An Interactive Macrophage Signal Transduction Map Facilitates Comparative Analyses of High-Throughput Data

Non-coding RNA LINC01010 regulates macrophage polarization and innate immune functions by modulating NFκB signaling pathway

Innate immune response is regulated by tissue resident or infiltrating immune cells such as macrophages (Mφ) that play critical role in tissue development, homeostasis, and repair of damaged tissue. However, the epigenetic mechanisms that regulate Mφ plasticity and innate immune functions are not well understood. Long non-coding RNA (lncRNA) are among the most abundant class of transcriptome but their function in myeloid cell biology is less explored. In this study, we deciphered the regulatory role of previously uncharacterized lncRNAs in Mφ polarization and innate immune responses. Two lncRNAs showed notable changes in their levels during M1 and M2 Mφ differentiation. Our findings indicate that LINC01010 expression increased and AC007032 expression decreased significantly. LINC01010 exhibit myeloid cell-specificity, while AC007032.1 is ubiquitous and expressed in both myeloid and lymphoid (T cells, B cells and NK cells) cells. Expression of these lncRNAs is dysregulated in periodontal disease (PD), a microbial biofilm-induced immune disease, and responsive to lipopolysaccharide (LPS) from different oral and non-oral bacteria. Knockdown of LINC01010 but not AC007032.1 reduced the surface expression of Mφ differentiation markers CD206 and CD68, and M1Mφ polarization markers MHCII and CD32. Furthermore, LINC01010 RNAi attenuated bacterial phagocytosis, antigen processing and cytokine secretion suggesting its key function in innate immunity. Mechanistically, LINC01010 knockdown Mφ treated with Escherichia coli LPS exhibit significantly reduced expression of multiple nuclear factor kappa B pathway genes. Together, our data highlight functional role of a PD-associated lncRNA LINC01010 in shaping macrophage differentiation, polarization, and innate immune activation.

Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma

The discovery of potential disease-causing genes can aid medical progress. The post-genomic era has made this a more difficult task. Modern high-throughput methods have not solved the problem of identifying disease genes. Conventional methods cannot be used to investigate many rare or lethal diseases. Monitoring gene expression values in different samples using microarray technology is one of the best and most accurate ways to identify disease-causing genes. One of the most recent advances in experimental molecular biology is microarrays, which allow researchers to simultaneously monitor the expression levels of thousands of genes. Statistical analysis of microarray data might aid gene discovery by revealing pathways related to the target gene and facilitating identification of candidate genes. Systems biology, an interdisciplinary approach, has emerged as a crucial analytic tool with the potential to reveal previously unidentified causes and consequences of human illness. Genetic, environmental, immunological, or neurological factors have been implicated in the developing complex disorders like cancer. Because of this, it is important to approach the study of such disease from a novel perspective. The system biology approach allows us to rapidly identify disease-causing genes and assess their viability as therapeutic targets. This chapter demonstrates systems biology approaches to identify candidate genes using public database. Oral squamous cell carcinoma (OSCC) is used as a model disease to show how systems biology can be used successfully to identify and prioritize disease genes.

An in vitro model of the macrophage-endothelial interface to characterize CAR T-cell induced cytokine storm

Chimeric Antigen Receptor (CAR) T-cell therapy is a highly effective treatment for B-cell malignancies but limited in use due to clinically significant hyperinflammatory toxicities. Understanding the pathophysiologic mechanisms which mediate these toxicities can help identify novel management strategies. Here we report a novel in vitro model of the macrophage-endothelial interface to study the effects of CAR T-cell-induced cytokine storm. Using this model, we demonstrate that macrophage-mediated inflammation is regulated by endothelial cell activity. Furthermore, endothelial inflammation occurs independently of macrophages following exposure to CAR T-cell products and the induced endothelial inflammation potentiates macrophage-mediated inflammatory signaling, leading to a hyperinflammatory environment. While corticosteroids, the current gold standard of care, attenuate the resulting macrophage inflammatory signaling, the endothelial activity remains refractory to this treatment strategy. Utilizing a network model, coupled to in vitro secretion profiling, we identified STAT3 programming as critical in regulating this endothelial behavior. Lastly, we demonstrate how targeting STAT3 activity can abrogate endothelial inflammation and attenuate this otherwise hyperinflammatory environment. Our results demonstrate that endothelial cells play a central role in the pathophysiology of CAR T-cell toxicities and targeting the mechanisms driving the endothelial response can guide future clinical management.

Triple-color single-molecule imaging for analysis of the role of receptor oligomers in signal transduction

Membrane receptors provide interfaces of various extracellular stimuli to transduce the signal into the cell. Receptors are required to possess such conflicting properties as high sensitivity and noise reduction for the cell to keep its homeostasis and appropriate responses. To understand the mechanisms by which these functions are achieved, single-molecule monitoring of the motilities of receptors and signaling molecules on the plasma membrane is one of the most direct approaches. This review article introduces several recent single-molecule imaging studies of receptors, including the author’s recent work on triple-color single-molecule imaging of G protein-coupled receptors. Based on these researches, advantages and perspectives of the single-molecule imaging approach to solving the mechanisms of receptor functions are illustrated.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

The Potential of OMICs Technologies for the Treatment of Immune-Mediated Inflammatory Diseases

Immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel diseases and inflammatory arthritis (e.g., rheumatoid arthritis, psoriatic arthritis), are marked by increasing worldwide incidence rates. Apart from irreversible damage of the affected tissue, the systemic nature of these diseases heightens the incidence of cardiovascular insults and colitis-associated neoplasia. Only 40–60% of patients respond to currently used standard-of-care immunotherapies. In addition to this limited long-term effectiveness, all current therapies have to be given on a lifelong basis as they are unable to specifically reprogram the inflammatory process and thus achieve a true cure of the disease. On the other hand, the development of various OMICs technologies is considered as “the great hope” for improving the treatment of IMIDs. This review sheds light on the progressive development and the numerous approaches from basic science that gradually lead to the transfer from “bench to bedside” and the implementation into general patient care procedures.

Molecular Mechanisms of the Toll-Like Receptor, STING, MAVS, Inflammasome, and Interferon Pathways

Pattern recognition receptors (PRRs) form the front line of defense against pathogens. Many of the molecular mechanisms that facilitate PRR signaling have been characterized in detail, which is critical for the development of accurate PRR pathway models at the molecular interaction level. These models could support the development of therapeutics for numerous diseases, including sepsis and COVID-19. This review describes the molecular mechanisms of the principal signaling interactions of the Toll-like receptor, STING, MAVS, and inflammasome pathways. A detailed molecular mechanism network is included as Data Set S1 in the supplemental material.

A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (https://www.synmirapy.net/dc-optimization). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

Interferon-Mediated Long Non-Coding RNA Response in Macrophages in the Context of HIV

Interferons play a critical role in the innate immune response against a variety of pathogens, such as HIV-1. Recent studies have shown that long non-coding genes are part of a reciprocal feedforward/feedback relationship with interferon expression. They presumably contribute to the cell type specificity of the interferon response, such as the phenotypic and functional transition of macrophages throughout the immune response. However, no comprehensive understanding exists today about the IFN–lncRNA interplay in macrophages, also a sanctuary for latent HIV-1. Therefore, we completed a poly-A+ RNAseq analysis on monocyte-derived macrophages (MDMs) treated with members of all three types of IFNs (IFN-α, IFN-ε, IFN-γ or IFN-λ) and on macrophages infected with HIV-1, revealing an extensive non-coding IFN and/or HIV-1 response. Moreover, co-expression correlation with mRNAs was used to identify important (long) non-coding hub genes within IFN- or HIV-1-associated gene clusters. This study identified and prioritized IFN related hub lncRNAs for further functional validation.

Fatty acid nitroalkenes inhibit the inflammatory response to bleomycin-mediated lung injury

Fatty acid nitroalkenes are reversibly-reactive electrophiles, endogenously detectable at nM concentrations, displaying anti-inflammatory actions. Nitroalkenes like 9- or 10-nitro-octadec-9-enoic acid (e.g. nitro-oleic acid, OA-NO₂) pleiotropically suppress cardiovascular inflammatory responses, with pulmonary responses less well defined. C57BL/6 J male mice were intratracheally administered bleomycin (3 U/kg, ITB), to induce pulmonary inflammation and acute injury, or saline and were treated with 50 μL OA-NO₂ (50 μg) or vehicle in the same instillation and 72 h post-exposure to assess anti-inflammatory properties. Bronchoalveolar lavage (BAL) and lung tissue were collected 7d later. ITB mice lost body weight, with OA-NO₂ mitigating this loss (-2.3 ± 0.94 vs -0.4 ± 0.83 g). Histology revealed ITB induced cellular infiltration, proteinaceous debris deposition, and tissue injury, all significantly reduced by OA-NO₂. Flow cytometry analysis of BAL demonstrated loss of Siglec F⁺/F4/80⁺/CD45⁺ alveolar macrophages with ITB (89 ± 3.5 vs 30 ± 3.7%). Analysis of CD11b/CD11c expressing cells showed ITB-induced non-resident macrophage infiltration (4 ± 2.3 vs 43 ± 2.4%) was decreased by OA-NO₂ (24 ± 2.4%). Additionally, OA-NO₂ attenuated increases in mature, activated interstitial macrophages (23 ± 4.8 vs. 43 ± 5.4%) in lung tissue digests. Flow analysis of CD31^-/CD45^-/Sca-1⁺ mesenchymal cells revealed ITB increased CD44⁺ populations (1 ± 0.4 vs 4 ± 0.4MFI), significantly reduced by OA-NO₂ (3 ± 0.4MFI). Single cell analysis of mesenchymal cells by western blotting showed profibrotic ZEB1 protein expression induced by ITB. Lung digest CD45⁺ cells revealed ITB increased HMGB1⁺ cells, with OA-NO₂ suppressing this response. Inhibition of HMGB1 expression correlated with increased basal phospholipid production and SP-B expression in the lung lining. These findings indicate OA-NO₂ inhibits ITB-induced pro-inflammatory responses by modulating resident cell function.

A graphical and computational modeling platform for biological pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net-based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.

Long non-coding RNAs regulating macrophage functions in homeostasis and disease

Non-coding RNAs, once considered "genomic junk", are now known to play central roles in the dynamic control of transcriptional and post-transcriptional gene expression. Long non-coding RNAs (lncRNAs) are an expansive class of transcripts broadly described as greater than 200 nucleotides in length. While most lncRNAs are species-specific, their lack of conservation does not imbue a lack of function. LncRNAs have been found to regulate numerous diverse biological functions, including those central to macrophage differentiation and activation. Through their ability to form RNA-DNA, RNA-protein and RNA-RNA interactions, lncRNAs have been implicated in the regulation of myeloid lineage determination, and innate and adaptive immune functions, among others. In this review, we discuss recent advances, current challenges and future opportunities in understanding the roles of lncRNAs in macrophage functions in homeostasis and disease.

A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.