Enrico Capobianco

Protein networking: insights into global functional organization of proteomes.

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large‐scale protein‐protein interaction studies. A second concept is the protein‐signaling network, in which the nodes correspond to levels of post‐translationally modified forms of proteins and directed edges to causal effects through post‐translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.

Comorbidity: a multidimensional approach

Comorbidity represents an extremely complex domain of research. An individual entity, the patient, is the center of gravity of a system characterized by multiple, complex, and interrelated conditions, disorders, or diseases. Such complexity is influenced by uncertainty that is difficult to decipher and is proportional to the number of associated morbidities. Computational scientists usually provide meta-analysis studies aimed at integrating various types of evidence, but in our opinion they may help reformulate comorbidity by emphasizing, in particular, two aspects: (i) a systems approach, which allows for an ensemble view of comorbidity, and offers a model representation generalizable to multimorbidity; and (ii) a dynamic network inference approach, which is indicated for the analysis of links among morbidities and evaluation of risk. Notably, the main question remains whether such instruments suggest a shift of paradigm providing prospective impact on medical practice. We have identified in the simultaneous consideration of multiple dimensions linked to comorbidity complexity the rationale for such translation.

Separate and Combined Effects of DNMT and HDAC Inhibitors in Treating Human Multi-Drug Resistant Osteosarcoma HosDXR150 Cell Line

Understanding the molecular mechanisms underlying multi-drug resistance (MDR) is one of the major challenges in current cancer research. A phenomenon which is common to both intrinsic and acquired resistance, is the aberrant alteration of gene expression in drug-resistant cancers. Although such dysregulation depends on many possible causes, an epigenetic characterization is considered a main driver. Recent studies have suggested a direct role for epigenetic inactivation of genes in determining tumor chemo-sensitivity. We investigated the effects of the inhibition of DNA methyltransferase (DNMT) and hystone deacethylase (HDAC), considered to reverse the epigenetic aberrations and lead to the re-expression of de novo methylated genes in MDR osteosarcoma (OS) cells. Based on our analysis of the HosDXR150 cell line, we found that in order to reduce cell proliferation, co-treatment of MDR OS cells with DNMT (5-Aza-dC, DAC) and HDAC (Trichostatin A, TSA) inhibitors is more effective than relying on each treatment alone. In re-expressing epigenetically silenced genes induced by treatments, a very specific regulation takes place which suggests that methylation and de-acetylation have occurred either separately or simultaneously to determine MDR OS phenotype. In particular, functional relationships have been reported after measuring differential gene expression, indicating that MDR OS cells acquired growth and survival advantage by simultaneous epigenetic inactivation of both multiple p53-independent apoptotic signals and osteoblast differentiation pathways. Furthermore, co-treatment results more efficient in inducing the re-expression of some main pathways according to the computed enrichment, thus emphasizing its potential towards representing an effective therapeutic option for MDR OS.

Sub-modular resolution analysis by network mixture models.

Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of high-throughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions.

Distinct Transcriptomic Features are Associated with Transitional and Mature B-Cell Populations in the Mouse Spleen

Splenic transitional B-cells (T1 and T2) are selected to avoid self-reactivity and to safeguard against autoimmunity, then differentiate into mature follicular (FO-I and FO-II) and marginal zone (MZ) subsets. Transcriptomic analysis by RNA-seq of the five B-cell subsets revealed T1 cell signature genes included RAG suggesting a potential for receptor revision. T1 to T2 B-cell differentiation was marked by a switch from Myb to Myc, increased expression of the PI3K adapter DAP10 and MHC class II. FO-II may be an intermediate in FO-I differentiation and may also become MZ B-cells as suggested by principle component analysis. MZ B-cells possessed the most distinct transcriptome including down-regulation of CD45 phosphatase-associated protein (CD45-AP/PTPRC-AP), as well as upregulation of IL-9R and innate molecules TLR3, TLR7, and bactericidal Perforin-2 (MPEG1). Among the endosomal TLRs, stimulation via TLR3 further enhanced Perforin-2 expression exclusively in MZ B-cells. Using gene-deleted and overexpressing transgenic mice we show that IL-9/IL-9R interaction resulted in rapid activation of STAT1, 3, and 5, primarily in MZ B-cells. Importantly, CD45-AP mutant mice had reduced transitional and increased mature MZ and FO B-cells, suggesting that it prevents premature entry of transitional B-cells to the mature B-cell pool or their survival and proliferation. Together, these findings suggest, developmental plasticity among splenic B-cell subsets, potential for receptor revision in peripheral tolerance whereas enhanced metabolism coincides with T2 to mature B-cell differentiation. Further, unique core transcriptional signatures in MZ B-cells may control their innate features.Splenic transitional B-cells (T1 and T2) are selected to avoid self-reactivity and to safeguard against autoimmunity, then differentiate into mature follicular (FO-I and FO-II) and marginal zone (MZ) subsets. Transcriptomic analysis by RNA-seq of the five B-cell subsets revealed T1 cell signature genes included RAG suggesting a potential for receptor revision. T1 to T2 B-cell differentiation was marked by a switch from Myb to Myc, increased expression of the PI3K adapter DAP10 and MHC class II. FO-II may be an intermediate in FO-I differentiation and may also become MZ B-cells as suggested by principle component analysis. MZ B-cells possessed the most distinct transcriptome including down-regulation of CD45 phosphatase-associated protein (CD45-AP/PTPRC-AP), as well as upregulation of IL-9R and innate molecules TLR3, TLR7, and bactericidal Perforin-2 (MPEG1). Among the endosomal TLRs, stimulation via TLR3 further enhanced Perforin-2 expression exclusively in MZ B-cells. Using gene-deleted and overexpressing transgenic mice we show that IL-9/IL-9R interaction resulted in rapid activation of STAT1, 3, and 5, primarily in MZ B-cells. Importantly, CD45-AP mutant mice had reduced transitional and increased mature MZ and FO B-cells, suggesting that it prevents premature entry of transitional B-cells to the mature B-cell pool or their survival and proliferation. Together, these findings suggest, developmental plasticity among splenic B-cell subsets, potential for receptor revision in peripheral tolerance whereas enhanced metabolism coincides with T2 to mature B-cell differentiation. Further, unique core transcriptional signatures in MZ B-cells may control their innate features.

Ten Challenges for Systems Medicine

Systems Medicine (Auffray et al., 2009, 2010) emphasizes the role of systems biology in medical/clinical applications. With the advent of new technologies, the “omics” explosion (i.e., next generation sequencing) and the induced changes from data-poor to data-rich applications (for instance related to high-content imaging, physiology, and structural biology) have established the necessity of a systems approach (Noble, 2008) not to be caught in the data deluge. The accumulation and variety of high-throughput evidences and studies have generated hypothesis-driven models and validations at a previously inconceivable scale. Correspondingly, the assembly of models tackling all the implied complexities suggests challenges for which no standard (e.g., specified according to assumptions) inference approaches currently exist. In response to such problems and uncertainties, both data-driven intensive applications and model-free or agnostic (non-parametric) inferences are re-defining bioinformatics/statistics pipelines and network model architectures. Computational tools will be designed to satisfy criteria of: (1) Efficiency in processing, mining, and analyzing sequencing data; in particular, parallel architectures and high performance computing will be necessary to address the current data volumes and complexities; (2) Flexibility in synergizing the “omics” fields with clinical, biological, and environmental information whose integrative nature will require network-centric knowledge representation systems (Pawson and Linding, 2008; Zanzoni et al., 2008; Barabasi et al., 2011) will be very important; (3) Accuracy in data post-processing by exploiting model checking through robust feature selection and accurate output annotation, including clinical samples and patients’ follow up information. The tasks required to satisfy such criteria are highly specific and technical, but show interrelationships that lead to systems approaches. From one hand, the components that need to be considered in such systems have heterogeneous features due to sample diversity acquired at data-poor (patients) and data-rich (cellular, imaging) resolutions, and require normalization to exploit complementary evidences (experimental, clinical, epidemiological, computational, simulation-based) and measurements (quantitative, environmental, perturbation-based). From another hand, a consensus concerning data collection and annotation is needed for comparative evaluations and assessment of data consistencies among studies and experiments. Systems medicine represents a mosaic of distinct and interconnected micro-systems allowing to infer the macro-systems dynamics and produce elements of synthesis such as signatures (Hood and Friend, 2011; Sung et al., 2012) and profiles originated by a variety of information sources and consequently characterized. For instance, disease networks have been discussed by Barabasi et al. (2011), while pathway analysis beyond “canonical pathways” (Califano et al., 2012) and conceived for monitoring and assessing the mechanisms of action of drugs by the identification of targets and biomarkers, could involve multiple differential conditions to evaluate responses at system’s level or at global network scale (protein–protein interaction, gene regulatory, microRNA-target etc.), including deviation from equilibrium and/or stability. In response to crucial bottlenecks in Systems Medicine, our contribution aims to point out 10 challenges that are going to characterize the field, and for which Figure ​Figure11 provides an ensemble view. Figure 1 Links between Challenges. A modularization of the Bioinformatics Infrastructure embedding integratively and significantly validated inferences will lead to a Systems Medicine Paradigm Shift.

Smart Cities, Big Data, and Communities: Reasoning From the Viewpoint of Attractors

In what sense is a city smart? There are established entities defining this rich area of cross-disciplinary studies, and they refer to social, technical, economic, and political factors that keep evolving, thus offering opportunities for constant refinement of the concept of smart city. The emerging properties are mostly contextual, and affect urban data types and their capacity to form complex information systems. A well-known problem in computational analysis is the integration of lot of generated data. The heterogeneity and diversity of smart city data sources suggest that a system’s approach could be ideal to assemble drivers of multiple forces and dynamics, suggesting adaptive solutions too. However, the nature of such systems is quite unpredictable and chaotic, leading to the natural aim of stabilizing them. Studies have proposed methods based on various criteria, say parametric, entropic, anthropic etc. As many factors and variables underlie the system’s drivers, attractors derived from dynamical systems are proposed to describe smart city contexts through the various interlinked big data and networks.

Integrative analysis of cancer imaging readouts by networks.

Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact.

Empowering Spot Detection in 2DE Images by Wavelet Denoising

Typical high-abundant proteins, including albumin, IgG, IgA and others, are the target of depletion methods usually applied to two-dimensional electrophoresis (2DE) of human biological fluids like serum and plasma. Detection of low-abundant proteins is of interest with regard to biomarkers for disease when being studied by 2DE or liquid chromatography-mass spectrometry (LC/MS). After depletion of very abundant proteins, serum samples consist of an enriched pool of low-abundant proteins that can be further studied without significant interferences, thus allowing for a full identification of the low abundant proteins, whose spots become now more visible. We have employed wavelet-based techniques and their derived denoisers to explore 2DE from disease-control human samples. We have pursued the goal of mimicking in silico the spot detection performance experimentally obtained by depletion methods, thus hoping to read through the critical high-abundant protein regions. Our results suggest that an efficient and effective computational tool has been added to other ones performing 2DE image analysis, such as decomposition and segmentation, but with the advantage of being specifically targeted to the depletion task.

In vivo quantitation of metabolites with an incomplete model function

Metabolites can serve as biomarkers. Estimation of metabolite concentrations from an in vivo magnetic resonance spectroscopy (MRS) signal often uses a reference signal to estimate a model function of the spectral lineshape. When no reference signal is available, the a priori unknown in vivo lineshape must be inferred from the data at hand. This makes quantitation of metabolites from in vivo MRS signals a semi-parametric estimation problem which, in turn, implies setting of hyper-parameters by users of the software involved. Estimation of metabolite concentrations is usually done by nonlinear least-squares (NLLS) fitting of a physical model function based on minimizing the residue. In this work, the semi-parametric task is handled by complementing the usual criterion of minimal residue with a second criterion acting in tandem with it. This second criterion is derived from the general physical knowledge that the width of the line is limited. The limit on the width is a hyper-parameter; its setting appeared not critical so far. The only other hyper-parameter is the relative weight of the two criteria. But its setting too is not critical. Attendant estimation errors, obtained from a Monte Carlo calculation, show that the two-criterion NLLS approach successfully handles the semi-parametric aspect of metabolite quantitation.