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 Tolllike 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 Alzheimers 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.

Background: The aging phenotype in humans is very heterogeneous and can be described as a complex mosaic resulting from the interaction of a variety of environmental, stochastic and genetic-epigenetic variables. Therefore, each old person must be considered as a singleton, and consequently the definition of ‘aging phenotype’ is very difficult. Objective: We discuss the phenotype of centenarians, the best example of successful aging, as well as other models exploited to study human aging and longevity, such as families enriched in long-living subjects, twins and cohorts of unrelated subjects. Methods: A critical review of literature available until March 2008. Conclusions: No single model can be considered the gold standard for the study of aging and longevity, instead the combination of results obtained from different models must be considered in order to better understand these complex phenomena. We propose that a systems biology concept such as that of ‘bow-tie’ architecture, useful for managing informa...

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. The sex different lifestyles, such as smoking addiction that is prevalent in men than in women, is considered one of the potential risk factor for developing pneumonia consequent to COVID-19 2. In addition, it is known that, in general, innate and immune responses are more intense and stronger in females than in males 3. This can provide women with a more effective weapon to fight new and infective pathogens, favouring viral clearance. However, further factors could be taken into account in order to explain the sex bias in COVID-19 death rates. In particular, the human angiotensin-converting enzyme 2 (ACE2), an essential enzyme of the renin-angiotensin system (RAS), is the functional receptor for the severe acute respiratory syndrome coronavirus (SARS-CoV) as well as for the recently identified SARS-CoV-2 4,5. It has been shown that ACE2 plays a protective role in chronic

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.

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information.