Systems medicine of inflammaging

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

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context

Inflammaging and Brain: Curcumin and Its Beneficial Potential as Regulator of Microglia Activation

Inflammaging is a term used to describe the tight relationship between low-grade chronic inflammation and aging that occurs during physiological aging in the absence of evident infection. This condition has been linked to a broad spectrum of age-related disorders in various organs including the brain. Inflammaging represents a highly significant risk factor for the development and progression of age-related conditions, including neurodegenerative diseases which are characterized by the progressive dysfunction and degeneration of neurons in the brain and peripheral nervous system. Curcumin is a widely studied polyphenol isolated from Curcuma longa with a variety of pharmacologic properties. It is well-known for its healing properties and has been extensively used in Asian medicine to treat a variety of illness conditions. The number of studies that suggest beneficial effects of curcumin on brain pathologies and age-related diseases is increasing. Curcumin is able to inhibit the formation of reactive-oxygen species and other pro-inflammatory mediators that are believed to play a pivotal role in many age-related diseases. Curcumin has been recently proposed as a potential useful remedy against neurodegenerative disorders and brain ageing. In light of this, our current review aims to discuss the potential positive effects of Curcumin on the possibility to control inflammaging emphasizing the possible modulation of inflammaging processes in neurodegenerative diseases

Entropy-Based Network Representation of the Individual Metabolic Phenotype

We approach here the problem of defining and estimating the nature of the metabolite-metabolite association network underlying the human individual metabolic phenotype in healthy subjects. We retrieved significant associations using an entropy-based approach and a multiplex network formalism. We defined a significantly over-represented network formed by biologically interpretable metabolite modules. The entropy of the individual metabolic phenotype is also introduced and discussed.</p

New Approaches for the Molecular Profiling of Human Cancers through Omics Data Analysis

In this thesis, we present three studies in which we applied ad hoc computational methods for the molecular profiling of human cancers using omics data. In the first study our main goal was to develop a pipeline of analysis able to detect a wide range of single nucleotide mutations with high validation rates. We combined two standard tools to create the GATK-LODN method, and we applied our pipeline to exome sequencing data of hematological and solid tumors. We created simulated datasets and performed experimental validation to test the pipeline sensitivity and specificity. In the second study we characterized the gene expression profiles of 11 tumor types aiming the discovery of multi-tumor drug targets and new strategies of drug combination and repurposing. We clustered tumors and applied a network-based analysis to integrate gene expression and protein interaction information. We defined three multi-tumor gene signatures, characterized by the following categories: NF-KB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis. We evaluated the gene signatures based on mutational, pharmacological and clinical evidences. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumor cell lines. In the third study we evaluated thyroid gene expression profiles of normal, Papillary Thyroid Carcinoma (PTC) and Anaplastic Thyroid Carcinoma (ATC) samples. The samples grouped in a progressional trend according to tissue type and the main biological processes affected in the normal to PTC transition were related to extracellular matrix and cell morphology; and those affected in the PTC to ATC transition were related to the control of cell cycle. We defined signatures related to each step of tumor progression and mapped the signatures onto protein-protein interaction and transcriptomical regulatory networks to prioritize genes for following experimental validation

Stochastic Modeling and Statistical Properties of Biological Systems Inferred from Omics Data

In this thesis we aim to describe the dynamic processes that govern the evolution of two very different ecological systems. First, we consider the ensemble of bacteria that populate the intestine (Gut Microbiota, GM), which has been proven to have great impact on human health, being associated to several metabolic and immunological diseases. Then, we deal with the set of protein domains enclosed in the genome of living organisms. In general, the neutrality hypothesis, that was proposed by Hubbell as the Ockham’s razor for ecology, is a respectable approximation for both the GM and the protein domains ecosystems. In the first case, a birth-death model that takes into account demographic noise is able to describe the population dynamics if we relax the neutrality assumption and consider two non-interacting niches in which species equivalence holds. Interestingly, the biodiversity index derived from our modeling predicts healthy aging with better accuracy than common indices. When constructing the empirical Relative Species Abundances distribution (RSA) for GM, a fundamental step regards the clustering of particular DNA sequences (16S rRNA). This is a critical task that enables to redefine the concept of species according to the phylogenetic tree. Here we introduce LOC-kNN, that is a parameter-free clustering algorithm recently developed by d’Errico et al, and we adapt it for this purpose. LOC-kNN detects clusters as density peaks based on the dataset topography and, besides still having difficulties in detecting small clusters, shows promising performances. Finally, for what concerns the protein domains ecosystem, environmental noise should also be taken into account. This has a multiplicative effect and, together with the introduction of the Gompertzian death hypothesis, predicts a Poisson Log-Normal RSA. The model fits well the protein domain RSA and captures the dynamics of genome evolution, manifesting good agreement with the phylogenetic distances among bacteria

Mathematical Physics Techniques for Omics Data Integration

Nowadays different types of high-throughput technologies allow us to collect information on the molecular components of biological systems. Each of such technologies is designed to simultaneously collect large sets of molecular data of a specific omic-kind. In order to draw a more comprehensive view of biological processes, experimental data made on different layers have to be integrated and analyzed. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make integrative analyses a challenge. Hence, the development of methods for omics integration is one of the most relevant problems computational scientists are addressing nowadays. The most representative and promising techniques for the analysis of omics data are presented and broadly divided into categories. In the literature we notice a growing interest around approaches that use graphs for modeling the relationships among omic variables. In particular we found that algorithms propagating molecular information on networks are being proposed in several applications and are often related to actual physical models. We considered the chemical master equation (CME) framework to model the exchange of information in biological networks as a stochastic process on the network. In this context we defined new algorithms and pipelines for the analysis of omics. In particular we propose two network-based methods with applications to both synthetic and prostate ardenocarcinoma data. In both the applications the molecular alterations are mapped on the protein-protein interaction network. In the first application we defined a novel methodology for extracting modules of connected genes that present the most significant differential molecular information between two classes of samples. In the second application we measure to which degree a distribution of deleterious molecular information on a given network deviates the normal trajectories of information flow using a perturbative approach to the CME