In this article we summarise present knowledge on the role of pro-inflammatory cytokines on chronic inflammation leading to organismal aging, a phenomenon we proposed to call "inflamm-aging". In particular, we review genetic data regarding polymorphisms of genes encoding for cytokines and proteins involved in natural immunity (such as Toll-like Receptors and Heat Shock Proteins) obtained from large population studies including young, old and very old people in good health status or affected by age-related diseases such as Alzheimer's Disease and Type II Diabetes. On the whole, despite some controversial results, the available data are in favour of the hypothesis that pro-inflammatory cytokines play an important role in aging and longevity. Further, we present a possible hypothesis to reconcile energetic dysfunction, including mitochondria, and inflamm-aging. New perspectives for future studies, including phylogenetic studies in animal models and in silico studies on mathematical and bioinformatic models inspired by the systems biology approach, are also proposed.

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases.

Coronavirus disease 2019 (COVID-19) death rate differs depending on sex: in Chinese confirmed cases, while the infection rate among men and women is similar, the death rate among men is 4.7% compared with 2.8% for women 1. Italian data are similar as the reported death rate in men is significantly higher than that in women, 16.6% vs. 9.1%, respectively. Moreover, preliminary data from Italian epidemics suggest also a significant sex difference in infection rate, being 52.5% in women and 47.5% in men (Integrated Surveillance on COVID19 epidemic in Italy published by Italian Institute of Health, April 28th 2020, https://www.epicentro.iss.it/coronavirus/bollettino/ Bollettino-sorveglianza-integrata-COVID-19_28-aprile_2020.pdf). So far, the mechanisms underlying the observed gender bias are not disclosed; however, some hypotheses can be put forward on the basis of current knowledge on gender differences in respiratory viral diseases.

Macrophages derived from monocyte precursors undergo specific polarization processes which are influenced by the local tissue environment: classically activated (M1) macrophages, with a pro-inflammatory activity and a role of effector cells in Th1 cellular immune responses, and alternatively activated (M2) macrophages, with anti-inflammatory functions and involved in immunosuppression and tissue repair. At least three different subsets of M2 macrophages, namely, M2a, M2b, and M2c, are characterized in the literature based on their eliciting signals. The activation and polarization of macrophages is achieved through many, often intertwined, signaling pathways. To describe the logical relationships among the genes involved in macrophage polarization, we used a computational modeling methodology, namely, logical (Boolean) modeling of gene regulation. We integrated experimental data and knowledge available in the literature to construct a logical network model for the gene regulation driving macrophage polarization to the M1, M2a, M2b, and M2c phenotypes. Using the software GINsim and BoolNet, we analyzed the network dynamics under different conditions and perturbations to understand how they affect cell polarization. Dynamic simulations of the network model, enacting the most relevant biological conditions, showed coherence with the observed behavior of in vivo macrophages. The model could correctly reproduce the polarization toward the four main phenotypes as well as to several hybrid phenotypes, which are known to be experimentally associated to physiological and pathological conditions. We surmise that shifts among different phenotypes in the model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the extremes of an uninterrupted sequence of states. Furthermore, model simulations suggest that anti-inflammatory macrophages are resilient to shift back to the pro-inflammatory phenotype.

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