Carlo Vittorio Cannistraci

From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

Specificity and transcriptional activity of microbiota associated with low and high microbial abundance sponges from the Red Sea

Marine sponges are generally classified as high microbial abundance (HMA) and low microbial abundance (LMA) species. Here, 16S rRNA amplicon sequencing was applied to investigate the diversity, specificity and transcriptional activity of microbes associated with an LMA sponge (Stylissa carteri), an HMA sponge (Xestospongia testudinaria) and sea water collected from the central Saudi Arabia coast of the Red Sea. Altogether, 887 068 denoised sequences were obtained, of which 806 661 sequences remained after quality control. This resulted in 1477 operational taxonomic units (OTUs) that were assigned to 27 microbial phyla. The microbial composition of S. carteri was more similar to that of sea water than to that of X. testudinaria, which is consistent with the observation that the sequence data set of S. carteri contained many more possibly sea water sequences (~24%) than the X. testudinaria data set (~6%). The most abundant OTUs were shared between all three sources (S. carteri, X. testudinaria, sea water), while rare OTUs were unique to any given source. Despite this high degree of overlap, each sponge species contained its own specific microbiota. The X. testudinaria‐specific bacterial taxa were similar to those already described for this species. A set of S. carteri‐specific bacterial taxa related to Proteobacteria and Nitrospira was identified, which are likely permanently associated with S. carteri. The transcriptional activity of sponge‐associated microorganisms correlated well with their abundance. Quantitative PCR revealed the presence of Poribacteria, representing typical sponge symbionts, in both sponge species and in sea water; however, low transcriptional activity in sea water suggested that Poribacteria are not active outside the host context.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding.

Motivation: Most functions within the cell emerge thanks to protein–protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable. Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions. Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction. Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com or timothy.ravasi@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online.

Identification and Predictive Value of Interleukin-6+ Interleukin-10+ and Interleukin-6− Interleukin-10+ Cytokine Patterns in ST-Elevation Acute Myocardial Infarction

Rationale: At the onset of ST-elevation acute myocardial infarction (STEMI), patients can present with very high circulating interleukin-6 (IL-6+) levels or very low-IL-6– levels. Objective: We compared these 2 groups of patients to understand whether it is possible to define specific STEMI phenotypes associated with outcome based on the cytokine response. Methods and Results: We compared 109 patients with STEMI in the top IL-6 level (median, 15.6 pg/mL; IL-6+ STEMI) with 96 in the bottom IL-6 level (median, 1.7 pg/mL; IL-6− STEMI) and 103 matched controls extracted from the multiethnic First Acute Myocardial Infarction study. We found minimal clinical differences between IL-6+ STEMI and IL-6− STEMI. We assessed the inflammatory profiles of the 2 STEMI groups and the controls by measuring 18 cytokines in blood samples. We exploited clustering analysis algorithms to infer the functional modules of interacting cytokines. IL-6+ STEMI patients were characterized by the activation of 2 modules of interacting signals comprising IL-10, IL-8, macrophage inflammatory protein-1&agr;, and C-reactive protein, and monocyte chemoattractant protein-1, macrophage inflammatory protein-1&bgr;, and monokine induced by interferon-&ggr;. IL-10 was increased both in IL-6+ STEMI and IL-6− STEMI patients compared with controls. IL-6+IL-10+ STEMI patients had an increased risk of systolic dysfunction at discharge and an increased risk of death at 6 months in comparison with IL-6−IL-10+ STEMI patients. We combined IL-10 and monokine induced by interferon-&ggr; (derived from the 2 identified cytokine modules) with IL-6 in a formula yielding a risk index that outperformed any single cytokine in the prediction of systolic dysfunction and death. Conclusions: We have identified a characteristic circulating inflammatory cytokine pattern in STEMI patients, which is not related to the extent of myocardial damage. The simultaneous elevation of IL-6 and IL-10 levels distinguishes STEMI patients with worse clinical outcomes from other STEMI patients. These observations could have potential implications for risk-oriented patient stratification and immune-modulating therapies.

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures—specifically dimension reduction (DR), coupled with clustering—provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. Methods: ‘Minimum Curvilinearity’ (MC) is a principle that—for small datasets—suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. Availability: https://sites.google.com/site/carlovittoriocannistraci/home Contact: kalokagathos.agon@gmail.com; massimo.alessio@hsr.it Supplementary information: Supplementary data are available at Bioinformatics online.

Identification and Predictive Value of IL6(+)IL10(+) and IL6(-)IL10(+) Cytokine Patterns in ST-Elevation Acute Myocardial Infarction

Rationale: At the onset of ST-elevation acute myocardial infarction (STEMI), patients can present with very high circulating interleukin-6 (IL-6+) levels or very low-IL-6– levels. Objective: We compared these 2 groups of patients to understand whether it is possible to define specific STEMI phenotypes associated with outcome based on the cytokine response. Methods and Results: We compared 109 patients with STEMI in the top IL-6 level (median, 15.6 pg/mL; IL-6+ STEMI) with 96 in the bottom IL-6 level (median, 1.7 pg/mL; IL-6− STEMI) and 103 matched controls extracted from the multiethnic First Acute Myocardial Infarction study. We found minimal clinical differences between IL-6+ STEMI and IL-6− STEMI. We assessed the inflammatory profiles of the 2 STEMI groups and the controls by measuring 18 cytokines in blood samples. We exploited clustering analysis algorithms to infer the functional modules of interacting cytokines. IL-6+ STEMI patients were characterized by the activation of 2 modules of interacting signals comprising IL-10, IL-8, macrophage inflammatory protein-1&agr;, and C-reactive protein, and monocyte chemoattractant protein-1, macrophage inflammatory protein-1&bgr;, and monokine induced by interferon-&ggr;. IL-10 was increased both in IL-6+ STEMI and IL-6− STEMI patients compared with controls. IL-6+IL-10+ STEMI patients had an increased risk of systolic dysfunction at discharge and an increased risk of death at 6 months in comparison with IL-6−IL-10+ STEMI patients. We combined IL-10 and monokine induced by interferon-&ggr; (derived from the 2 identified cytokine modules) with IL-6 in a formula yielding a risk index that outperformed any single cytokine in the prediction of systolic dysfunction and death. Conclusions: We have identified a characteristic circulating inflammatory cytokine pattern in STEMI patients, which is not related to the extent of myocardial damage. The simultaneous elevation of IL-6 and IL-10 levels distinguishes STEMI patients with worse clinical outcomes from other STEMI patients. These observations could have potential implications for risk-oriented patient stratification and immune-modulating therapies.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

The co‐transcriptome of uropathogenic Escherichia coli‐infected mouse macrophages reveals new insights into host–pathogen interactions

Urinary tract infections (UTI) are among the most common infections in humans. Uropathogenic Escherichia coli (UPEC) can invade and replicate within bladder epithelial cells, and some UPEC strains can also survive within macrophages. To understand the UPEC transcriptional programme associated with intramacrophage survival, we performed host–pathogen co‐transcriptome analyses using RNA sequencing. Mouse bone marrow‐derived macrophages (BMMs) were challenged over a 24 h time course with two UPEC reference strains that possess contrasting intramacrophage phenotypes: UTI89, which survives in BMMs, and 83972, which is killed by BMMs. Neither of these strains caused significant BMM cell death at the low multiplicity of infection that was used in this study. We developed an effective computational framework that simultaneously separated, annotated and quantified the mammalian and bacterial transcriptomes. Bone marrow‐derived macrophages responded to the two UPEC strains with a broadly similar gene expression programme. In contrast, the transcriptional responses of the UPEC strains diverged markedly from each other. We identified UTI89 genes up‐regulated at 24 h post‐infection, and hypothesized that some may contribute to intramacrophage survival. Indeed, we showed that deletion of one such gene (pspA) significantly reduced UTI89 survival within BMMs. Our study provides a technological framework for simultaneously capturing global changes at the transcriptional level in co‐cultures, and has generated new insights into the mechanisms that UPEC use to persist within the intramacrophage environment.

Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome.

Lipidomics of human blood plasma is an emerging biomarker discovery approach that compares lipid profiles under pathological and physiologically normal conditions, but how a healthy lipidome varies within the population is poorly understood. By quantifying 281 molecular species from 27 major lipid classes in the plasma of 71 healthy young Caucasians whose 35 clinical blood test and anthropometric indices matched the medical norm, we provided a comprehensive, expandable and clinically relevant resource of reference molar concentrations of individual lipids. We established that gender is a major lipidomic factor, whose impact is strongly enhanced by hormonal contraceptives and mediated by sex hormone-binding globulin. In lipidomics epidemiological studies should avoid mixed-gender cohorts and females taking hormonal contraceptives should be considered as a separate sub-cohort. Within a gender-restricted cohort lipidomics revealed a compositional signature that indicates the predisposition towards an early development of metabolic syndrome in ca. 25% of healthy male individuals suggesting a healthy plasma lipidome as resource for early biomarker discovery.

Questing for circadian dependence in ST-segment-elevation acute myocardial infarction: A multicentric and multiethnic study

Rationale: Four monocentric studies reported that circadian rhythms can affect left ventricular infarct size after ST-segment–elevation acute myocardial infarction (STEMI). Objective: To further validate the circadian dependence of infarct size after STEMI in a multicentric and multiethnic population. Methods and Results: We analyzed a prospective cohort of subjects with first STEMI from the First Acute Myocardial Infarction study that enrolled 1099 patients (ischemic time <6 hours) in Italy, Scotland, and China. We confirmed a circadian variation of STEMI incidence with an increased morning incidence (from 6:00 am till noon). We investigated the presence of circadian dependence of infarct size plotting the peak creatine kinase against time onset of ischemia. In addition, we studied the patients from the 3 countries separately, including 624 Italians; all patients were treated with percutaneous coronary intervention. We adopted several levels of analysis with different inclusion criteria consistent with previous studies. In all the analyses, we did not find a clear-cut circadian dependence of infarct size after STEMI. Conclusions: Although the circadian dependence of infarct size supported by previous studies poses an intriguing hypothesis, we were unable to converge toward their conclusions in a multicentric and multiethnic setting. Parameters that vary as a function of latitude could potentially obscure the circadian variations observed in monocentric studies. We believe that, to assess whether circadian rhythms can affect the infarct size, future study design should not only include larger samples but also aim to untangle the molecular time–dynamic mechanisms underlying such a relation.