Changes in blood epigenetic age have been associated with several pathological conditions and have recently been described to anticipate cancer development. In this work, we analyze a publicly available leukocytes methylation dataset to evaluate the relation between DNA methylation age and the prospective development of specific types of cancer. We calculated DNA methylation age acceleration using five state-of-the-art estimators 9three multi-site: Horvath, Hannum, Weidner; and two CpG specific: ELOV2 and FHL2) in a cohort including 845 subjects from the EPIC-Italy project and we compared 424 samples that remained cancer-free over the approximately ten years of follow-up with 235 and 166 subjects who developed breast and colorectal cancer, respectively. We show that the epigenetic age estimated from blood DNA methylation data is statistically significantly associated to future breast and male colorectal cancer development. These results are corroborated by survival analysis that shows significant association between age acceleration and cancer incidence suggesting that the chance of developing age-related diseases may be predicted by circulating epigenetic markers, with a dependence upon tumor type, sex and age estimator. These are encouraging results towards the non-invasive and perspective usage of epigenetic biomarkers.

Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recovers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.

This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical).

This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method.

eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition.

Additionally, it has also come to light an implication of the mitochondrial genome in the regulation of the intracellular epigenetic landscape, as demonstrated by the being itself object of epigenetic modifications. An increasing body of data is progressively indicating that the comprehension of the epigenetic landscape, actively integrated with the genetic elements, is crucial to delineate the molecular basis of the inter-individual complexity of ageing process. Indeed, it has emerged that DNA methylation changes occur during ageing, consisting mainly in a progressive process of genome demethylation, in a hypermethylation of gene-specific CpG dinucleotides, as well as in an inter-individual divergence of the epigenome due to stochastic events and environmental exposures throughout life, namely as epigenetic drift.

Degeneration is a hallmark of autoimmune diseases, whose incidence grows worldwide. Currenttherapies attempt to control the immune response to limit degeneration, commonly promotingimmunodepression. Differently, mechanical stimulation is known to trigger healing (regeneration) andit has recently been proposed locally for its therapeutic potential on severely injured areas. As the earlystages of healing consist of altered intra- and inter-cellular fluxes of soluble molecules, we explored thepotential of this early signal to spread, over time, beyond the stimulation district and become systemic,to impact on distributed or otherwise unreachable injured areas. We report in a model of arthritis inrats how stimulations delivered in the subcutaneous dorsal tissue result, over time, in the control andhealing of the degeneration of the paws' joints, concomitantly with the systemic activation of woundhealing phenomena in blood and in correlation with a more eubiotic microbiome in the gut intestinaldistrict.

To exploit this stability and reinforce it, we conducted a meta-analysis on publicly available DNA methylation datasets generated on: normal colorectal, adenoma (ADE) and adenocarcinoma (CRC) samples using the Illumina 450k array, in the systems medicine frame, searching for tumor gene episignatures, to produce a carefully selected list of potential drivers, markers and targets of the disease. The analysis proceeds from a differential meta-analysis of the methylation profiles using an analytical pipeline recently developed by our group [1], through network reconstruction, topological and functional analyses, to finally highlight relevant epigenomic features. Our results show that genes already highlighted for their genetic or transcriptional alteration in colorectal cancer are also differentially methylated, reinforcing-regardless of the level of cellular control-their role in the complex of alterations involved in tumorigenesis. Colorectal cancer is among the leading causes of cancer death worldwide. Despite numerous molecular characterizations of the phenomenon, the exact dynamics of its onset and progression remain elusive. Colorectal cancer onset has been characterized by changes in DNA methylation profiles, that, owing to the stability of their patterns, are promising candidates to shed light on the molecular events laying at the base of this phenomenon.

This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells.

Immunosenescence is thought to result from cellular aging and to reflect exposure to environmental stressors and antigens, including cytomegalovirus (CMV). However, not all of the features of immunosenescence are consistent with this view, and this has led to the emergence of the sister theory of "inflammaging". The recently discovered diffuse tissue distribution of resident memory T cells (TRM) which don't recirculate, calls these theories into question. These cells account for most T cells residing in barrier epithelia which sit in and travel through the extracellular matrix (ECM). With almost all studies to date carried out on peripheral blood, the age-related changes of the ECM and their consequences for T cell mobility, which is crucial for the function of these cells, have been largely ignored. We propose an update of the theoretical framework of immunosenescence, based on a novel hypothesis: the increasing stiffness and cross-linking of the senescent ECM lead to a progressive immunodeficiency due to an age-related decrease in T cell mobility and eventually the death of these cells. A key element of this mechanism is the mechanical stress to which the cell cytoplasm and nucleus are subjected during passage through the ECM. This hypothesis is based on an "evo-devo" perspective bringing together some major characteristics of aging, to create a single interpretive framework for immunosenescence.

Results: We propose to additionally evaluate a microbiome based on its global composition, by automatic annotation of pathogenic genera and statistical assessment of the net varied frequency of harmless versus harmful organisms. This application is intuitive, quantitative and computationally efficient and designed to cope with the currently incomplete species' functional knowledge. Our results, applied to human GI-microbiome data exemplify how this layer of information provides additional insights into treatments' impact on the GI microbiome, allowing to characterize a more physiologic effects of Prednisone versus Methotrexate, two treatments for rheumatoid arthritis (RA) a complex autoimmune systemic disease. Background: Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated. Typical metagenomic analyses are run to associate to a condition (disease, therapy, diet) a pool of bacteria, whose eubiotic/dysbiotic potential is assessed either by a-diversity, a measure of the varieties populating the microbiome, or by Firmicutes to Bacteroides ratio, associated to systemic inflammation, and finally by manual and direct inspection of bacteria's biological functions, when known. These approaches lead to results sometimes difficult to interpret in terms of the evolution towards a specific microbial composition, harmed by large areas of unknown.

Normalized mutual information (NMI) is a widely used metric for performance evaluation of community detection methods, recently proven to be affected by finite size effects. To overcome this issue, a metric called relative normalized mutual information (rNMI) has been proposed. However, we show here that rNMI is still a biased metric and may lead, under given circumstances, to erroneous conclusions. The bias is an effect of the so-called reverse finite size effect. We discuss different strategies to address this issue, and then propose a new metric, the corrected normalized mutual information (cNMI), symmetric and well normalized, in the form of empirical calculation and closed-form expression. The experiments show that cNMI not only removes the finite size effect of NMI but also the reverse finite size effect of rNMI, and is hence more suitable for performance evaluation of community detection methods and for other approaches typical of the more general clustering context.

Community structure is an important feature of networks, and the correct detection of communities is a fundamental problem in network analysis. Statistical inference has recently been proposed for successful detection, provided the number of communities can be appropriately estimated a priori. In the absence of such information, model selection by determination of the number of communities remains an issue. We show here that correlation between communities from a highly parceled partition can be used to estimate a narrow range of variation for the real number of communities. This range, further elaborated by modularity-based belief propagation, correctly identifies communities. Testing on synthetic networks generated by a stochastic block model and a set of real-world networks shows that our method can alleviate the effects of modularity fluctuations well and enhance the ability of community detection of the bare modularity-based belief propagation method.

Degeneration is a hallmark of autoimmune diseases, whose incidence grows worldwide. Current therapies attempt to control the immune response to limit degeneration, commonly promoting immunodepression. Differently, mechanical stimulation is known to trigger healing (regeneration) and it has recently been proposed locally for its therapeutic potential on severely injured areas. As the early stages of healing consist of altered intra-and inter-cellular fluxes of soluble molecules, we explored the potential of this early signal to spread, over time, beyond the stimulation district and become systemic, to impact on distributed or otherwise unreachable injured areas. We report in a model of arthritis in rats how stimulations delivered in the subcutaneous dorsal tissue result, over time, in the control and healing of the degeneration of the paws' joints, concomitantly with the systemic activation of wound healing phenomena in blood and in correlation with a more eubiotic microbiome in the gut intestinal district.

SBMLMechanotransduction Map

Stable, predictive biomarkers and interpretable disease signatures are seen as a signi cant step towards personalized medicine. In this per- spective, integration of multi-omic data com- ing from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strat- egy to reconstruct and analyse complex mul- ti-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data -although often publicly and freely available- lie in data- bases and repositories underutilised or not used at all. Issues coming from lack of stand- ardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as inter- twined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. Papers discuss data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of speci c diseases or in identifying candidate biomarkers to exploit the full bene t of multi-omic datasets and their intrinsic information content. Topics of interest include, but are not limited to: o Methods for the integration of layered data, including, but not limited to, genomics, transcrip- tomics, glycomics, proteomics, metabolomics; o Application of multi-omic data integration approaches for diagnostic biomarker discovery in any eld of the life sciences; o Innovative approaches for the analysis and the visualization of multi-omic datasets; o Methods and applications for systematic measurements from single/undivided samples (com- prising genomic, transcriptomic, proteomic, metabolomic measurements, among others); o Multi-scale approaches for integrated dynamic modelling and simulation; o Implementation of applications, computational resources and repositories devoted to data integration including, but not limited to, data warehousing, database federation, semantic integration, service-oriented and/or wiki integration; o Issues related to the de nition and implementation of standards, shared identities and seman- tics, with particular focus on the integration problem.

Results: We here present a molecular map of mechanotransduction, built in CellDesigner to warrant that maximum information is embedded in a compact network format. To validate the map's necessity we tested its redundancy in comparison with existing pathways, and to estimate its sufficiency, we quantified its ability to reproduce biological events with dynamic simulations, using Signaling Petri Networks. Motivation: Mechanotransduction-the ability to output a biochemical signal from a mechanical input-is related to the initiation and progression of a broad spectrum of molecular events. Yet, the characterization of mechanotransduction lacks some of the most basic tools as, for instance, it can hardly be recognized by enrichment analysis tools, nor could we find any pathway representation. This greatly limits computational testing and hypothesis generation on mechanotransduction biological relevance and involvement in disease or physiological mechanisms.

Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome. Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling. Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens. Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.

Performance of N-ary classification testing (expanding binary false positive/negative, true positive/negative)