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BACKGROUNDPatients with coronavirus disease 2019 (COVID-19) develop pneumonia generally associated with lymphopenia and a severe inflammatory response due to uncontrolled cytokine release. These mediators are transcriptionally regulated by the JAK/STAT signaling pathways, which can be disabled by small molecules.METHODSWe treated a group of patients (n = 20) with baricitinib according to an off-label use of the drug. The study was designed as an observational, longitudinal trial and approved by the local ethics committee. The patients were treated with 4 mg baricitinib twice daily for 2 days, followed by 4 mg per day for the remaining 7 days. Changes in the immune phenotype and expression of phosphorylated STAT3 (p-STAT3) in blood cells were evaluated and correlated with serum-derived cytokine levels and antibodies against severe acute respiratory syndrome-coronavirus 2 (anti-SARS-CoV-2). In a single treated patient, we also evaluated the alteration of myeloid cell functional activity.RESULTSWe provide evidence that patients treated with baricitinib had a marked reduction in serum levels of IL-6, IL-1β, and TNF-α, a rapid recovery of circulating T and B cell frequencies, and increased antibody production against the SARS-CoV-2 spike protein, all of which were clinically associated with a reduction in the need for oxygen therapy and a progressive increase in the P/F (PaO2, oxygen partial pressure/FiO2, fraction of inspired oxygen) ratio.CONCLUSIONThese data suggest that baricitinib prevented the progression to a severe, extreme form of the viral disease by modulating the patients' immune landscape and that these changes were associated with a safer, more favorable clinical outcome for patients with COVID-19 pneumonia.TRIAL REGISTRATIONClinicalTrials.gov NCT04438629.FUNDINGThis work was supported by the Fondazione Cariverona (ENACT Project) and the Fondazione TIM.

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors owing to its robust desmoplasia, low immunogenicity, and recruitment of cancer-conditioned, immunoregulatory myeloid cells. These features strongly limit the success of immunotherapy as a single agent, thereby suggesting the need for the development of a multitargeted approach. The goal is to foster T lymphocyte infiltration within the tumor landscape and neutralize cancer-triggered immune suppression, to enhance the therapeutic effectiveness of immune-based treatments, such as anticancer adoptive cell therapy (ACT).MethodsWe examined the contribution of immunosuppressive myeloid cells expressing arginase 1 and nitric oxide synthase 2 in building up a reactive nitrogen species (RNS)-dependent chemical barrier and shaping the PDAC immune landscape. We examined the impact of pharmacological RNS interference on overcoming the recruitment and immunosuppressive activity of tumor-expanded myeloid cells, which render pancreatic cancers resistant to immunotherapy.ResultsPDAC progression is marked by a stepwise infiltration of myeloid cells, which enforces a highly immunosuppressive microenvironment through the uncontrolled metabolism of L-arginine by arginase 1 and inducible nitric oxide synthase activity, resulting in the production of large amounts of reactive oxygen and nitrogen species. The extensive accumulation of myeloid suppressing cells and nitrated tyrosines (nitrotyrosine, N-Ty) establishes an RNS-dependent chemical barrier that impairs tumor infiltration by T lymphocytes and restricts the efficacy of adoptive immunotherapy. A pharmacological treatment with AT38 ([3-(aminocarbonyl)furoxan-4-yl]methyl salicylate) reprograms the tumor microenvironment from protumoral to antitumoral, which supports T lymphocyte entrance within the tumor core and aids the efficacy of ACT with telomerase-specific cytotoxic T lymphocytes.ConclusionsTumor microenvironment reprogramming by ablating aberrant RNS production bypasses the current limits of immunotherapy in PDAC by overcoming immune resistance.

Inflammatory responses rapidly detect pathogen invasion and mount a regulated reaction. However, dysregulated anti-pathogen immune responses can provoke life-threatening inflammatory pathologies collectively known as cytokine release syndrome (CRS), exemplified by key clinical phenotypes unearthed during the SARS-Cov-2 pandemic. The underlying pathophysiology of CRS remains elusive. We found that FLIP, a protein that controls caspase-8 death pathways, was highly expressed in myeloid cells of COVID-19 lungs. FLIP controlled CRS by fueling a STAT3-dependent inflammatory program. Indeed, constitutive expression of a viral FLIP homologue in myeloid cells triggered a STAT3-linked, progressive and fatal inflammatory syndrome in mice, characterized by elevated cytokine output, lymphopenia, lung injury and multiple organ dysfunctions that mimicked human CRS. As STAT3-targeting approaches relieved inflammation, immune disorders, and organ failures in these mice, targeted intervention towards this pathway could suppress the lethal CRS inflammatory state.One sentence summaryFLIP-expressing myeloid cells are key drivers of CRS through aberrant overexpression of STAT3 pathway. STAT3-targeting is effective in mitigating CRS like severe COVID-19.

Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic networks thanks to their natural expressiveness to represent metabolites, reactions, molecule interactions, and simulation randomness due to system fluctuations and environmental noise. In the literature, starting from the network model and the complete set of system parameters, there exist frameworks that allow for dynamic system simulation. Nevertheless, they do not allow for automatic model parameterization, which is a crucial task to identify, in silico, the network configurations that lead the model to satisfy specific temporal properties. To cover such a gap, this work first presents a framework to implement SPN models into SystemC code. Then, it shows how the framework allows for automatic parameterization of the networks. The user formally defines the network properties to be observed and the framework automatically extrapolates, through Assertion-based Verification (ABV), the parameter configurations that satisfy such properties. We present the results obtained by applying the proposed framework to model the complex metabolic network of the purine metabolism. We show how the automatic extrapolation of the system parameters allowed us to simulate the model under different conditions, which led to the understanding of behavioral differences in the regulation of the entire purine network. We also show the scalability of the approach through the modeling and simulation of four biological networks, each one with different structural characteristics.

Since the beginning of the SARS-CoV-2 pandemic, COVID-19 appeared as a unique disease with unconventional tissue and systemic immune features. Here we show a COVID-19 immune signature associated with severity by integrating single-cell RNA-seq analysis from blood samples and broncho-alveolar lavage fluids with clinical, immunological and functional ex vivo data. This signature is characterized by lung accumulation of naïve lymphoid cells associated with a systemic expansion and activation of myeloid cells. Myeloid-driven immune suppression is a hallmark of COVID-19 evolution, highlighting arginase-1 expression with immune regulatory features of monocytes. Monocyte-dependent and neutrophil-dependent immune suppression loss is associated with fatal clinical outcome in severe patients. Additionally, our analysis shows a lung CXCR6+ effector memory T cell subset is associated with better prognosis in patients with severe COVID-19. In summary, COVID-19-induced myeloid dysregulation and lymphoid impairment establish a condition of ‘immune silence’ in patients with critical COVID-19., Integrated studies of matched tissue sites and cell types in COVID-19 patients are important to define the immune mechanisms of pathology. Here, the authors describe an immune signature in fatal COVID-19 patients harmonizing single-cell RNA sequencing of blood and matched BAL cells with deep clinical, immunological and functional data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing pandemic coronavirus disease 2019 (COVID-19). The majority of patients with COVID-19 have a good prognosis, but variable percentages in different countries develop pneumonia associated with lymphocytopenia and severe inflammatory response due to uncontrolled release of cytokines. These immune mediators are transcriptionally regulated by JAK-STAT molecular pathways, which can be disabled by small molecules. Here, we provide evidences on the efficacy of baricitinib, a JAK1/JAK2 inhibitor, in correcting the immune abnormalities observed in patients hospitalized with COVID-19. Indeed, we demonstrate a significant reduction in serum levels of interleukin (IL)-6, IL-1β and tumor necrosis factor (TNF)α, a rapid recovery in circulating T and B cell frequencies and an increased antibody production against SARS-CoV-2 spike protein in baricitinib-treated patients. Moreover, treated patients underwent a rapid reduction in oxygen flow need and progressive increase in the P/F. Our work provides the basis on developing effective treatments against COVID-19 pathogenesis using on-target therapy.

This work presents a platform for the modelling, simulation and automatic parametrization of semi-quantitative metabolic networks. Starting from a network modelled through Petri Nets (PN) and represented in SBML, the platform converts the model into an internal representation implemented through an Electronic Design Automation (EDA) description language. It applies techniques and tools well established in the EDA field to simulate the model and to automate the network parametrization. We present the validation of the model simulation and of the parameters automatically extrapolated by the platform with the state of art modelling and simulation tools for PNs. The validation uses a real metabolic network and shows the platform opportunities and limitations in reproducing the experimental results, simulating the models in different conditions, and facilitating the analysis of the dynamics that regulate the network.

Stochastic Petri nets (SPN) are a form of Petri net where the transitions fire after a probabilistic and randomly determined delay. They are adopted in a wide range of applications thanks to their capability of incorporating randomness in the models and taking into account possible fluctuations and environmental noise. In Systems Biology, they are becoming a reference formalism to model metabolic networks, in which the noise due to molecule interactions in the environment plays a crucial role. Some frameworks have been proposed to implement and dynamically simulate SPN. Nevertheless, they do not allow for automatic model parametrization, which is a crucial task to identify the network configurations that lead the model to satisfy temporal properties of the model. This paper presents a framework that synthesizes the SPN models into SystemC code. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, through Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to implement and simulate a complex biological network, i.e., the purine metabolism, with the aim of reproducing the metabolomics data obtained in-vitro from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders.

Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in silico define and predict the roles of IncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of IncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered IncRNAs are not yet known. The results show both the outperformance of LErNet compared to enrichment approaches in literature and its robustness in case of partially missing context information. LErNet is provided as an R package. It is available at https://github.com/InfOmics/LErNet.

This chapter is devoted to illustrate the usage of state-of-the-art methodologies for miRNA regulatory network construction and analysis. Advantages in understanding the role of miRNAs in regulating gene expression are increasing the possibility of developing targeted therapies and drugs. This new possibility can be exploited by gaining new knowledge through analyzing interactions between a specific miRNA and a targeted gene.

Stochastic Petri Nets (SPN)are recognized as one of the standard formalisms to model metabolic networks. They allow incorporating randomness in the model and taking into account possible fluctuations and noise due to molecule interactions in the environment. Even though some frameworks have been proposed to implement and simulate SPN (e.g., Snoopy, Monalisa), they do not allow for automatic model parameterization, which is a crucial task to identify the network configurations that lead the model to satisfy certain biological properties. We present a framework to synthesize the SPN model of a metabolic network into executable code that can be simulated through a discrete event-based simulator. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, through Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to model the purine metabolism and to reproduce the metabolomics data obtained from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders. We show system parameterization extrapolated by the framework to reproduce the experimental results and to simulate the model under different conditions.

This chapter is devoted to illustrate the usage of state-of-the-art methodologies for miRNA regulatory network construction and analysis. Advantages in understanding the role of miRNAs in regulating gene expression are increasing the possibility of developing targeted therapies and drugs. This new possibility can be exploited by gaining new knowledge through analyzing interactions between a specific miRNA and a targeted gene.