Aminopeptidase N/CD13 Crosslinking Promotes the Activation and Membrane Expression of Integrin CD11b/CD18.

The ß2 integrin CD11b/CD18, also known as complement receptor 3 (CR3), and the moonlighting protein aminopeptidase N (CD13), are two myeloid immune receptors with overlapping activities: adhesion, migration, phagocytosis of opsonized particles, and respiratory burst induction. Given their common functions, shared physical location, and the fact that some receptors can activate a selection of integrins, we hypothesized that CD13 could induce CR3 activation through an inside-out signaling mechanism and possibly have an influence on its membrane expression. We revealed that crosslinking CD13 on the surface of human macrophages not only activates CR3 but also influences its membrane expression. Both phenomena are affected by inhibitors of Src, PLCγ, Syk, and actin polymerization. Additionally, after only 10 min at 37 °C, cells with crosslinked CD13 start secreting pro-inflammatory cytokines like interferons type 1 and 2, IL-12p70, and IL-17a. We integrated our data with a bioinformatic analysis to confirm the connection between these receptors and to suggest the signaling cascade linking them. Our findings expand the list of features of CD13 by adding the activation of a different receptor via inside-out signaling. This opens the possibility of studying the joint contribution of CD13 and CR3 in contexts where either receptor has a recognized role, such as the progression of some leukemias.

Cyclic Attractors Are Critical for Macrophage Differentiation, Heterogeneity, and Plasticity.

Adaptability, heterogeneity, and plasticity are the hallmarks of macrophages. How these complex properties emerge from the molecular interactions is an open question. Thus, in this study we propose an actualized regulatory network of cytokines, signaling pathways, and transcription factors to survey the differentiation, heterogeneity, and plasticity of macrophages. The network recovers attractors, which in regulatory networks correspond to cell types, that correspond to M0, M1, M2a, M2b, M2c, M2d, M2-like, and IL-6 producing cells, including multiple cyclic attractors that are stable to perturbations. These cyclic attractors reproduce experimental observations and show that oscillations result from the structure of the network. We also study the effect of the environment in the differentiation and plasticity of macrophages, showing that the observed heterogeneity in macrophage populations is a result of the regulatory network and its interaction with the micro-environment. The macrophage regulatory network gives a mechanistic explanation to the heterogeneity and plasticity of macrophages seen in vivo and in vitro, and offers insights into the mechanism that allows the immune system to react to a complex dynamic environment.

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

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

Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1).

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), is a global health threat with the potential to cause severe disease manifestations in the lungs. Although COVID-19 has been extensively characterized clinically, the factors distinguishing SARS-CoV-2 from other respiratory viruses are unknown. Here, we compared the clinical, histopathological, and immunological characteristics of patients with COVID-19 and pandemic influenza A(H1N1). We observed a higher frequency of respiratory symptoms, increased tissue injury markers, and a histological pattern of alveolar pneumonia in pandemic influenza A(H1N1) patients. Conversely, dry cough, gastrointestinal symptoms and interstitial lung pathology were observed in COVID-19 cases. Pandemic influenza A(H1N1) was characterized by higher levels of IL-1RA, TNF-α, CCL3, G-CSF, APRIL, sTNF-R1, sTNF-R2, sCD30, and sCD163. Meanwhile, COVID-19 displayed an immune profile distinguished by increased Th1 (IL-12, IFN-γ) and Th2 (IL-4, IL-5, IL-10, IL-13) cytokine levels, along with IL-1ß, IL-6, CCL11, VEGF, TWEAK, TSLP, MMP-1, and MMP-3. Our data suggest that SARS-CoV-2 induces a dysbalanced polyfunctional inflammatory response that is different from the immune response against pandemic influenza A(H1N1). Furthermore, we demonstrated the diagnostic potential of some clinical and immune factors to differentiate both diseases. These findings might be relevant for the ongoing and future influenza seasons in the Northern Hemisphere, which are historically unique due to their convergence with the COVID-19 pandemic.

Medical Systems Biology.

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

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.

Case Studies.

The aim of this chapter is to illustrate the modeling procedures discussed in the previous chapter via three well-chosen examples.

The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks.

Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity.

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-ß and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.