Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

A new procedure to analyze RNA non-branching structures

RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer

CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules

Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks

Background: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. Results: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. Conclusions: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly

SAveRUNNER: an R-based tool for drug repurposing

Background: Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. Results: We developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods. Conclusions: The algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git, along with a comprehensive user guide

Validazione di un percorso diagnostico rapido per l'analisi del fenotipo di sensibilità dei batteri Gram negativi

The multidrug-resistant bacterial infections represent a global and complex problem because of the increase in their prevalence and the lack of new antibiotics. In this context, the antimicrobial stewardship programs are based on the collaboration between the infectious disease specialist and the microbiologist, which must merge their competencies to optimize antimicrobial therapy, implement surveillance and control the spread of MDR epidemic in health-care facilities. Moreover, they should create specific diagnostic procedures to obtain a prompt diagnosis of sepsis, with the species-identification of the pathogen and its sensitivity profile to antibiotics. Our research evaluated a new diagnostic approach for the early determination of the phenotypic antimicrobial susceptibility in Gram-negative bacteria. The first study aimed to validate an innovative rapid procedure for bacterial antibiogram in sepsis, which is 24 hours quicker than the traditional one. Nowadays indeed, the laboratory can use new technologies molecular biology techniques as a support in sepsis diagnostics; however, culture and microbroth diluition are still the gold standards in sepsis’ microbiological diagnosis. Unfortunately, they require a long time to be finalized, and this is more and more a severe limit since patients’ survival depends on a rapid and correct therapy start. Many authors studied new pathways of identification of resistances, but until now, nobody was able to establish a shared procedure based on evidence. For this reason, in the real-life laboratory, operational protocols are based on the responsibility and the expertise of the single professional. In our study, we included 145 strains of E. coli, K. pneumoniae and P. aeruginosa, with different phenotypic profiles. They were analyzed with both the gold standard method and the rapid one, comparing the values of Minimal Inhibitory Concentration. The procedure included haemocolture’s bottles inoculum, spot streaking on an agar plate, 5 hours incubation at 37°C, and finally microbroth diluition test preparation (Sensititre® method). MIC data and their interpretation (susceptible, intermediate or resistant) were collected and compared to ones obtained with the gold standard test, and agreement index was calculated together with sensitivity, specificity and prediction parameters. The aggregate data analysis showed a good performance, regarding precision, the Categorical Agreement (97.9%), the Essential Agreement (99.1%), the sensitivity (96%) and specificity (99%). Furthermore, the errors related to the evaluation of both the single antibiotic and the total of the MIC resulted in being acceptable. However, we observed the presence of some significant Very Major and Major Errors concerning Ampicillin/Sulbactam, Piperacillin/Tazobactam e Fosfomycin: these results suggested us to exclude the three drugs from the new rapid susceptibility test, without precluding the utility in guiding the so called “first line therapy”. In the second study, we evaluated the application of mass spectrometry to the rapid detection of quinolones’ resistance, with particular attention to the expression of the aminoglycoside acetyltransferase variant AAC(6’)-Ib-cr. We included in the study 72 strains of E. coli, which were spot streaked on an agar plate, incubated for 5 hours at 37°C with norfloxacin disk and finally collected for the MALDI-TOF analysis. For the study, we used the Saramis® software, Vitek® MS (Biomerieux), which allows examining separately every single detected spectrum. In particular, we investigated the presence of spectra related to the native and the acetylated norfloxacin spectra (“indirect” method) and the AAC(6’)-Ib-cr spectra (“direct” method). In both the cases, the analysis did not show satisfactory results, regarding both agreements with the reference test and sensitivity/specificity. In conclusion, our study demonstrated that the new rapid procedure for the bacterial antimicrobial susceptibility test has an acceptable agreement with the traditional one regarding the major part of the antibiotics that are used in practice as a first line therapy in sepsis. On the contrary, our method for the evaluation of quinolone with mass spectrometry did not show any clinical and microbiological viability. Further studies might be designed in this topic, but they should always consider the need to guarantee a precise and rapid result, together with a targeted diagnosis and care that never neglect expertise, caution, and clinical monitoring

SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.Comment: 42 pages, 9 figure

MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods

Laparoscopic treatment of internal hernia following minimally invasive left hemicolectomy

n/