Systems medicine of inflammaging

Abstract

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

Keywords: ecological model; inflammation; multi-scale; multilayer networks; networks; propagation.

Figure 1.

SM as extension of SB. The SB basic cycle (red) is composed of Biological hypotheses, Technology (mainly devoted to omics measurements) and computational tools (statistical and modeling methods). The core SB cycle is then extended by increasing its complexity in a multi-scale way; starting from the cellular-subcellular domain, we reach the individual domain (the domain identified by a single patient and its internal structure in terms of organs and tissues). Finally, the higher domain is constituted by collection of patients and their interactions in an epidemiological context.

Figure 2.

Patient data space. Personalized medicine gathers together a huge amount of data characterizing a single patient (red shape). This integrated system can be interpreted as a network of networks. These integrated networks belong to different complexity layers, i.e. the omics layer (e.g. DNA and cells), the anatomical-functional layer (e.g. skeleton, circulatory system, nervous system, lymphatic system) and finally, the environmental layer (e.g. dietary habits, diseases and drugs, social behaviors and sports). In particular, the anatomical-functional scale is a spatial multiplex network, i.e. nodes can be considered as specific regions in space, connected by different functional links. A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.

Figure 3.

The unifying BioMedical hypothesis of Inflammation as a driver of inflammaging and age-related pathological processes. The propagating (bystander) character of inflammation and inflammaging is strictly related to the microenvironment, including the circulating one (e.g. the communicome, virome, mobilome and GM), and its suggested interpretation from an ecological point of view.

Figure 4.

RSA of one GM sample from Claesson et al. (gray histogram) fitted with a mixture of two Negative Binomials (black line). The RSA is a measure of biodiversity and is usually represented in the form of Preston Plot, plotting the number of species that have a certain number of individuals (in log 2). The neutral model proposed by Volkov et al. predicts a Negative Binomial distribution for the RSA and fits well the TE population. The GM RSA is rather fitted by a mixture of two Negative Binomials, meaning that a relaxation of the neutrality assumption is needed. The GM is thus well described by a hybrid niche-neutral model, in which two neutral niches are considered.

Figure 5.

Single-probe versus region-centric approaches for the analysis of DNA methylation microarrays. CpG DNA methylation measured by Infinium 450 k microarrays is expressed as a continuous value ranging from 0 (the CpG site is unmethylated in all the analyzed DNA molecules) to 1 (the CpG site is unmethylated in all the analyzed DNA molecules). In the analysis of differential methylation between two groups of samples, single-probe and region-centric approaches return different results. Single-probe analysis favors genomic regions like the one reported in box A, where a unique CpG site strongly differs in its methylation value between group A and group B. In the genomic region reported in box B, on the contrary, differences in DNA methylation values between groups A and B are smaller, but they involve several adjacent CpG sites; this configuration is preferentially identified by a region-centric approach. In the figure, CpG sites are represented as lollipops. A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.

Figure 6.

A representation of a generic multilayer network composed by two graphs: G₁ and G₂. The interlayer connections are in red, while the intralayer connections are in green for graph G₁ and in blue for graph G₂. The adjacency matrix of the related projection network proj(M) is displayed in the lower-right corner. A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.

Figure 7.

A possible multiplex architecture for SM: in a multiplex network, the same set of nodes (patients) has different types of interactions in each layer. Each layer corresponds to a given omics measurement, and a link might be a similarity measure between two people. We represent only few layers for the sake of simplicity. The bottom layer is divided into Genetics and Environment that may have a different role in the causation of a given phenotypical trait (e.g. clusters). A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.