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Abstract Richter’s syndrome (RS) is the transformation of chronic lymphocytic leukemia (CLL) into a high-grade B-cell malignancy. Molecular and functional studies have pointed out that CLL cells are close to the apoptotic threshold and dependent on BCL-2 for survival. However, it remains undefined how evasion from apoptosis evolves during disease transformation. Here, we employed functional and static approaches to compare the regulation of mitochondrial apoptosis in CLL and RS. BH3 profiling of 17 CLL and 9 RS samples demonstrated that RS cells had reduced apoptotic priming and lower BCL-2 dependence than CLL cells. While a subset of RS was dependent on alternative anti-apoptotic proteins and was sensitive to specific BH3 mimetics, other RS cases harbored no specific anti-apoptotic addiction. Transcriptomics of paired CLL/RS samples revealed downregulation of pro-apoptotic sensitizers during disease transformation. Albeit expressed, effector and activator members were less likely to colocalize with mitochondria in RS compared to CLL. Electron microscopy highlighted reduced cristae width in RS mitochondria, a condition further promoting apoptosis resistance. Collectively, our data suggest that RS cells evolve multiple mechanisms that lower the apoptotic priming and shift the anti-apoptotic dependencies away from BCL-2, making direct targeting of mitochondrial apoptosis more challenging after disease transformation.

Summary: Response to multiple microenvironmental cues and resilience to mechanical stress are essential features of trafficking leukocytes. Here, we describe unexpected role of titin (TTN), the largest protein encoded by the human genome, in the regulation of mechanisms of lymphocyte trafficking. Human T and B lymphocytes express five TTN isoforms, exhibiting cell-specific expression, distinct localization to plasma membrane microdomains, and different distribution to cytosolic versus nuclear compartments. In T lymphocytes, the LTTN1 isoform governs the morphogenesis of plasma membrane microvilli independently of ERM protein phosphorylation status, thus allowing selectin-mediated capturing and rolling adhesions. Likewise, LTTN1 controls chemokine-triggered integrin activation. Accordingly, LTTN1 mediates rho and rap small GTPases activation, but not actin polymerization. In contrast, chemotaxis is facilitated by LTTN1 degradation. Finally, LTTN1 controls resilience to passive cell deformation and ensures T lymphocyte survival in the blood stream. LTTN1 is, thus, a critical and versatile housekeeping regulator of T lymphocyte trafficking.

Despite the promising results of new CFTR targeting drugs designed for the recovery of F508del- and class III variants activity, none of them have been approved for individuals with selected rare mutations, because uncharacterized CFTR variants lack information associated with the ability of these compounds in recovering their molecular defects. Here we used both rectal organoids (colonoids) and primary nasal brushed cells (hNEC) derived from a CF patient homozygous for A559T (c.1675G>A) variant to evaluate the responsiveness of this pathogenic variant to available CFTR targeted drugs that include VX-770, VX-809, VX-661 and VX-661 combined with VX-445. A559T is a rare mutation, found in African-Americans people with CF (PwCF) with only 85 patients registered in the CFTR2 database. At present, there is no treatment approved by FDA (U.S. Food and Drug Administration) for this genotype. Short-circuit current (Isc) measurements indicate that A559T-CFTR presents a minimal function. The acute addition of VX-770 following CFTR activation by forskolin had no significant increment of baseline level of anion transport in both colonoids and nasal cells. However, the combined treatment, VX-661-VX-445, significantly increases the chloride secretion in A559T-colonoids monolayers and hNEC, reaching approximately 10% of WT-CFTR function. These results were confirmed by forskolin-induced swelling assay and by western blotting in rectal organoids. Overall, our data show a relevant response to VX-661-VX-445 in rectal organoids and hNEC with CFTR genotype A559T/A559T. This could provide a strong rationale for treating patients carrying this variant with VX-661-VX-445-VX-770 combination.

Abstract Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.

The use of nanoparticles in medicine is sometimes hampered by their potential to activate immune cells, eliciting inflammation or allergy. We investigated whether magnetic nanoparticles (MNPs) or biomimetic magnetic nanoparticles (BMNPs) affect relevant activities of human monocytes. We found that the nanoparticles neither elicited the production of pro-inflammatory mediators IL-6 and TNFα by resting monocytes (when BMNP dose < 300 μg/mL) nor enhanced their secretion induced by R848, a molecule engaging virus-recognizing receptors, or bacterial lipopolysaccharide (LPS). MNPs and BMNPs neither induced the generation of reactive oxygen species (ROS), nor affected the ROS production elicited by the NADPH oxidase activator phorbol myristate acetate (PMA) or the fungal derivative β-glucan. BMNPs, but not MNPs, caused an up-regulation of the maturation markers CD80, CD83, and CD86 in immature monocyte-derived dendritic cells (DCs), whereas both nanoparticles did not affect the LPS-induced expression of these markers. Moreover, the nanoparticles were greedily ingested by monocytes and DCs without altering their viability. Therefore, these nanoparticles are candidates for medical applications because they do not activate pro-inflammatory activities of monocytes. Furthermore, their ability to stimulate DC maturation could be used for the design of vaccines. Moreover, harmlessly engulfed nanoparticles could be vehicles to carry molecules inside the immune cells to regulate the immune response.

Protein tyrosine phosphatase receptor gamma (PTPRG) is known to interact with and regulate several tyrosine kinases, exerting a tumor suppressor role in several type of cancers. Its wide expression in human tissues compared to the other component of group 5 of receptor phosphatases, PTPRZ expressed as a chondroitin sulfate proteoglycan in the central nervous system, has raised interest in its role as a possible regulatory switch of cell signaling processes. Indeed, a carbonic anhydrase-like domain (CAH) and a fibronectin type III domain are present in the N-terminal portion and were found to be associated with its role as [HCO3−] sensor in vascular and renal tissues and a possible interaction domain for cell adhesion, respectively. Studies on PTPRG ligands revealed the contactins family (CNTN) as possible interactors. Furthermore, the correlation of PTPRG phosphatase with inflammatory processes in different normal tissues, including cancer, and the increasing amount of its soluble form (sPTPRG) in plasma, suggest a possible role as inflammatory marker. PTPRG has important roles in human diseases; for example, neuropsychiatric and behavioral disorders and various types of cancer such as colon, ovary, lung, breast, central nervous system, and inflammatory disorders. In this review, we sum up our knowledge regarding the latest discoveries in order to appreciate PTPRG function in the various tissues and diseases, along with an interactome map of its relationship with a group of validated molecular interactors.

Oxyresveratrol, a polyphenol extracted from the plant Artocarpus lakoocha Roxb, has been reported to be an antioxidant and an oxygen-free radical scavenger. We investigated whether oxyresveratrol affects the generation of superoxide anion (O2−) by human monocytes, which are powerful reactive oxygen species (ROS) producers. We found that oxyresveratrol inhibited the O2− production induced upon stimulation of monocytes with β-glucan, a well known fungal immune cell activator. We then investigated whether the inclusion of oxyresveratrol into nanoparticles could modulate its effects on O2− release. We synthesized poly(lactic-co-glycolic acid) (PLGA) nanoparticles, and we assessed their effects on monocytes. We found that empty PLGA nanoparticles induced O2− production by resting monocytes and enhanced the formation of this radical in β-glucan-stimulated monocytes. Interestingly, the insertion of oxyresveratrol into PLGA nanoparticles significantly inhibited the O2− production elicited by unloaded nanoparticles in resting monocytes as well as the synergistic effect of nanoparticles and β-glucan. Our results indicate that oxyresveratrol is able to inhibit ROS production by activated monocytes, and its inclusion into PLGA nanoparticles mitigates the oxidative effects due to the interaction between these nanoparticles and resting monocytes. Moreover, oxyresveratrol can contrast the synergistic effects of nanoparticles with fungal agents that could be present in the patient tissues. Therefore, oxyresveratrol is a natural compound able to make PLGA nanoparticles more biocompatible.

Abstract Recent data highlight the presence, in HIV-1-seropositive patients with lymphoma, of p17 variants (vp17s) endowed with B-cell clonogenicity, suggesting a role of vp17s in lymphomagenesis. We investigated the mechanisms responsible for the functional disparity on B cells between a wild-type p17 (refp17) and a vp17 named S75X. Here, we show that a single Arginine (R) to Glycine (G) mutation at position 76 in the refp17 backbone (p17R76G), as in the S75X variant, is per se sufficient to confer a B-cell clonogenic potential to the viral protein and modulate, through activation of the PTEN/PI3K/Akt signaling pathway, different molecules involved in apoptosis inhibition (CASP-9, CASP-7, DFF-45, NPM, YWHAZ, Src, PAX2, MAPK8), cell cycle promotion and cancer progression (CDK1, CDK2, CDK8, CHEK1, CHEK2, GSK-3 beta, NPM, PAK1, PP2C-alpha). Moreover, the only R to G mutation at position 76 was found to strongly impact on protein folding and oligomerization by altering the hydrogen bond network. This generates a conformational shift in the p17 R76G mutant which enables a functional epitope(s), masked in refp17, to elicit B-cell growth-promoting signals after its interaction with a still unknown receptor(s). Our findings offer new opportunities to understand the molecular mechanisms accounting for the B-cell growth-promoting activity of vp17s.

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.

The increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node interference and robustness as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis.

In adult mammals, after optic nerve injury, retinal ganglion cells (RGCs) do not regenerate their axons and most of them die by apoptosis within a few days. Recently, several strategies that activate neuronal intracellular pathways were proposed to prevent such degenerative processes. The rho-related small GTPase Rac1 is part of a complex, still not fully understood, intracellular signaling network, mediating in neurons many effects, including axon growth and cell survival. However, its role in neuronal survival and regeneration in vivo has not yet been properly investigated. To address this point we intravitreally injected selective cell-penetrating Rac1 mutants after optic nerve crush and studied the effect on RGC survival and axonal regeneration. We injected two well-characterized L61 constitutively active Tat-Rac1 fusion protein mutants, in which a second F37A or Y40C mutation confers selectivity in downstream signaling pathways. Results showed that, 15 days after crush, both mutants were able to improve survival and to prevent dendrite degeneration, while the one harboring the F37A mutation also improved axonal regeneration. The treatment with F37A mutant for one month did not improve the axonal elongation respect to 15 days. Furthermore, we found an increase of Pak1 T212 phosphorylation and ERK1/2 expression in RGCs after F37A treatment, whereas ERK1/2 was more activated in glial cells after Y40C administration. Our data suggest that the selective activation of distinct Rac1-dependent pathways could represent a therapeutic strategy to counteract neuronal degenerative processes in the retina.

Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc.

HIV replication in mononuclear phagocytes is a multi-step process regulated by viral and cellular proteins with the peculiar feature of virion budding and accumulation in intra-cytoplasmic vesicles. Interaction of urokinase-type plasminogen activator (uPA) with its cell surface receptor (uPAR) has been shown to favor virion accumulation in such sub-cellular compartment in primary monocyte-derived macrophages and chronically infected promonocytic U1 cells differentiated into macrophage-like cells by stimulation with phorbol myristate acetate (PMA). By adopting this latter model system, we have here investigated which intracellular signaling pathways were triggered by uPA/uPAR interaction leading the redirection of virion accumulation in intra-cytoplasmic vesicles.uPA induced activation of RhoA, PKCδ and PKCε in PMA-differentiated U1 cells. In the same conditions, RhoA, PKCδ and PKCε modulated uPA-induced cell adhesion and polarization, whereas only RhoA and PKCε were also responsible for the redirection of virions in intracellular vesicles. Distribution of G and F actin revealed that uPA reorganized the cytoskeleton in both adherent and polarized cells. The role of G and F actin isoforms was unveiled by the use of cytochalasin D, a cell-permeable fungal toxin that prevents F actin polymerization. Receptor-independent cytoskeleton remodeling by Cytochalasin D resulted in cell adhesion, polarization and intracellular accumulation of HIV virions similar to the effects gained with uPA.These findings illustrate the potential contribution of the uPA/uPAR system in the generation and/or maintenance of intra-cytoplasmic vesicles that actively accumulate virions, thus sustaining the presence of HIV reservoirs of macrophage origin. In addition, our observations also provide evidences that pathways controlling cytoskeleton remodeling and activation of PKCε bear relevance for the design of new antiviral strategies aimed at interfering with the partitioning of virion budding between intra-cytoplasmic vesicles and plasma membrane in infected human macrophages.

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

The growing dimension and complexity of available experimental data generating biological networks has increased the need for tools allowing to categorize nodes by their topological relevance in biological networks. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes for the identification of the most important nodes of a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be also integrated with data sets from lab experiments, such as expression or phosphorylation levels of the proteins represented in the network, using the graphical features of the tool. This opens a new perspective in the analysis of biological networks, since integration of topological analysis with lab experimental data can increase the predictive power of a bioinformatical analysis.