Background: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. Conclusions: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM.

Unsupervised learning approaches are frequently employed to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups.We developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface.PyPI package: https://pypi.org/project/biconhttps://exbio.wzw.tum.de/biconSupplementary data are available at Bioinformatics online.

Commento su Dimorfismo sessuale e suscettibilità al programma di morte cellulare per la newsletter di Medicina di Genere dell'ISS.

Coronavirus disease 2019 (COVID-19) death rate differs depending on sex. Some hypotheses can be put forward on the basis of current knowledge on gender differences in respiratory viral diseases.

The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes.EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen.Supplementary data are available at Bioinformatics online.

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients.

L'idea imprenditoriale da cui prende origine la start-up ProNeuro, nasce come conseguenza del lavoro di ricerca svolto dai soci fondatori presso il Consiglio Nazionale delle Ricerche (CNR). Questo lavoro ha portato negli ultimi 3 anni al deposito di due domande di brevetto italiano, di cui una già estesa in PCT, che proteggono l'utilizzo della molecola ProNGF-A per scopi terapeutici mirati alla cura di patologie neurologiche e infiammatorie (domanda di brevetto Nr. 102018000003279 del 05/03/2018 e PCT/IB2019/051753 del 05/03/2019) e la produzione di una forma mutata di ProNGF-A e il suo utilizzo per terapia neurologica e di patologie cutanee (domanda di brevetto numero 102019000014646 del 12/08/2019). Tali brevetti sono di proprietà del CNR, mentre ProNeuro ha messo a punto un sistema di offerta finalizzato alla loro valorizzazione. Attraverso attività di Ricerca e Sviluppo, ProNeuro individua principi attivi farmacologici con attività protettiva e riparativa per il sistema nervoso, ne modifica la struttura per renderli maggiormente efficaci, sicuri e biocompatibili, mette a punto i metodi produttivi ed esegue le prime fasi di caratterizzazione dei loro effetti, prima di proporli ad aziende farmaceutiche per un successivo sviluppo come farmaci destinati al mercato. ProNeuro commercializza, quindi, i diritti di utilizzo della proprietà intellettuale e una serie di prodotti collegati alle attività di discovery, produzione (trasferimento tecnologico) e prima validazione sia predittiva che biologica di nuovi neurofarmaci. ProNeuro avrà la forma giuridica di Società a responsabilità limitata e si configura come spin-off CNR. Come tale, il rapporto tra la società ProNeuro e il CNR è regolato dal "Regolamento per la costituzione e la partecipazione del CNR alle Imprese Spin off, Del,18/2019". I brevetti sopracitati, attualmente di proprietà del CNR, verranno concessi in licenza a ProNeuro, con possibilità di sub-licenziare a terzi, sulla base del suddetto Regolamento. Questo prevede, infatti, la cessione a condizioni agevolate delle licenze sui brevetti di proprietà CNR, la messa a disposizione di risorse logistiche e strumentali in fase di start-up e l'autorizzazione al proprio personale a svolgere attività a favore delle spin-off, con copertura dei costi salariali per un terzo del tempo lavorativo per tre anni. La sede dell'impresa è stata individuata presso l'Istituto di Farmacologia Traslazionale del CNR, via del Fosso del Cavaliere 100, 00133 Roma

Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statis- tical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationally- cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

Presentazione delle attività di ricerca su computational immunology e network medicine al workshop "Hot Topics in Systems" all'interno della conferenza PLACE2019

In the context of network medicine, disease genes, i.e. genes that have been experimentally associated to the onset or progression of a pathology, show a complex set of features that are not easily reduced to, and grasped by a simple network approach (e.g., studying centrality measures or clustering characteristics of the gene network). Here, to overcome such limitations and to exploit a larger set of informational attributes available, we analyze a sizeable integrated set of biological, ontological and topological features (including interaction data and GO categories, among others) related to different collections of disease genes (including, but not limited to sets related to several inflammatory and dysmetabolic diseases) via a comprehensive machine learning (ML) approach, in order to discover recurring patterns of attributes associated to families of disease genes. In this way the chances of revealing complex, hidden topological, ontological and statistical properties of the genes under scrutiny is wider and the derived "signature" can be heuristically used in a discovery process to find further yet unknown disease genes. We show hurdles, discriminating capabilities and main results in sorting out and in reconstructing the feature sets, in selecting the appropriate ML approach and in analyzing the datasets.

Network-based ranking methods (e.g. centrality analysis) have found extensive use in systems medicine for the prediction of essential proteins, for the prioritization of drug targets candidates in the treatment of several pathologies and in biomarker discovery, and for human disease genes identification. Here we propose to use critical nodes as defined by the Critical Node Problem for the analysis of key physiological and pathophysiological signaling pathways, as target candidates for treatment and management of several cancer types, neurologic and inflammatory dysfunctions, among others. We show how critical nodes allow to rank the importance of proteins in the pathways in a non-trivial way, substantially different from classical centrality measures. Such ranking takes into account the extent to which the network depends on its key players to maintain its cohesiveness and consistency, and coherently maps biologically relevant characteristics that can be critical in disease onset and treatments.

Background. Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim. In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results. The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

Sex dimorphism in cell response to stress has previously been investigated by different research groups. This dimorphism could be at least in part accounted for by sex-biased expression of regulatory elements such as microRNAs (miRs). In order to spot previously unknown miR expression differences we took advantage of prior knowledge on specialized databases to identify X chromosome-encoded miRs potentially escaping X chromosome inactivation (XCI). MiR-548am-5p emerged as potentially XCI escaper and was experimentally verified to be significantly up-regulated in human XX primary dermal fibroblasts (DFs) compared to XY ones. Accordingly, miR-548am-5p target mRNAs, e.g. the transcript for Bax, was differently modulated in XX and XY DFs. Functional analyses indicated that XY DFs were more prone to mitochondria-mediated apoptosis than XX ones. Experimentally induced overexpression of miR548am-5p in XY cells by lentivirus vector transduction decreased apoptosis susceptibility, whereas its down-regulation in XX cells enhanced apoptosis susceptibility. These data indicate that this approach could be used to identify previously unreported sex-biased differences in miR expression and that a miR identified with this approach, miR548am-5p, can account for sex-dependent differences observed in the susceptibility to mitochondrial apoptosis of human DFs.

The protozoan parasite Trypanosoma cruz causes the Chagas disease, which final outcome can be morbidity or death. The complexity of this infection is due to the many kinds of players involved in the immune response and to the variety of host cells targeted by the parasite. We built an ordinary differential equation model which includes aspects of innate and adaptive immune response to study the T. cruzi infection. The model also includes cardiomyocytes to represent how the infection affects the heart. We used parasitemia experimental data of infected wild-type mice to estimate the model parameters. We investigated how the number of parasites and infected cardiomyocytes were affected by changes of parameters controlling the survival rates of the parasite. We thus introduce a 20% variation in either macrophages, CDS+T cells, or anti- parasite specific antibody activity. This resulted in a change of the parasitemia as expected, and produced a broader variation in the number of parasites around the peak of parasitemia. Moreover, the same three model modifications were enabled one at a time to simulate a knockout effect in the host. The results of the knockout effects were a faster parasite growth and death of the host in all three cases, in agreement with in vivo experimental data. The model also is corroborated by in vivo data from the literature where the inhibition of macrophages, antibody, or CTL is not compensated by the other parasite killing mechanisms, and as a result lead to death of the host. Altogether these results indicate that the immune system plays a crucial role in controlling T. cruzi infection and impairment of one modality of action greatly reduces its efficiency and results in a much larger extensionof the infection of cardiomyocytes.

Infection by Leishmania can cause diseases ranging from self-healing cutaneous to visceral dissemination that can lead to death if untreated. In order to explore the early phase of the infection and the role of macrophages, we implement a system of differential equations involving the major players in the innate immune response to leishmaniasis (i.e., parasites in the intracellular and free form, infected and uninfected macrophages, and NO/ROS). The model was adjusted and validated using data from C57BL/6, KO and SCID mice published in the literature. The key findings were the surprisingly more active macrophages in the mice knockouts for IL-12 and IFN-g. This result can be interpreted as an indication of an M2b polarization of the macrophages in these mice. Sensitivity Analysis shows that NO/ROS secretion rate is more important to Leishmania control then the mechanisms of killing intracellular parasites. This model is a useful tool for comprehending the infection and treatments. Index Terms-leishmaniasis, cutaneous, innate-immune-response, macrophages, ODE

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 analysed 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 behaviour 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.

The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise. Built upon an existing model, it allows to detail better both subjects' characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered.