Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algorithms have been proposed for an automatic PPI interpretation, at first by solely considering the network topology, and later by integrating Gene Ontology (GO) terms as node similarity attributes. Here we present MTGO - Module detection via Topological information and GO knowledge, a novel functional module identification approach. MTGO let emerge the bimolecular machinery underpinning PPI networks by leveraging on both biological knowledge and topological properties. In particular, it directly exploits GO terms during the module assembling process, and labels each module with its best fit GO term, easing its functional interpretation. MTGO shows largely better results than other state of the art algorithms (including recent GO-based ones) when searching for small or sparse functional modules, while providing comparable or better results all other cases. MTGO correctly identifies molecular complexes and literature-consistent processes in an experimentally derived PPI network of Myocardial infarction. A software version of MTGO is available freely for non-commercial purposes at https://gitlab.com/d1vella/MTGO.

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

Atrial fibrillation (AF) is a common arrhythmia mainly affecting the elderly population, which can lead to serious complications such as stroke, ischaemic attack and vascular dementia. These problems are caused by thrombi which mostly originate in the left atrial appendage (LAA), a small muscular sac protruding from left atrium. The abnormal heart rhythm associated with AF results in alterations in the heart muscle contractions and in some reshaping of the cardiac chambers. This study aims to verify if and how these physiological changes can establish hemodynamic conditions in the LAA promoting thrombus formation, by means of computational fluid dynamic (CFD) analyses. In particular, sinus and fibrillation contractility was replicated by applying wall velocity/motion to models based on healthy and dilated idealized shapes of the left atrium with a common LAA morphology. The models were analyzed and compared in terms of shear strain rate (SSR) and vorticity, which are hemodynamic parameters directly associated with thrombogenicity. The study clearly indicates that the alterations in contractility and morphology associated with AF pathologies play a primary role in establishing hemodynamic conditions which promote higher incidence of ischaemic events, consistently with the clinical evidence. In particular, in the analyzed models, the impairment in contractility determined a decrease in SSR of about 50%, whilst the chamber pathological dilatation contributed to a 30% reduction, indicating increased risk of clot formation. The equivalent rigid wall model was characterized by SSR values about one order of magnitude smaller than in the contractile models, and substantially different vortical behavior, suggesting that analyses based on rigid chambers, although common in the literature, are inadequate to provide realistic results on the LAA hemodynamics.

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The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at .

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BackgroundA large majority of thrombi causing ischemic complications under atrial fibrillation (AF) originate in the left atrial appendage (LAA), an anatomical structure departing from the left atrium, characterized by a large morphological variability between individuals. This work analyses the hemodynamics simulated for different patient-specific models of LAA by means of computational fluid–structure interaction studies, modeling the effect of the changes in contractility and shape resulting from AF.MethodsThree operating conditions were analyzed: sinus rhythm, acute atrial fibrillation, and chronic atrial fibrillation. These were simulated on four patient-specific LAA morphologies, each associated with one of the main morphological variants identified from the common classification: chicken wing, cactus, windsock, and cauliflower. Active contractility of the wall muscle was calibrated on the basis of clinical evaluations of the filling and emptying volumes, and boundary conditions were imposed on the fluid to replicate physiological and pathological atrial pressures, typical of the various operating conditions.ResultsThe LAA volume and shear strain rates were analyzed over time and space for the different models. Globally, under AF conditions, all models were well aligned in terms of shear strain rate values and predicted levels of risk. Regions of low shear rate, typically associated with a higher risk of a clot, appeared to be promoted by sudden bends and focused at the trabecule and the lobes. These become substantially more pronounced and extended with AF, especially under acute conditions.ConclusionThis work clarifies the role of active and passive contraction on the healthy hemodynamics in the LAA, analyzing the hemodynamic effect of AF that promotes clot formation. The study indicates that local LAA topological features are more directly associated with a thromboembolic risk than the global shape of the appendage, suggesting that more effective classification criteria should be identified.