 is the main effector of a senescence/apoptosis switch and that the disruption of the positive feedback involving ATM, miR-34a-5p, and the histone deacetylase HDAC1 abrogates senescence.

Towards the contribution of the p38MAPK pathway to the dual role of TGFß in cancer: A boolean model approach.

The transforming growth factor-beta (TGF-ß) pathway is involved in the regulation of cell growth and differentiation. In normal cells or in the early stages of cancer, this pathway can control proliferation stimuli by inducing cell cycle arrest or apoptosis (through the MAP-kinase protein p38MAPK), while in late stages it seems to act as a tumor promoter. This feature is known as the TGF-ß dual role in cancer and it is not completely explained. This seems to arise through the accumulation of mutations in cancer development that affect the normal function of these pathways. In this work we propose a Boolean model of the crosstalk between the TGF-ß, p38 MAPK and cell cycle checkpoint pathways which qualitatively describes this dual behavior. The model shows that for the wild type case, TGF-ß acts as tumor supressor by inducing cell cycle arrest or apoptosis, as expected. However, the loss of function (LoF) of its two signaling proteins: SMAD2 and SMAD3 has immortalization effects due to the activation of the PI3K/AKT pathway that contributes to inhibit apoptosis. In silico mutations of the model elements were compared with cell phenotypes in experiments presenting agreement. In addition, we performed a series of double gene perturbations (that simulate random deleterious mutations) to determine the main regulators of the network. The results suggest that SMAD2/3 and p38MAPK are key players in processing the network input. In addition, when the LoF of SMAD2/3 is combined with the LoF of p38MAPK and p53, cell cycle arrest is completely abrogated. In conclusion, the model allows to visualize, through in silico mutations, the dual role of TGF-ß: for the wild-type case TGF-ß is able to block proliferation, however deleterious mutations can impair cell cycle arrest promoting cellular proliferation.

Dynamical Analysis of the Regulatory Network Controlling Natural Killer Cells Differentiation.

Many disease fighting strategies have focused on the generation of NK cells, since they constitute the main immune barrier against cancer and intracellular pathogens such as viruses. Therefore, a predictive model for the development of NK cells would constitute a useful tool to test several hypotheses regarding the production of these cells during both physiological and pathological conditions. Here, we present a boolean network model that reproduces experimental results reported on the literature regarding the progressive stages of the development of NK cells in wild-type and mutant backgrounds. The model allows for the simulation of different conditions, including extracellular micro-environment as well as the simulation of genetic alterations. It also describes how NK cell differentiation depends on a molecular regulatory network that controls the specification of lymphoid lineages, such as T and B cells, which share a common progenitor with NKs. Furthermore, the study shows that the structure of the regulatory network strongly determines the stability of the expression patterns against perturbations.