Survival and apoptotic pathways initiated by TNF-alpha: modeling and predictions

Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response

Cell-to-cell variability during TNFα stimulated Tumor Necrosis Factor Receptor 1 (TNFR1) signaling can lead to single-cell level pro-survival and apoptotic responses. This variability stems from the heterogeneity in signal flow through intracellular signaling entities that regulate the balance between these two phenotypes. Using systematic Boolean dynamic modeling of a TNFR1 signaling network, we demonstrate that the signal flow path variability can be modulated to enable cells favour apoptosis. We developed a computationally efficient approach "Boolean Modeling based Prediction of Steady-state probability of Phenotype Reachability (BM-ProSPR)" to accurately predict the network's ability to settle into different phenotypes. Model analysis juxtaposed with the experimental observations revealed that NFκB and PI3K transient responses guide the XIAP behaviour to coordinate the crucial dynamic cross-talk between the pro-survival and apoptotic arms at the single-cell level. Model predicted the experimental observations that ~31% apoptosis increase can be achieved by arresting Comp1 - IKK* activity which regulates the NFκB and PI3K dynamics. Arresting Comp1 - IKK* activity causes signal flow path re-wiring towards apoptosis without significantly compromising NFκB levels, which govern adequate cell survival. Priming an ensemble of cancerous cells with inhibitors targeting the specific interaction involving Comp1 and IKK* prior to TNFα exposure could enable driving them towards apoptosis.

Bistability regulates TNFR2-mediated survival and death of T-regulatory cells

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.

Advances in RIPK1 kinase inhibitors

Programmed necrosis is a new modulated cell death mode with necrotizing morphological characteristics. Receptor interacting protein 1 (RIPK1) is a critical mediator of the programmed necrosis pathway that is involved in stroke, myocardial infarction, fatal systemic inflammatory response syndrome, Alzheimer's disease, and malignancy. At present, the reported inhibitors are divided into four categories. The first category is the type I ATP-competitive kinase inhibitors that targets the area occupied by the ATP adenylate ring; The second category is type Ⅱ ATP competitive kinase inhibitors targeting the DLG-out conformation of RIPK1; The third category is type Ⅲ kinase inhibitors that compete for binding to allosteric sites near ATP pockets; The last category is others. This paper reviews the structure, biological function, and recent research progress of receptor interaction protein-1 kinase inhibitors.

Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis

The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model.

It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer's disease (AD), Parkinson's disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model's response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.

Role of inflammation and oxidative stress in chemotherapy-induced neurotoxicity

Chemotherapeutic agents may adversely affect the nervous system, including the neural precursor cells as well as the white matter. Although the mechanisms are not completely understood, several hypotheses connecting inflammation and oxidative stress with neurotoxicity are now emerging. The proposed mechanisms differ depending on the class of drug. For example, toxicity due to cisplatin occurs due to activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), which alters hippocampal long-term potentiation. Free radical injury is also involved in the cisplatin-mediated neurotoxicity as dysregulation of nuclear factor erythroid 2-related factor 2 (Nrf2) has been seen which protects against the free radical injury by regulating glutathione S-transferases and hemeoxygenase-1 (HO-1). Thus, correcting the imbalance between NF-κB and Nrf2/HO-1 pathways may alleviate cisplatin-induced neurotoxicity. With newer agents like bortezomib, peripheral neuropathy occurs due to up-regulation of TNF-α and IL-6 in the sensory neurons. Superoxide dismutase dysregulation is also involved in bortezomib-induced neuropathy. This article reviews the available literature on inflammation and oxidative stress in neurotoxicity caused by various classes of chemotherapeutic agents. It covers the conventional medicines like platinum compounds, vinca alkaloids, and methotrexate, as well as the newer therapeutic agents like immunomodulators and immune checkpoint inhibitors. A better understanding of the pathophysiology will lead to further advancement in strategies for management of chemotherapy-induced neurotoxicity.

The Mechanism of Lung and Intestinal Injury in Acute Pancreatitis: A Review

Acute pancreatitis (AP), as a common cause of clinical acute abdomen, often leads to multi-organ damage. In the process of severe AP, the lungs and intestines are the most easily affected organs aside the pancreas. These organ damages occur in succession. Notably, lung and intestinal injuries are closely linked. Damage to ML, which transports immune cells, intestinal fluid, chyle, and toxic components (including toxins, trypsin, and activated cytokines to the systemic circulation in AP) may be connected to AP. This process can lead to the pathological changes of hyperosmotic edema of the lung, an increase in alveolar fluid level, destruction of the intestinal mucosal structure, and impairment of intestinal mucosal permeability. The underlying mechanisms of the correlation between lung and intestinal injuries are inflammatory response, oxidative stress, and endocrine hormone secretion disorders. The main signaling pathways of lung and intestinal injuries are TNF-α, HMGB1-mediated inflammation amplification effect of NF-κB signal pathway, Nrf2/ARE oxidative stress response signaling pathway, and IL-6-mediated JAK2/STAT3 signaling pathway. These pathways exert anti-inflammatory response and anti-oxidative stress, inhibit cell proliferation, and promote apoptosis. The interaction is consistent with the traditional Chinese medicine theory of the lung being connected with the large intestine (fei yu da chang xiang biao li in Chinese). This review sought to explore intersecting mechanisms of lung and intestinal injuries in AP to develop new treatment strategies.

RNPS1 inhibits excessive tumor necrosis factor/tumor necrosis factor receptor signaling to support hematopoiesis in mice

SignificanceMessenger RNA (mRNA) splicing is fundamental to protein expression in mammals. Homozygous deletion of single protein components of the splicing machinery or its regulatory factors is embryonic lethal. However, through forward genetic screening in mice, we identified a viable hypomorphic missense mutation of the splicing regulator RNPS1. Homozygous mutant mice displayed altered immune cell development due to excessive tumor necrosis factor (TNF)-dependent immune cell apoptosis. Splicing was impaired in CD8⁺ T cells and hematopoietic stem cells from RNPS1 mutant mice. TNF knockout rescued hematopoiesis and dramatically reduced splicing defects in RNPS1 hematopoietic cells, demonstrating a surprising link between elevated TNF and defects in splicing caused by RNPS1 deficiency.

Dichotomous Role of Tumor Necrosis Factor in Pulmonary Barrier Function and Alveolar Fluid Clearance

Alveolar-capillary leak is a hallmark of the acute respiratory distress syndrome (ARDS), a potentially lethal complication of severe sepsis, trauma and pneumonia, including COVID-19. Apart from barrier dysfunction, ARDS is characterized by hyper-inflammation and impaired alveolar fluid clearance (AFC), which foster the development of pulmonary permeability edema and hamper gas exchange. Tumor Necrosis Factor (TNF) is an evolutionarily conserved pleiotropic cytokine, involved in host immune defense against pathogens and cancer. TNF exists in both membrane-bound and soluble form and its mainly -but not exclusively- pro-inflammatory and cytolytic actions are mediated by partially overlapping TNFR1 and TNFR2 binding sites situated at the interface between neighboring subunits in the homo-trimer. Whereas TNFR1 signaling can mediate hyper-inflammation and impaired barrier function and AFC in the lungs, ligand stimulation of TNFR2 can protect from ventilation-induced lung injury. Spatially distinct from the TNFR binding sites, TNF harbors within its structure a lectin-like domain that rather protects lung function in ARDS. The lectin-like domain of TNF -mimicked by the 17 residue TIP peptide- represents a physiological mediator of alveolar-capillary barrier protection. and increases AFC in both hydrostatic and permeability pulmonary edema animal models. The TIP peptide directly activates the epithelial sodium channel (ENaC) -a key mediator of fluid and blood pressure control- upon binding to its α subunit, which is also a part of the non-selective cation channel (NSC). Activity of the lectin-like domain of TNF is preserved in complexes between TNF and its soluble TNFRs and can be physiologically relevant in pneumonia. Antibody- and soluble TNFR-based therapeutic strategies show considerable success in diseases such as rheumatoid arthritis, psoriasis and inflammatory bowel disease, but their chronic use can increase susceptibility to infection. Since the lectin-like domain of TNF does not interfere with TNF's anti-bacterial actions, while exerting protective actions in the alveolar-capillary compartments, it is currently evaluated in clinical trials in ARDS and COVID-19. A more comprehensive knowledge of the precise role of the TNFR binding sites versus the lectin-like domain of TNF in lung injury, tissue hypoxia, repair and remodeling may foster the development of novel therapeutics for ARDS.

Multiscale computational modeling of cancer growth using features derived from microCT images

Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

Stability Analysis of a Signaling Circuit with Dual Species of GTPase Switches

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, function, vesicular transport between organelles, and signal transduction. These hydrolase enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we propose a model for a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. We develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through coupling and feedback loops. We provide explicit formulae for the steady states of the system and perform classical local stability analysis to systematically investigate the role of the different connections between the GTPase switches. Interestingly, a coupling of the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. Moreover, when a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the coupled system, two other locally stable states emerged. In both states the tGTPase is inactivated and active tGTPase concentrations are low. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states that can be parametrized by a range of inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady-state behaviors and shed light on how such coupling may impact signaling mechanisms in eukaryotic cells.

MODELING INTER-KINGDOM REGULATION OF INFLAMMATORY SIGNALING IN HUMAN INTESTINAL EPITHELIAL CELLS

The human gastrointestinal (GI) tract is colonized by a highly diverse and complex microbial community (i.e., microbiota). The microbiota plays an important role in the development of the immune system, specifically mediating inflammatory responses, however the exact mechanisms are poorly understood. We have developed a mathematical model describing the effect of indole on host inflammatory signaling in HCT-8 human intestinal epithelial cells. In this model, indole modulates transcription factor nuclear factor κ B (NF-κB) and produces the chemokine interleukin-8 (IL-8) through the activation of the aryl hydrocarbon receptor (AhR). Phosphorylated NF-κB exhibits dose and time-dependent responses to indole concentrations and IL-8 production shows a significant down-regulation for 0.1 ng/mL TNF-α stimulation. The model shows agreeable simulation results with the experimental data for IL-8 secretion and normalized NF-κB values. Our results suggest that microbial metabolites such as indole can modulate inflammatory signaling in HTC-8 cells through receptor-mediated processes.

Controlled release of Mitomycin C from modified cellulose based thermo-gel prevents post-operative de novo peritoneal adhesion

The complications from surgery associated peritoneal adhesion can be alleviated by combination of physical isolation and pharmaceutical treatment. This work aims to develop thermo-sensitive hydrogel barrier by combining mitomycin C (MMC) with modified tempo oxidized nanocellulose (cTOCN) through EDC/NHS-chemical conjugation followed by integration with methyl cellulose (MC). The MMC was successfully combined with cTOCN and ensured controlled release of MMC from hydrogel throughout 14 days. Amount of MC (1.5, 2.5, 3.5% w/v) was proportional to gelation time and inversely proportional to degradation of hydrogel. The optimized hydrogel (C2.5T1M0.2) needed only 30 s for thermoreversible sol-gel (4℃-37℃) phenomenon and did not show in vitro fibroblast cells toxicity as well as ensured complete adhesion prevention efficacy, reperitonealization in rat side wall-cecal abrasion model. Overall, the developed C2.5T1M0.2 thermo-gel advances state-of-the-art in view of cytocompatibility, mechanical stability, optimum degradation, good injectability, sustain drug release from surgical sites, and satisfactory de novo anti-adhesion capacity.

Doxorubicin-induced neurotoxicity is associated with acute alterations in synaptic plasticity, apoptosis, and lipid peroxidation

Cognitive deficits are commonly reported by patients following treatment with chemotherapeutic agents. Anthracycline-containing chemotherapy regimens are associated with cognitive impairment and reductions in neuronal connectivity in cancer survivors, and doxorubicin (Dox) is a commonly used anthracycline. Although it has been reported that Dox distribution to the central nervous system (CNS) is limited, considerable Dox concentrations are observed in the brain with co-administration of certain medications. Additionally, pro-inflammatory cytokines, which are overproduced in cancer or in response to chemotherapy, can reduce the integrity of the blood-brain barrier (BBB). Therefore, the aim of this study was to evaluate the acute neurotoxic effects of Dox on hippocampal neurons. In this study, we utilized a hippocampal cell line (H19-7/IGF-IR) along with rodent hippocampal slices to evaluate the acute neurotoxic effects of Dox. Hippocampal slices were used to measure long-term potentiation (LTP), and expression of proteins was determined by immunoblotting. Cellular assays for mitochondrial complex activity and lipid peroxidation were also utilized. We observed reduction in LTP in hippocampal slices with Dox. In addition, lipid peroxidation was increased as measured by thiobarbituric acid reactive substances content indicating oxidative stress. Caspase-3 expression was increased indicating an increased propensity for cell death. Finally, the phosphorylation of signaling molecules which modulate LTP including extracellular signal-regulated kinase 1/2 (ERK1/2), p38 mitogen-activated protein kinase, and Akt were increased. This data indicates that acute Dox exposure dose-dependently impairs synaptic processes associated with hippocampal neurotransmission, induces apoptosis, and increases lipid peroxidation leading to neurotoxicity.

Downregulation of TNF-α/TNF-R1 Signals by AT-Lipoxin A4 May Be a Significant Mechanism of Attenuation in SAP-Associated Lung Injury

Our previous studies verified the potent anti-inflammatory effects against severe acute pancreatitis (SAP) of AT-Lipoxin A4 and their analogues. However, the anti-inflammatory effects of AT-Lipoxin A4 on SAP-associated lung injury are not thoroughly known. We used western blot, polymerase chain reaction (PCR), and immunofluorescence to investigate the downregulation of TNF-α signals in cellular and animal models of SAP-associated lung injury following AT-Lipoxin A4 intervention. In vitro, we found that AT-Lipoxin A4 markedly suppressed protein expression in TNF-α signals in human pulmonary microvascular endothelial cell, such as tumor necrosis factor receptor-associated factor 2 (TRAF2), TNF-R1-associated death domain (TRADD), receptor-interacting protein (RIP), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin. Moreover, AT-Lipoxin A4 inhibited downstream signals activated by TNF-α, including NF-κB/p65, JNK/MAPK, and ERK/MAPK. In vivo, AT-Lipoxin A4 significantly decreased pathological scores of the pancreas and lungs and the serum levels of IL-6 and TNF-α. Immunofluorescence, western blotting, and real-time PCR assay showed that AT-Lipoxin A4 significantly attenuated the expression of TNF-R1, TRADD, TRAF2, and RIP in the lungs of SAP rats. In addition, the activation of NF-κB was also downregulated by AT-Lipoxin A4 administration as compared with SAP rats. AT-Lipoxin A4 could inhibit the production of proinflammatory mediators and activation of TNF-α downstream signals such as NF-κB and MAPK. Downregulation of TNF-α signals by AT-Lipoxin A4 may be a significant mechanism in the attenuation of SAP-associated lung injury.

Quantitative single cell analysis uncovers the life/death decision in CD95 network

CD95/Fas/APO-1 is a member of the death receptor family that triggers apoptotic and anti-apoptotic responses in particular, NF-κB. These responses are characterized by a strong heterogeneity within a population of cells. To determine how the cell decides between life and death we developed a computational model supported by imaging flow cytometry analysis of CD95 signaling. Here we show that CD95 stimulation leads to the induction of caspase and NF-κB pathways simultaneously in one cell. The related life/death decision strictly depends on cell-to-cell variability in the formation of the death-inducing complex (DISC) on one side (extrinsic noise) vs. stochastic gene expression of the NF-κB pathway on the other side (intrinsic noise). Moreover, our analysis has uncovered that the stochasticity in apoptosis and NF-kB pathways leads not only to survival or death of a cell, but also causes a third type of response to CD95 stimulation that we termed ambivalent response. Cells in the ambivalent state can undergo cell death or survive which was subsequently validated by experiments. Taken together, we have uncovered how these two competing pathways control the fate of a cell, which in turn plays an important role for development of anti-cancer therapies.

Activation of death receptor (DR) family has been reported to activate both apoptotic as well as anti-apoptotic responses. Molecular mechanisms underlying the intricate details of this crosstalk have not been established yet. Here we show that these pathways are triggered simultaneously in one cell. Furthermore, using stochastic computational modeling we uncovered how an individual cell undergoes apoptosis, while other cells survive upon the same DR activation conditions. This was only possible by combination of computational modeling supported by experimental validation based on the state of the art single cell analysis. The latter included cutting edge technology of imaging flow cytometry, which combines microscopy and flow cytometry in one measurement circuit enabling quantitative analysis of endogenous cellular protein levels estimated from a large number of cells simultaneously. This allowed to shed the light on the question how a single cell possibly avoids apoptosis, which is a highly actual topic in the field of cancer research and development of efficient anti-cancer therapies.

Dietary soy isoflavones during pregnancy suppressed the immune function in male offspring albino rats

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Less attention has been paid to the immune effects of phytoestrogens during pregnancy on the first generation.

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Soy isoflavones fed to pregnant rats could modulate the immune response of the male offspring.

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Isoflavones reduced spleen and thymus weights in rats born to dams fed dietary soy.

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Soy isoflavones possibly mediated its effect through reduction of IFN-γ that interacts with the IL-12 production pathway.

Phytoestrogens have an impact on both animals and humans due to use of legumes in animal diets as well as the increase of vegetarian diets in some human populations. Phytoestrogens thought to have varieties of adverse effects, among which immune system was involved. The present study aimed to investigate the effect of prenatal exposure to dietary soy isoflavones on some immunological parameters in male albino rat offspring. The pregnant rats were divided to three groups (12/group). Control group (free soy isoflavones), low soy isoflavones group (6.5%) and high soy isoflavones group (26%). The male offspring cell-mediated immune response was determined using phytohemagglutinin (PHA) injection and the intumesce index which was calculated on postnatal day 50 (PND 50). At PND 50, blood samples were collected for interleukin 12 (IL-12), interferon γ (IFN-γ) and tumor necrosis factor α (TNF-α) determination. Spleen, thymus, and PHA injected footpads were fixed for histopathology. Intumesce index, IL-12, IFN-γ, spleen and thymus relative weights were significantly (P < 0.05) decreased in offspring born to dams fed low and high dietary soy isoflavones. In contrary, TNF-α was significantly (P < 0.05) increased in offspring born to dams fed high dietary soy isoflavones. Spleen of rats born to dams fed high dose of dietary soy isoflavones showed coagulative necrosis in white pulp. In conclusion, male offspring born to dams fed different levels of soy isoflavones showed marked immunosuppression after PHA stimulation. This effect was mediated through the reduced IFN-γ that interacts with the IL-12 production pathway.

Hybrid multiscale modeling and prediction of cancer cell behavior

Background

Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems.

Methods

In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters.

Results

Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable.

Conclusion

Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.

Prenatal Exposure to Soy Isoflavones Altered the Immunological Parameters in Female Rats

Information on the effects of phytoestrogens on animals has increased recently; however, there were only few studies on prenatal exposure on cellular immune response. Pregnant rats were assigned to 3 groups (12 rats per group), the first was fed control diet, the second was fed low-dose (6.5 g/100 g of diet) soy isoflavones, while the third was fed high-dose (26 g/100 g of diet) soy isoflavones. The female offspring cell-mediated immune response was determined using phytohemagglutinin (PHA) injection, and intumesce index was calculated on postnatal day 50. After 24 hours of PHA injection, blood samples were collected for tumor necrosis factor α, interferon γ (IFN-γ), and interleukin (IL)-12 determination. Spleen, thymus, and PHA-injected footpads were fixed for histopathology. Intumesce index was significantly (P < 0.05) reduced in rats' offspring born from dams fed low- and high-dietary soy isoflavones than that in control groups. Thymic relative weights in offspring of rats fed high-dietary soy isoflavones showed a significant (P < 0.05) decrease compared to that in the control group. Female offspring where low and high-dietary soy isoflavones were fed to their dams showed a significant (P < 0.05) decrease in IFN-γ and IL-12 than that in control ones. Spleen of rats born from dams fed high dose of dietary soy isoflavones showed lymphocytic depletion in white pulp. Taking together, it is clear that dietary soy isoflavones at prenatal period had immunosuppressive effect on female offspring after PHA stimulation. This effect was mediated through reduced IFN-γ that interplayed in IL-12 production pathway thus reducing its level.

Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation

NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.

Relationship between irradiation-induced neuro-inflammatory environments and impaired cognitive function in the developing brain of mice

PURPOSE

Radiation-induced brain injury (RIBI) is the most common side-effect after cranial radiation therapy (CRT). In the present study, the RIBI mice model was established and the changes in the expression of tumor necrosis factor alpha (TNF-α) and interleukin-1beta (IL-1β) mRNA, and the related signal pathways in the hippocampus of this model were investigated.

MATERIALS AND METHODS

10 Gy CRT or sham-irradiation was given to the three-week old mice. The water maze test was used to test the RIBI model in mice. The expression of pro-inflammatory cytokines was detected by real-time polymerase chain reaction (PCR) in vivo. The changes of microglial activation and neurogensis in the hippocampus were analyzed by immunofluorescence and immunohistochemistry. The cytoplasm to nuclei translocation of Nuclear factor kappa B (NF-κB), and the protein expressions of IkappaB-alpha (IκB-α), NF-κB essential modulator (NEMO), p53-induced protein with a death domain (PIDD), TNF-α and IL-1β were examined by Western blotting. A RIBI model was established by Morris water maze test 6 weeks after 10 Gy CRT in three-week old C57BL/6J mice.

RESULTS

The mRNA and protein expression levels of TNF-α and IL-1β reached the peak during the early phase after CRT. Increases in cytokine levels also were observed after irradiation of mouse BV-2 microglial cells. Neurogensis was significantly inhibited in the hippocampus with an increase of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) positive cells. The total number of microglia was decreased after CRT, but microglial activation was significantly increased. Western blotting revealed, in the RIBI mice, the expression of IκB-α was down-regulated, accompanied by the up-regulated expression of NEMO and regulated auto-proteolysis of PIDD. Also the NF-κB pathway activation was observed in BV-2 cells after irradiation.

CONCLUSIONS

CRT-induced pro-inflammatory cytokines release in the brain tissues and inhibition of neurogenesis in the hippocampus might be contributed by the microglial activation and play an important role in RIBI.

An agent-based model of the response to angioplasty and bare-metal stent deployment in an atherosclerotic blood vessel

PURPOSE

While animal models are widely used to investigate the development of restenosis in blood vessels following an intervention, computational models offer another means for investigating this phenomenon. A computational model of the response of a treated vessel would allow investigators to assess the effects of altering certain vessel- and stent-related variables. The authors aimed to develop a novel computational model of restenosis development following an angioplasty and bare-metal stent implantation in an atherosclerotic vessel using agent-based modeling techniques. The presented model is intended to demonstrate the body's response to the intervention and to explore how different vessel geometries or stent arrangements may affect restenosis development.

METHODS

The model was created on a two-dimensional grid space. It utilizes the post-procedural vessel lumen diameter and stent information as its input parameters. The simulation starting point of the model is an atherosclerotic vessel after an angioplasty and stent implantation procedure. The model subsequently generates the final lumen diameter, percent change in lumen cross-sectional area, time to lumen diameter stabilization, and local concentrations of inflammatory cytokines upon simulation completion. Simulation results were directly compared with the results from serial imaging studies and cytokine levels studies in atherosclerotic patients from the relevant literature.

RESULTS

The final lumen diameter results were all within one standard deviation of the mean lumen diameters reported in the comparison studies. The overlapping-stent simulations yielded results that matched published trends. The cytokine levels remained within the range of physiological levels throughout the simulations.

CONCLUSION

We developed a novel computational model that successfully simulated the development of restenosis in a blood vessel following an angioplasty and bare-metal stent deployment based on the characteristics of the vessel cross-section and stent. A further development of this model could ultimately be used as a predictive tool to depict patient outcomes and inform treatment options.

Inflammatory bowel disease: mechanisms, redox considerations, and therapeutic targets

Oxidative stress is thought to play a key role in the development of intestinal damage in inflammatory bowel disease (IBD), because of its primary involvement in intestinal cells' aberrant immune and inflammatory responses to dietary antigens and to the commensal bacteria. During the active disease phase, activated leukocytes generate not only a wide spectrum of pro-inflammatory cytokines, but also excess oxidative reactions, which markedly alter the redox equilibrium within the gut mucosa, and maintain inflammation by inducing redox-sensitive signaling pathways and transcription factors. Moreover, several inflammatory molecules generate further oxidation products, leading to a self-sustaining and auto-amplifying vicious circle, which eventually impairs the gut barrier. The current treatment of IBD consists of long-term conventional anti-inflammatory therapy and often leads to drug refractoriness or intolerance, limiting patients' quality of life. Immune modulators or anti-tumor necrosis factor α antibodies have recently been used, but all carry the risk of significant side effects and a poor treatment response. Recent developments in molecular medicine point to the possibility of treating the oxidative stress associated with IBD, by designing a proper supplementation of specific lipids to induce local production of anti-inflammatory derivatives, as well as by developing biological therapies that target selective molecules (i.e., nuclear factor-κB, NADPH oxidase, prohibitins, or inflammasomes) involved in redox signaling. The clinical significance of oxidative stress in IBD is now becoming clear, and may soon lead to important new therapeutic options to lessen intestinal damage in this disease.

Membrane TNF-alpha-activated programmed necrosis is mediated by Ceramide-induced reactive oxygen species

Background

Programmed necrosis is a form of caspase-independent cell death whose molecular regulation is poorly understood. While tumor necrosis factor-alpha (TNF-α) has been identified as an activator of programmed necrosis, the specific context under which this can happen is unclear. Recently we reported that TNF-α can be expressed by human tumor cells as both a membrane tethered (mTNF-α) and a soluble (sTNF-α) form. Whereas low level, tumor-derived sTNF-α acts as a tumor promoter, tumor cell expression of mTNF-α significantly delays tumor growth in mice, in large part by induction of programmed necrosis of tumor associated myeloid cells. In this study we sought to determine the molecular mechanism involved in mTNF-α oxidative stress-induced cell death by evaluating the known pathways involved in TNF receptor-induced programmed necrosis.

Methods

The source of Reactive Oxygen Species (ROS) in mTNF-α treated cells was determined by coculturing RAW 264.7 monocytic and L929 fibroblasts cells with fixed B16F10 control or mTNF-α expressing-melanoma cells in the presence of inhibitors of NADPH and mitochondria ROS. To identify the down-stream effector of TNF-a receptors (TNFR), level of phospho-RIP-1 and ceramide activity were evaluated.

To determine whether mTNF-mediated cell death was dependent on a specific TNFR, cell death was measured in primary CD11b myeloid cells isolated from wild-type or TNFR-1, TNFR-2, TNFR-1 and TNFR-2 double knockout mice, cocultured with various TNF-α isoform.

Results

Tumor derived-mTNF-α increased ROS-mediated cytotoxicity, independent of caspase-3 activity. Although TNFR on target cells were required for this effect, we observed that mTNF-induced cell death could be mediated through both TNFR-1 and the death domain-lacking TNFR-2. ROS generation and cytotoxicity were inhibited by a mitochondrial respiratory chain inhibitor but not by an inhibitor of NADPH oxidase. mTNF-α mediated cytotoxicity was independent of RIP-1, a serine/threonine kinase that serves as a main adaptor protein of sTNF-α induced programmed necrosis. Instead, mTNF-α-induced ROS and cell death was prohibited by the ceramide-activated protein kinase (CAPK) inhibitor.

Conclusion

These findings demonstrate that the mTNF-α isoform is an effective inducer of programmed necrosis through a caspase independent, ceramide-related pathway. Interestingly, unlike sTNFα, mTNF-induced programmed necrosis is not dependent on the presence of TNFR1.

Model composition through model reduction: a combined model of CD95 and NF-κB signaling pathways

Background

Many mathematical models characterizing mechanisms of cell fate decisions have been constructed recently. Their further study may be impossible without development of methods of model composition, which is complicated by the fact that several models describing the same processes could use different reaction chains or incomparable sets of parameters. Detailed models not supported by sufficient volume of experimental data suffer from non-unique choice of parameter values, non-reproducible results, and difficulty of analysis. Thus, it is necessary to reduce existing models to identify key elements determining their dynamics, and it is also required to design the methods allowing us to combine them.

Results

Here we propose a new approach to model composition, based on reducing several models to the same level of complexity and subsequent combining them together. Firstly, we suggest a set of model reduction tools that can be systematically applied to a given model. Secondly, we suggest a notion of a minimal complexity model. This model is the simplest one that can be obtained from the original model using these tools and still able to approximate experimental data. Thirdly, we propose a strategy for composing the reduced models together. Connection with the detailed model is preserved, which can be advantageous in some applications. A toolbox for model reduction and composition has been implemented as part of the BioUML software and tested on the example of integrating two previously published models of the CD95 (APO-1/Fas) signaling pathways. We show that the reduced models lead to the same dynamical behavior of observable species and the same predictions as in the precursor models. The composite model is able to recapitulate several experimental datasets which were used by the authors of the original models to calibrate them separately, but also has new dynamical properties.

Conclusion

Model complexity should be comparable to the complexity of the data used to train the model. Systematic application of model reduction methods allows implementing this modeling principle and finding models of minimal complexity compatible with the data. Combining such models is much easier than of precursor models and leads to new model properties and predictions.

Rapamycin preconditioning attenuates transient focal cerebral ischemia/reperfusion injury in mice

Rapamycin, an mTOR inhibitor and immunosuppressive agent in clinic, has protective effects on traumatic brain injury and neurodegenerative diseases. But, its effects on transient focal ischemia/reperfusion disease are not very clear. In this study, we examined the effects of rapamycin preconditioning on mice treated with middle cerebral artery occlusion/reperfusion operation (MCAO/R). We found that the rapamycin preconditioning by intrahippocampal injection 20 hr before MCAO/R significantly improved the survival rate and longevity of mice. It also decreased the neurological deficit score, infracted areas and brain edema. In addition, rapamycin preconditioning decreased the production of NF-κB, TNF-α, and Bax, but not Bcl-2, an antiapoptotic protein in the ischemic area. From these results, we may conclude that rapamycin preconditioning attenuate transient focal cerebral ischemia/reperfusion injury and inhibits apoptosis induced by MCAO/R in mice.

TNFα affects energy metabolism and stimulates biogenesis of mitochondria in EA.hy926 endothelial cells

Mitochondrial response of EA.hy926 endothelial cells to tumour necrosis factor alpha (TNFα) was investigated. It was confirmed that TNFα stimulates reactive oxygen species (ROS) generation and increases intercellular adhesion molecule-1 (ICAM-1) level. These changes were paralleled by elevated oxygen consumption, slightly raised total mitochondrial mass and increased manganese superoxide dismutase (Mn-SOD) and uncoupling protein 2 (UCP2) content. They also correlated with a rise of mitochondrial transcription factor 1 (TFAM), nuclear respiratory factor-1 (NRF-1) and peroxisome proliferator-activated receptor-γ coactivator (PGC)-1α, which are involved in regulation of mitochondrial biogenesis and an elevated level of selected respiratory chain proteins. Thus, the apparent stimulatory effect of TNFα on mitochondrial metabolism probably reflects an increased amount of mitochondria rather than activation of biochemical processes per se, although the latter cannot be excluded definitely. These observations are similar to those described for cardiac muscle cells challenged with bacterial lipopolysaccharide (LPS), in which mitochondrial biogenesis was postulated. Stimulation of mitochondrial biogenesis could be a mechanism activated to prevent TNFα-induced cell death.

NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis

The NF-κB signaling pathway is central to the body’s response to many pathogens. Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this pathway have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung. NF-κB signaling is triggered via tumor necrosis factor-α (TNF) binding to receptors on macrophages; TNF has been shown to play a key role in infection dynamics in humans and multiple animal systems. Using our multi-scale model, we predict the impact of TNF-induced NF-κB-mediated responses on the outcome of infection at the level of a granuloma, an aggregate of immune cells and bacteria that forms in response to infection and is key to containment of infection and clinical latency. We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size. Because we incorporate intracellular signaling pathways explicitly, our analysis also elucidates NF-κB-associated signaling molecules and processes that may be new targets for infection control.

A modular model of the apoptosis machinery

Using a modular principle of computer hardware as a metaphor, we defined and implemented in the BioUML platform a module concept for biological pathways. BioUML provides a user interface to create modular models and convert them automatically into plain models for further simulations. Using this approach, we created the apoptosis model including 13 modules: death stimuli (TRAIL, CD95L, and TNF-α)-induced activation of caspase-8; survival stimuli (p53, EGF, and NF-κB) regulation; the mitochondria level; cytochrome C- and Smac-induced activation of caspase-3; direct activation of effector caspases by caspase-8 and - 12; PARP and apoptosis execution phase modules. Each module is based on earlier published models and extended by data from the Reactome and TRANSPATH databases. The model ability to simulate the apoptosis-related processes was checked; the modules were validated using experimental data.

AVAILABILITY

http://www.biouml.org/apoptosis.shtml .

Mathematical modeling and sensitivity analysis of the integrated TNFα-mediated apoptotic pathway for identifying key regulators

TNFα-mediated apoptosis is one of the complex and tightly regulated cellular processes as it involves the activation of both pro- and anti-apoptotic signaling pathways. Thus, it is important to elucidate the molecular players of this process and their dynamics in order to gain an in-depth understanding of the mechanisms underlying apoptosis. To this end, we proposed an integrated model of TNFα-mediated apoptosis pathway in Type I cells, formulated based on the principles of mass action kinetics. The model includes major apoptotic modules-the extrinsic and intrinsic pathways, the NFκB survival signaling and various regulatory mechanisms. We performed simulations and sensitivity analyses to study the role of NFκB pathway in regulating apoptosis, and identified IAP as one of the more potent regulators of apoptosis.

Modular model of TNFalpha cytotoxicity

MOTIVATION

Tumour Necrosis Factor alpha (TNF) initiates a complex series of biochemical events in the cell upon binding to its type R1 receptor (TNF-R1). Recent experimental work has unravelled the molecular regulation of the signalling complexes that lead either to cell survival or death. Survival signals are activated by direct binding of TNF to TNF-R1 at the cell membrane whereas apoptotic signals by endocytosed TNF/TNF-R1 complexes. Here we describe a reduced, effective model with few free parameters, where we group some intricate mechanisms into effective modules, that successfully describes this complex set of actions. We study the parameter space to show that the model is structurally stable and robust over a broad range of parameter values.

RESULTS

We use state-of-the-art Bayesian methods (a Sequential Monte Carlo sampler) to perform inference of plausible values of the model parameters from experimental data. As a result, we obtain a robust model that can provide a solid basis for further modelling of TNF signalling. The model is also suitable for inclusion in multi-scale simulation programs that are presently under development to study the behaviour of large tumour cell populations.

AVAILABILITY

We provide supplementary material that includes all mathematical details and all algorithms (Matlab code) and models (SBML descriptions).

CONTACT

edoardo.milotti@ts.infn.it

Modeling the TNFα-induced apoptosis pathway in hepatocytes

The proinflammatory cytokine TNFα fails to provoke cell death in isolated hepatocytes but has been implicated in hepatocyte apoptosis during liver diseases associated with chronic inflammation. Recently, we showed that TNFα is able to sensitize primary murine hepatocytes cultured on collagen to Fas ligand-induced apoptosis and presented a mathematical model of the sensitizing effect. Here, we analyze how TNFα induces apoptosis in combination with the transcriptional inhibitor actinomycin D (ActD). Accumulation of reactive oxygen species (ROS) in response to TNFR activation turns out to be critical for sustained activation of JNK which then triggers mitochondrial pathway-dependent apoptosis. In addition, the amount of JNK is strongly upregulated in a ROS-dependent way. In contrast to TNFα plus cycloheximide no cFLIP degradation is observed suggesting a different apoptosis pathway in which the Itch-mediated cFLIP degradation and predominantly caspase-8 activation is not involved. Time-resolved data of the respective pro- and antiapoptotic factors are obtained and subjected to mathematical modeling. On the basis of these data we developed a mathematical model which reproduces the complex interplay regulating the phosphorylation status of JNK and generation of ROS. This model was fully integrated with our model of TNFα/Fas ligand sensitizing as well as with a published NF-κB-model. The resulting comprehensive model delivers insight in the dynamical interplay between the TNFα and FasL pathways, NF-κB and ROS and gives an example for successful model integration.

On the architecture of cell regulation networks

BACKGROUND

With the rapid development of high-throughput experiments, detecting functional modules has become increasingly important in analyzing biological networks. However, the growing size and complexity of these networks preclude structural breaking in terms of simplest units. We propose a novel graph theoretic decomposition scheme combined with dynamics consideration for probing the architecture of complex biological networks.

RESULTS

Our approach allows us to identify two structurally important components: the "minimal production unit"(MPU) which responds quickly and robustly to external signals, and the feedback controllers which adjust the output of the MPU to desired values usually at a larger time scale. The successful application of our technique to several of the most common cell regulation networks indicates that such architectural feature could be universal. Detailed illustration and discussion are made to explain the network structures and how they are tied to biological functions.

CONCLUSIONS

The proposed scheme may be potentially applied to various large-scale cell regulation networks to identify functional modules that play essential roles and thus provide handles for analyzing and understanding cell activity from basic biochemical processes.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.

Sensitivity analysis of dynamic biological systems with time-delays

BACKGROUND

Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.

RESULTS

We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention.

CONCLUSIONS

By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma

Tuberculosis (TB) granulomas are organized collections of immune cells comprised of macrophages, lymphocytes and other cells that form in the lung as a result of immune response to Mycobacterium tuberculosis (Mtb) infection. Formation and maintenance of granulomas are essential for control of Mtb infection and are regulated in part by a pro-inflammatory cytokine, tumor necrosis factor-α (TNF). To characterize mechanisms that control TNF availability within a TB granuloma, we developed a multi-scale two compartment partial differential equation model that describes a granuloma as a collection of immune cells forming concentric layers and includes TNF/TNF receptor binding and trafficking processes. We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using our model, we then demonstrated that the organization of immune cells within a TB granuloma as well as TNF/TNF receptor binding and intracellular trafficking are two important factors that control TNF availability and may spatially coordinate TNF-induced immunological functions within a granuloma. Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry. To further elucidate the role of TNF in the process of granuloma development, our modeling and experimental findings on TNF-associated molecular scale aspects of the granuloma can be incorporated into larger scale models describing the immune response to TB infection. Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

Tuberculosis is a common and deadly infectious disease caused by a highly successful bacterium, Mycobacterium tuberculosis (Mtb). Multiple host immune factors control the formation of a self-organizing aggregate of immune cells termed a granuloma in the lungs after inhalation of Mtb. One such factor, tumor necrosis factor-α (TNF), is a protein that regulates inflammatory immune responses. Availability of TNF within a TB granuloma has been proposed to have a critical role in the protective immunity against TB. However, direct measurement of the level of TNF in a granuloma is not experimentally feasible. Therefore, we develop a mathematical model based on an experimental model of granuloma developed in mice to predict TNF availability in a granuloma. We measure values of critical model parameters and explore mechanisms that influence TNF availability in the granuloma. We find that cellular organization in a granuloma and intracellular trafficking of TNF control TNF availability in a granuloma. Further, our model analysis also highlights anti-TNF drug properties that determine their TNF neutralization power. Our findings complement and extend those of recent studies on the role of TNF in the immune response against TB.

Amino acid transport systems beta and A in fetal T lymphocytes in intrauterine growth restriction and with tumor necrosis factor-alpha treatment

Intrauterine growth restriction (IUGR) is associated with reduced activity of placental amino acid transport systems beta and A. Whether this phenotype is maintained in fetal cells outside the placenta is unknown. In IUGR, cord blood tumor necrosis factor (TNF)-alpha concentrations are raised, potentially influencing amino acid transport in fetal cells. We used fetal T lymphocytes as a model to study systems beta and A amino acid transporters in IUGR compared with normal pregnancy. We also studied the effect of TNF-alpha on amino acid transporter activity. In fetal lymphocytes from IUGR pregnancies, taurine transporter mRNA expression encoding system beta transporter was reduced, but there was no change in system beta activity. No significant differences were observed in system A mRNA expression (encoding SNAT1 and SNAT2) or system A activity between the two groups. After 24 or 48 h TNF-alpha treatment, fetal T lymphocytes from normal pregnancies showed no significant change in system A or system beta activity, although cell viability was compromised. This study represents the first characterization of amino acid transport in a fetal cell outside the placenta in IUGR. We conclude that the reduced amino acid transporter activity found in placenta in IUGR is not a feature of all fetal cells.

Modelling cellular signalling systems

Cell signalling pathways and networks are complex and often non-linear. Signalling pathways can be represented as systems of biochemical reactions that can be modelled using differential equations. Computational modelling of cell signalling pathways is emerging as a tool that facilitates mechanistic understanding of complex biological systems. Mathematical models are also used to generate predictions that may be tested experimentally. In the present chapter, the various steps involved in building models of cell signalling pathways are discussed. Depending on the nature of the process being modelled and the scale of the model, different mathematical formulations, ranging from stochastic representations to ordinary and partial differential equations are discussed. This is followed by a brief summary of some recent modelling successes and the state of future models.

Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

Background

The development of quantitative models of signal transduction, as well as parameter estimation to improve existing models, depends on the ability to obtain quantitative information about various proteins that are part of the signaling pathway. However, commonly-used measurement techniques such as Western blots and mobility shift assays provide only qualitative or semi-quantitative data which cannot be used for estimating parameters. Thus there is a clear need for techniques that enable quantitative determination of signal transduction intermediates.

Results

This paper presents an integrated modeling and experimental approach for quantitatively determining transcription factor profiles from green fluorescent protein (GFP) reporter data. The technique consists of three steps: (1) creating data sets for green fluorescent reporter systems upon stimulation, (2) analyzing the fluorescence images to determine fluorescence intensity profiles using principal component analysis (PCA) and K-means clustering, and (3) computing the transcription factor concentration from the fluorescence intensity profiles by inverting a model describing transcription, translation, and activation of green fluorescent proteins.

We have used this technique to quantitatively characterize activation of the transcription factor NF-κB by the cytokine TNF-α. In addition, we have applied the quantitative NF-κB profiles obtained from our technique to develop a model for TNF-α signal transduction where the parameters were estimated from the obtained data.

Conclusion

The technique presented here for computing transcription factor profiles from fluorescence microscopy images of reporter cells generated quantitative data on the magnitude and dynamics of NF-κB activation by TNF-α. The obtained results are in good agreement with qualitative descriptions of NF-κB activation as well as semi-quantitative experimental data from the literature. The profiles computed from the experimental data have been used to re-estimate parameters for a NF-κB model and the results of additional experiments are predicted very well by the model with the new parameter values. While the presented approach has been applied to NF-κB and TNF-α signaling, it can be used to determine the profile of any transcription factor as long as GFP reporter fluorescent profiles are available.

Understanding NF-kappaB signaling via mathematical modeling

Mammalian inflammatory signaling, for which NF-kappaB is a principal transcription factor, is an exquisite example of how cellular signaling pathways can be regulated to produce different yet specific responses to different inflammatory insults. Mathematical models, tightly linked to experiment, have been instrumental in unraveling the forms of regulation in NF-kappaB signaling and their underlying molecular mechanisms. Our initial model of the IkappaB-NF-kappaB signaling module highlighted the role of negative feedback in the control of NF-kappaB temporal dynamics and gene expression. Subsequent studies sparked by this work have helped to characterize additional feedback loops, the input-output behavior of the module, crosstalk between multiple NF-kappaB-activating pathways, and NF-kappaB oscillations. We anticipate that computational techniques will enable further progress in the NF-kappaB field, and the signal transduction field in general, and we discuss potential upcoming developments.