Computational Techniques for the Structural and Dynamic Analysis of Biological Networks

The analysis of biological systems involves the study of networks from different omics such as genomics, transcriptomics, metabolomics and proteomics. In general, the computational techniques used in the analysis of biological networks can be divided into those that perform (i) structural analysis, (ii) dynamic analysis of structural prop- erties and (iii) dynamic simulation. Structural analysis is related to the study of the topology or stoichiometry of the biological network such as important nodes of the net- work, network motifs and the analysis of the flux distribution within the network. Dy- namic analysis of structural properties, generally, takes advantage from the availability of interaction and expression datasets in order to analyze the structural properties of a biological network in different conditions or time points. Dynamic simulation is useful to study those changes of the biological system in time that cannot be derived from a structural analysis because it is required to have additional information on the dynamics of the system. This thesis addresses each of these topics proposing three computational techniques useful to study different types of biological networks in which the structural and dynamic analysis is crucial to answer to specific biological questions. In particu- lar, the thesis proposes computational techniques for the analysis of the network motifs of a biological network through the design of heuristics useful to efficiently solve the subgraph isomorphism problem, the construction of a new analysis workflow able to integrate interaction and expression datasets to extract information about the chromo- somal connectivity of miRNA-mRNA interaction networks and, finally, the design of a methodology that applies techniques coming from the Electronic Design Automation (EDA) field that allows the dynamic simulation of biochemical interaction networks and the parameter estimation

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

: Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement

Automatic Parameterization of the Purine Metabolism Pathway through Discrete Event-based Simulation

Model development and analysis of metabolic networks is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulation of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Although qualitative characterizations of such complex mechanisms are, at least partially, available, a fully-parametrized quantitative description is often miss- ing. On the other hand, several characteristics and issues to model biological systems are common to the electronics system modelling, such as concurrency, reactivity, abstraction levels, automatic reverse engineering, as well as design space explosion during validation. This work presents a methodology that applies languages, techniques, and tools well established in the context of electronic design automation (EDA) for modelling and simulation of metabolic networks through Petri nets. The paper presents the results obtained by applying the proposed methodology to model the purine metabolism starting from the metabolomics data obtained from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders

Efficient Simulation and Parametrization of Stochastic Petri Nets in SystemC: A Case study from Systems Biology

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 appli- cations 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, thorough 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

Peripheral inflammation preceeding ischemia impairs neuronal survival through mechanisms involving miR‐127 in aged animals

Envelliment; Inflamació; MicroARNEnvejecimiento; Inflamación; MicroARNAging; Inflammation; MicroRNAIschemic stroke, the third leading cause of death in the Western world, affects mainly the elderly and is strongly associated with comorbid conditions such as atherosclerosis or diabetes, which are pathologically characterized by increased inflammation and are known to influence the outcome of stroke. Stroke incidence peaks during influenza seasons, and patients suffering from infections such as pneumonia prior to stroke exhibit a worse stroke outcome. Earlier studies have shown that comorbidities aggravate the outcome of stroke, yet the mediators of this phenomenon remain obscure. Here, we show that acute peripheral inflammation aggravates stroke‐induced neuronal damage and motor deficits specifically in aged mice. This is associated with increased levels of plasma proinflammatory cytokines, rather than with an increase of inflammatory mediators in the affected brain parenchyma. Nascent transcriptomics data with mature microRNA sequencing were used to identify the neuron‐specific miRNome, in order to decipher dysregulated miRNAs in the brains of aged animals with stroke and co‐existing inflammation. We pinpoint a previously uninvestigated miRNA in the brain, miR‐127, that is highly neuronal, to be associated with increased cell death in the aged, LPS‐injected ischemic mice. Target prediction tools indicate that miR‐127 interacts with several basally expressed neuronal genes, and of these we verify miR‐127 binding to Psmd3. Finally, we report reduced expression of miR‐127 in human stroke brains. Our results underline the impact of peripheral inflammation on the outcome of stroke in aged subjects and pinpoint molecular targets for restoring endogenous neuronal capacity to combat ischemic stroke.This study was supported by Emil Aaltonen Foundation, Academy of Finland and Finnish Cultural Foundation

Pre-diagnostic prognostic value of leukocytes count and neutrophil-to-lymphocyte ratio in patients who develop colorectal cancer

IntroductionEmerging evidence is pointing towards a relevant role of immunity in cancer development. Alterations in leukocytes count and neutrophil-to-lymphocyte ratio (NLR) at diagnosis of colorectal cancer (CRC) seems to predict poor prognosis, but no data is available for the pre-diagnostic values.MethodsRetrospective analysis of patients who underwent surgery for CRC at our center (2005 – 2020). 334 patients with a complete blood count dating at least 24 months prior to diagnosis were included. Changes in pre-diagnosis values of leukocytes (Pre-Leu), lymphocytes (Pre-Lymph), neutrophils (Pre-Neut), and NLR (Pre-NLR) and their correlation with overall- (OS) and cancer-related survival (CRS) were analyzed.ResultsPre-Leu, Pre-Neut and Pre-NLR showed an increasing trend approaching the date of diagnosis, while Pre-Lymph tended to decrease. The parameters were tested for associations with survival after surgery through multivariable analysis. After adjusting for potential confounding factors, Pre-Leu, Pre-Neut, Pre-Lymph and Pre-NLR resulted independent prognostic factors for OS and CRS. On sub-group analysis considering the interval between blood sampling and surgery, higher Pre-Leu, Pre-Neut, and Pre-NLR and lower Pre-Lymph were associated with worse CRS, and the effect was more evident when blood samples were closer to surgery.ConclusionTo our knowledge, this is the first study showing a significant correlation between pre-diagnosis immune profile and prognosis in CRC

Extracellular Vesicles Mediate Mesenchymal Stromal Cell-Dependent Regulation of B Cell PI3K-AKT Signaling Pathway and Actin Cytoskeleton

Mesenchymal stromal cells (MSCs) are adult, multipotent cells of mesodermal origin representing the progenitors of all stromal tissues. MSCs possess significant and broad immunomodulatory functions affecting both adaptive and innate immune responses once MSCs are primed by the inflammatory microenvironment. Recently, the role of extracellular vesicles (EVs) in mediating the therapeutic effects of MSCs has been recognized. Nevertheless, the molecular mechanisms responsible for the immunomodulatory properties of MSC-derived EVs (MSC-EVs) are still poorly characterized. Therefore, we carried out a molecular characterization of MSC-EV content by high-throughput approaches. We analyzed miRNA and protein expression profile in cellular and vesicular compartments both in normal and inflammatory conditions. We found several proteins and miRNAs involved in immunological processes, such as MOES, LG3BP, PTX3, and S10A6 proteins, miR-155-5p, and miR-497-5p. Different in silico approaches were also performed to correlate miRNA and protein expression profile and then to evaluate the putative molecules or pathways involved in immunoregulatory properties mediated by MSC-EVs. PI3K-AKT signaling pathway and the regulation of actin cytoskeleton were identified and functionally validated in vitro as key mediators of MSC/B cell communication mediated by MSC-EVs. In conclusion, we identified different molecules and pathways responsible for immunoregulatory properties mediated by MSC-EVs, thus identifying novel therapeutic targets as safer and more useful alternatives to cell or EV-based therapeutic approaches

PSEN1ΔE9, APPswe, and APOE4 Confer Disparate Phenotypes in Human iPSC-Derived Microglia

Summary Here we elucidate the effect of Alzheimer disease (AD)-predisposing genetic backgrounds, APOE4, PSEN1ΔE9, and APPswe, on functionality of human microglia-like cells (iMGLs). We present a physiologically relevant high-yield protocol for producing iMGLs from induced pluripotent stem cells. Differentiation is directed with small molecules through primitive erythromyeloid progenitors to re-create microglial ontogeny from yolk sac. The iMGLs express microglial signature genes and respond to ADP with intracellular Ca2+ release distinguishing them from macrophages. Using 16 iPSC lines from healthy donors, AD patients and isogenic controls, we reveal that the APOE4 genotype has a profound impact on several aspects of microglial functionality, whereas PSEN1ΔE9 and APPswe mutations trigger minor alterations. The APOE4 genotype impairs phagocytosis, migration, and metabolic activity of iMGLs but exacerbates their cytokine secretion. This indicates that APOE4 iMGLs are fundamentally unable to mount normal microglial functionality in AD.Peer reviewe

Interrupting the nitrosative stress fuels tumor-specific cytotoxic T lymphocytes in pancreatic cancer

BACKGROUND: Pancreatic 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). METHODS: We 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. RESULTS: PDAC 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. CONCLUSIONS: Tumor microenvironment reprogramming by ablating aberrant RNS production bypasses the current limits of immunotherapy in PDAC by overcoming immune resistance

LErNet: characterization of lncRNAs via context-aware network expansion and enrichment analysis

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