Stochastic modeling of B lymphocyte terminal differentiation and its suppression by dioxin

Resilience function uncovers the critical transitions in cancer initiation

Considerable evidence suggests that during the progression of cancer initiation, the state transition from wellness to disease is not necessarily smooth but manifests switch-like nonlinear behaviors, preventing the cancer prediction and early interventional therapy for patients. Understanding the mechanism of such wellness-to-disease transitions is a fundamental and challenging task. Despite the advances in flux theory of nonequilibrium dynamics and 'critical slowing down'-based system resilience theory, a system-level approach still lacks to fully describe this state transition. Here, we present a novel framework (called bioRFR) to quantify such wellness-to-disease transition during cancer initiation through uncovering the biological system's resilience function from gene expression data. We used bioRFR to reconstruct the biologically and dynamically significant resilience functions for cancer initiation processes (e.g. BRCA, LUSC and LUAD). The resilience functions display the similar resilience pattern with hysteresis feature but different numbers of tipping points, which implies that once the cell become cancerous, it is very difficult or even impossible to reverse to the normal state. More importantly, bioRFR can measure the severe degree of cancer patients and identify the personalized key genes that are associated with the individual system's state transition from normal to tumor in resilience perspective, indicating that bioRFR can contribute to personalized medicine and targeted cancer therapy.

Dynamical systems approaches to personalized medicine

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.

Embracing Systems Toxicology at Single-Cell Resolution

As systems biology expands its multi-omic spectrum to increasing resolutions, distinguishing cells based on single-cell profiles becomes feasible. Unlike traditional bulk assays that average cellular responses and blur the distinct identities of responsive cells, single-cell technologies enable sensitive detection of small cellular changes and precise identification of those cells perturbed by toxicants. Among the suite of omic technologies that continue to expand and become affordable, single-cell RNA sequencing (scRNA-seq) is at the cutting edge and leading the way to transform systems toxicology. Single-cell systems toxicology can provide a wealth of information to elucidate cell-specific alterations and response trajectories, detect points-of-departure, map and develop dynamical models of toxicity pathways.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.

Modeling Procedures

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.

Kinetic models of hematopoietic differentiation

As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.

The Influence of Human Interindividual Variability on the Low-Dose Region of Dose-Response Curve Induced by 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in Primary B Cells

The influence of interindividual variability is not typically assessed in traditional toxicological studies. Given that chemical exposures occur in heterogeneous populations, this knowledge gap has the potential to cause undue harm within the realms of public health and industrial and municipal finances. A recent report from the National Research Council (NRC) suggests that when accounting for interindividual variation in responses, traditionally assumed nonlinear dose-response relationships (DRRs) for noncancer-causing endpoints would better be explained with a linear relationship within the low-dose region. To address this knowledge gap and directly test the NRC's assumption, this study focused on assessing the DRR between 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD) exposure and immune suppression in a cohort of unique human donors. Human B cells were isolated from 51 individual donors and treated with logarithmically increasing concentrations of TCDD (0-30 nM TCDD). Two endpoints sensitive to TCDD were assessed: (1) number of IgM-secreting B cells and (2) quantity of IgM secreted. The results show that TCDD significantly suppressed both the number of IgM-secreting B cells and the quantity of IgM secreted (P < .05). Statistical model comparisons indicate that the low-dose region of the two DRRs is best explained with a nonlinear relationship. Rather than assuming low-dose linearity for all noncancer-causing DRRs, our study suggests the need to consider the specific mode of action of toxicants and pharmaceuticals during risk-management decision making.

Computational Modeling of a Transcriptional Switch Underlying B-Lymphocyte Lineage Commitment of Hematopoietic Multipotent Cells

Despite progresses in identifying the cellular mechanisms at the basis of the differentiation of hematopoietic stem/progenitor cells, little is known about the regulatory circuitry at the basis of lineage commitment of hematopoietic multipotent progenitors. To address this issue, we propose a computational approach to give further insights in the comprehension of this genetic mechanism. Differently from T lymphopoiesis, however, there is at present no mathematical model describing lineage restriction of multipotent progenitors to early B-cell precursors. Here, we provide a first model-constructed on the basis of current experimental evidence from literature and of publicly available microarray datasets-of the genetic regulatory network driving the cellular fate determination at the stage of lymphoid lineage commitment, with particular regard to the multipotent-B-cell progenitor transition. By applying multistability analysis methods, we are able to assess the capability of the model to capture the experimentally observed switch-like commitment behavior. These methods allow us to confirm the central role of zinc finger protein 521 (ZNF521) in this process, that we had previously reported, and to identify a novel putative functional interaction for ZNF521, which is essential to realize such characteristic behavior. Moreover, using the devised model, we are able to rigorously analyze the mechanisms underpinning irreversibility of the physiological commitment step and to devise a possible reprogramming strategy, based on the combined modification of the expression of ZNF521 and EBF1.

Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses

BACKGROUND

Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations.

OBJECTIVES

Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data.

METHODS

Here we have examined dose-response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures.

DISCUSSION AND CONCLUSION

Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays.

All-or-none suppression of B cell terminal differentiation by environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin

Many environmental contaminants can disrupt the adaptive immune response. Exposure to the ubiquitous aryl hydrocarbon receptor (AhR) ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other agonists suppresses the antibody response. The underlying pathway mechanism by which TCDD alters B cell function is not well understood. The present study investigated the mechanism of AhR-mediated pathways and mode of suppression by which TCDD perturbs terminal differentiation of B cells to plasma cells and thereby impairs antibody production. An integrated approach combining computational pathway modeling and in vitro assays with primary mouse B cells activated by lipopolysaccharide was employed. We demonstrated that suppression of the IgM response by TCDD occurs in an all-or-none (binary) rather than graded mode: i.e., it reduces the number of IgM-secreting cells in a concentration-dependent manner without affecting the IgM content in individual plasma cells. The mathematical model of the gene regulatory circuit underpinning B cell differentiation revealed that two previously identified AhR-regulated pathways, inhibition of signaling protein AP-1 and activation of transcription factor Bach2, could account for the all-or-none mode of suppression. Both pathways disrupt the operation of a bistable-switch circuit that contains transcription factors Bcl6, Prdm1, Pax5, and Bach2 and regulates B cell fate. The model further predicted that by transcriptionally activating Bach2, TCDD might delay B cell differentiation and increase the likelihood of isotype switching, thereby altering the antibody repertoire. In conclusion, the present study revealed the mode and specific pathway mechanisms by which the environmental immunosuppressant TCDD suppresses B cell differentiation.

Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

Plasma Cytokine Concentrations in Workers Exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

Objectives: Few epidemiological studies have studied the effect of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on blood cytokine levels. In this study we investigated changes in plasma levels of a large panel of cytokines, chemokines, and growth factors among workers from a Dutch historical cohort occupationally exposed to chlorophenoxy herbicides and contaminants including TCDD. Methods: Eighty-five workers who had been exposed to either high (n = 47) or low (n = 38) TCDD levels more than 30 years before serum collection were included in the current investigation. Plasma level of 16 cytokines, 10 chemokines, and 6 growth factors were measured. Current plasma levels of TCDD (TCDD_current) were determined by high-resolution gas chromatography/isotope-dilution high-resolution mass spectrometry. TCDD blood levels at the time of last exposure (TCDD_max) were estimated using a one-compartment first order kinetic model. Results: Blood levels of most analytes had a negative association with current and estimated past maximum TCDD levels. These decreases reached formal statistical significance for fractalkine, transforming growth factor alpha (TGF-α), and fibroblast growth factor 2 (FGF2) with increasing TCDD levels. Conclusion: Our study showed a general reduction in most analyte levels with the strongest effects for fractalkine, FGF2, and TGF-α. These findings suggest that TCDD exposure could suppress the immune system and that chemokine and growth factor-dependent cellular pathway changes by TCDD may play role in TCDD toxicity and associated health effects.

Familial paraproteinemia: hyper-responsive B-cells as endophenotype

The prevalence of paraproteinemias or monoclonal gammopathies increases with age. No other major risk factors have been recognized, but significant associations have been reported with chronic antigen exposure, agricultural environment, and family history. In around 130 families reported worldwide, IgG or IgA monoclonal gammopathy of undetermined significance (MGUS) occurs with multiple myeloma (MM) whereas Waldenström's macroglobulinemia (WM) is linked to IgM MGUS. Of the 8 multi-case families described here, 5 are remarkable for including both IgG/IgA and IgM type disorders. In the remaining 3 families IgG/IgA MGUS and MM occurred with Hodgkin disease and T-cell malignancies. These different patterns of familial paraproteinemia indicate different genetic backgrounds. A previously described functional phenotype of hyper-responsive B lymphocytes fulfils criteria for being an endophenotype and may be related to raised serum IgM. Identifying an endophenotype is important to ensure correct classification of affected family members and thus enhance the power of genetic studies.

A continuous-time, discrete-state method for simulating the dynamics of biochemical systems

Computational systems biology is largely driven by mathematical modeling and simulation of biochemical networks, via continuous deterministic methods or discrete event stochastic methods. Although the deterministic methods are efficient in predicting the macroscopic behavior of a biochemical system, they are severely limited by their inability to represent the stochastic effects of random molecular fluctuations at lower concentration. In this work, we have presented a novel method for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of stochastic effects. To demonstrate the feasibility of our approach, we have tested our method on three previously reported biochemical networks. The results, while staying true to their deterministic form, also reflect the stochastic effects of random fluctuations that are dominant as the system transitions into a lower concentration. This ability to adapt to a concentration gradient makes this method particularly attractive for systems biology-based applications.

The long winding road toward understanding the molecular mechanisms for B-cell suppression by 2,3,7,8-tetrachlorodibenzo-p-dioxin

Suppression of humoral immune responses by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) was first reported in the mid-1970s. Since this initial observation, much effort has been devoted by many laboratories toward elucidation of the cellular and molecular mechanisms responsible for the profound impairment of humoral immune responses by TCDD, which is characterized by decreased B cell to plasma cell differentiation and suppression of immunoglobulin production. These efforts have led to a significant body of research demonstrating a direct effect of TCDD on B-cell maturation and function as well as a requisite but as yet undefined role of the aryl hydrocarbon receptor (AhR) in these effects. Likewise, a number of molecular targets putatively involved in mediating B-cell dysfunction by TCDD, and other AhR ligands, have been identified. However, our current understanding has primarily relied on findings from mouse models, and the translation of this knowledge to effects on human B cells and humoral immunity in humans is less clear. Therefore, a current challenge is to determine how TCDD and the AhR affect human B cells. Efforts have been made in this direction but continued progress in developing adequate human models is needed. An in-depth discussion of these advances and limitations in elucidating the cellular and molecular mechanisms putatively involved in the suppression of B-cell function by TCDD as well as the implications on human diseases associated in epidemiological studies with exposure to TCDD and dioxin-like compounds is the primary focus of this review.