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Succurro E, Vizza P, Cicone F, Cassano V, Massimino M, Giofrè F, Fiorentino TV, Perticone M, Sciacqua A, Guzzi PH, Veltri P, Andreozzi F, Cascini GL, Sesti G. Sex-specific differences in myocardial glucose metabolic rate in non-diabetic, pre-diabetic and type 2 diabetic subjects. Cardiovasc Diabetol 2024; 23:144. [PMID: 38671460 PMCID: PMC11055246 DOI: 10.1186/s12933-024-02246-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 04/22/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Evidence has shown that women with type 2 diabetes (T2DM) have a higher excess risk for cardiovascular disease (CVD) than men with T2DM. Subjects with either T2DM or prediabetes exhibit myocardial insulin resistance, but it is still unsettled whether sex-related differences in myocardial insulin resistance occur in diabetic and prediabetic subjects. METHODS We aimed to evaluate sex-related differences in myocardial glucose metabolic rate (MRGlu), assessed using dynamic PET with 18F-FDG combined with euglycemic-hyperinsulinemic clamp, in subjects with normal glucose tolerance (NGT; n = 20), prediabetes (n = 11), and T2DM (n = 26). RESULTS Women with prediabetes or T2DM exhibited greater relative differences in myocardial MRGlu than men with prediabetes or T2DM when compared with their NGT counterparts. As compared with women with NGT, those with prediabetes exhibited an age-adjusted 35% lower myocardial MRGlu value (P = 0.04) and women with T2DM a 74% lower value (P = 0.006), respectively. Conversely, as compared with men with NGT, men with T2DM exhibited a 40% lower myocardial MRGlu value (P = 0.004), while no significant difference was observed between men with NGT and prediabetes. The statistical test for interaction between sex and glucose tolerance on myocardial MRGlu (P < 0.0001) was significant suggesting a sex-specific association. CONCLUSIONS Our data suggest that deterioration of glucose homeostasis in women is associated with a greater impairment in myocardial glucose metabolism as compared with men. The sex-specific myocardial insulin resistance could be an important factor responsible for the greater effect of T2DM on the excess risk of cardiovascular disease in women than in men.
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Affiliation(s)
- Elena Succurro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy.
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy.
| | - Patrizia Vizza
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Velia Cassano
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Mattia Massimino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Federica Giofrè
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Maria Perticone
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Angela Sciacqua
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Computer Engineering, Electronics and Systems, University of Calabria, ModelingRende, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, 00189, Rome, Italy
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Vizza P, Aracri F, Guzzi PH, Gaspari M, Veltri P, Tradigo G. Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case. BMC Med Inform Decis Mak 2024; 24:93. [PMID: 38584282 PMCID: PMC11000316 DOI: 10.1186/s12911-024-02491-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024] Open
Abstract
Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.
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Affiliation(s)
- Patrizia Vizza
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Federica Aracri
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy.
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Græcia University, 88100, Catanzaro, Italy
| | - Marco Gaspari
- Department of Experimental and Clinical Medicine, Magna Græcia University, 88100, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Computers, Modeling, Electronics and Systems Engineering, University of Calabria, 87036, Rende, Italy
| | - Giuseppe Tradigo
- Department of Theoretical and Applied Sciences, eCampus University, 22060, Novedrate, CO, Italy
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Giancotti R, Lomoio U, Puccio B, Tradigo G, Vizza P, Torti C, Veltri P, Guzzi PH. The Omicron XBB.1 Variant and Its Descendants: Genomic Mutations, Rapid Dissemination and Notable Characteristics. Biology (Basel) 2024; 13:90. [PMID: 38392308 PMCID: PMC10886209 DOI: 10.3390/biology13020090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/24/2024]
Abstract
The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on spike protein structure on the subvariants of XBB, with particular attention to the velocity of variant diffusion and virus activity with respect to its diffusion. We examine the structural and functional distinctions of the variants in three different conformations: (i) spike glycoprotein in complex with ACE2 (1-up state), (ii) spike glycoprotein (closed-1 state), and (iii) S protein (open-1 state). We also estimate the affinity binding between the spike protein and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in preparing the immune system for virus variant recognition. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly.
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Affiliation(s)
- Raffaele Giancotti
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Ugo Lomoio
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Barbara Puccio
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | | | - Patrizia Vizza
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Carlo Torti
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Computer Engineering, Modelling, Electronics and System, University of Calabria, 87036 Rende, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
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Vizza P, Marotta N, Ammendolia A, Guzzi PH, Veltri P, Tradigo G. REHABS: An Innovative and User-Friendly Device for Rehabilitation. Bioengineering (Basel) 2023; 11:5. [PMID: 38275573 DOI: 10.3390/bioengineering11010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual's recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients' physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes.
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Affiliation(s)
- Patrizia Vizza
- Department of Medical and Surgical Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy
| | - Nicola Marotta
- Department of Clinical and Experimental Medicine, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy
| | - Antonio Ammendolia
- Department of Medical and Surgical Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy
| | | | - Giuseppe Tradigo
- Department of Theoretical and Applied Sciences, University e-Campus, 22060 Novedrate, Italy
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Gabriele C, Aracri F, Prestagiacomo LE, Rota MA, Alba S, Tradigo G, Guzzi PH, Cuda G, Damiano R, Veltri P, Gaspari M. Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables. Clin Proteomics 2023; 20:52. [PMID: 37990292 PMCID: PMC10662699 DOI: 10.1186/s12014-023-09439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/18/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. METHODS In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy. RESULTS Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). CONCLUSIONS To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.
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Affiliation(s)
- Caterina Gabriele
- Research Centre for Advanced Biochemistry and Molecular Biology, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy.
| | - Federica Aracri
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Licia Elvira Prestagiacomo
- Research Centre for Advanced Biochemistry and Molecular Biology, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | | | | | | | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Giovanni Cuda
- Research Centre for Advanced Biochemistry and Molecular Biology, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Rocco Damiano
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
- Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy
| | - Marco Gaspari
- Research Centre for Advanced Biochemistry and Molecular Biology, Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy.
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Lagzian A, Riseh RS, Sarikhan S, Ghorbani A, Khodaygan P, Borriss R, Guzzi PH, Veltri P. Genome mining conformance to metabolite profile of Bacillus strains to control potato pathogens. Sci Rep 2023; 13:19095. [PMID: 37925555 PMCID: PMC10625545 DOI: 10.1038/s41598-023-46672-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 11/03/2023] [Indexed: 11/06/2023] Open
Abstract
Biocontrol agents are safe and effective methods for controlling plant disease pathogens, such as Fusarium solani, which causes dry wilt, and Pectobacterium spp., responsible for potato soft rot disease. Discovering agents that can effectively control both fungal and bacterial pathogens in potatoes has always presented a challenge. Biological controls were investigated using 500 bacterial strains isolated from rhizospheric microbial communities, along with two promising biocontrol strains: Pseudomonas (T17-4 and VUPf5). Bacillus velezensis (Q12 and US1) and Pseudomonas chlororaphis VUPf5 exhibited the highest inhibition of fungal growth and pathogenicity in both laboratory (48%, 48%, 38%) and greenhouse (100%, 85%, 90%) settings. Q12 demonstrated better control against bacterial pathogens in vivo (approximately 50%). Whole-genome sequencing of Q12 and US1 revealed a genome size of approximately 4.1 Mb. Q12 had 4413 gene IDs and 4300 coding sequences, while US1 had 4369 gene IDs and 4255 coding sequences. Q12 exhibited a higher number of genes classified under functional subcategories related to stress response, cell wall, capsule, levansucrase synthesis, and polysaccharide metabolism. Both Q12 and US1 contained eleven secondary metabolite gene clusters as identified by the antiSMASH and RAST servers. Notably, Q12 possessed the antibacterial locillomycin and iturin A gene clusters, which were absent in US1. This genetic information suggests that Q12 may have a more pronounced control over bacterial pathogens compared to US1. Metabolic profiling of the superior strains, as determined by LC/MS/MS, validated our genetic findings. The investigated strains produced compounds such as iturin A, bacillomycin D, surfactin, fengycin, phenazine derivatives, etc. These compounds reduced spore production and caused deformation of the hyphae in F. solani. In contrast, B. velezensis UR1, which lacked the production of surfactin, fengycin, and iturin, did not affect these structures and failed to inhibit the growth of any pathogens. Our findings suggest that locillomycin and iturin A may contribute to the enhanced control of bacterial pectolytic rot by Q12.
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Affiliation(s)
- Arezoo Lagzian
- Department of Plant Protection, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Roohallah Saberi Riseh
- Department of Plant Protection, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Sajjad Sarikhan
- Molecular Bank, Iranian Biological Resource Center (IBRC), ACECR, Tehran, Iran
| | - Abozar Ghorbani
- Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute, Karaj, Iran.
| | - Pejman Khodaygan
- Department of Plant Protection, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Rainer Borriss
- Institute of Biology, Humboldt University Berlin, Berlin, Germany
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.
| | - Pierangelo Veltri
- Department of Informatics Modeling Electronics and System Engineering, University of Calabria, Calabria, Italy
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Tradigo G, Das JK, Vizza P, Roy S, Guzzi PH, Veltri P. Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future. Vaccines (Basel) 2023; 11:1496. [PMID: 37766172 PMCID: PMC10535057 DOI: 10.3390/vaccines11091496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.
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Affiliation(s)
- Giuseppe Tradigo
- Department of Computer Science, eCampus University, 22060 Novedrate, Italy;
| | - Jayanta Kumar Das
- Longitudinal Studies Section, Translation Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA;
| | - Patrizia Vizza
- Department of Surgical and Medical Science, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok 737102, India;
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Science, Magna Græcia University, 88100 Catanzaro, Italy;
| | - Pierangelo Veltri
- Department of Computer Science, Modelling, Electronics and Systems, University of Calabria, 87036 Rende, Italy;
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Mottaghi-Dastjerdi N, Ghorbani A, Montazeri H, Guzzi PH. A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis. BMC Gastroenterol 2023; 23:248. [PMID: 37482618 PMCID: PMC10364406 DOI: 10.1186/s12876-023-02891-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis. METHODS Gene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes. RESULTS Eleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages. CONCLUSIONS These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.
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Affiliation(s)
- Negar Mottaghi-Dastjerdi
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran.
| | - Abozar Ghorbani
- Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, Iran.
| | - Hamed Montazeri
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
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Lomoio U, Puccio B, Tradigo G, Guzzi PH, Veltri P. SARS-CoV-2 protein structure and sequence mutations: Evolutionary analysis and effects on virus variants. PLoS One 2023; 18:e0283400. [PMID: 37471335 PMCID: PMC10358949 DOI: 10.1371/journal.pone.0283400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023] Open
Abstract
The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises. However, there is the need for models and tools to study the links between the evolution of S protein sequence, structure and functions, and virus transmissibility and the effects of vaccination. As studies on S protein have been generated a large amount of relevant information, we propose in this work to use Protein Contact Networks (PCNs) to relate protein structures with biological properties by means of network topology properties. Topological properties are used to compare the structural changes with sequence changes. We find that both node centrality and community extraction analysis can be used to relate protein stability and functionality with sequence mutations. Starting from this we compare structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. Finally by applying our model to the Omicron variant we report a timeline correlation between Omicron and the vaccination campaign.
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Affiliation(s)
- Ugo Lomoio
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
| | - Barbara Puccio
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
| | | | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
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Milano M, Cinaglia P, Guzzi PH, Cannataro M. Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases. Life (Basel) 2023; 13:1520. [PMID: 37511895 PMCID: PMC10381714 DOI: 10.3390/life13071520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aβ plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a human ortholog. This study's goal consists of comparing PPI network of C. elegans and human by applying computational techniques, widely used for the analysis of PPI networks between species, such as Local Network Alignment (LNA). For this aim, we used L-HetNetAligner algorithm to build a local alignment among two PPI networks, i.e., C. elegans and human PPI networks associated with AD and PD built-in silicon. The results show that L-HetNetAligner can find local alignments representing functionally related subregions. In conclusion, since local alignment enables the extraction of functionally related modules, the method can be used to study complex disease progression.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Cinaglia
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Health Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
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11
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Guzzi PH, Cortese F, Mannino GC, Pedace E, Succurro E, Andreozzi F, Veltri P. Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities. Sci Rep 2023; 13:10372. [PMID: 37365269 DOI: 10.1038/s41598-023-37550-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/23/2023] [Indexed: 06/28/2023] Open
Abstract
The study of the relationship between type 2 diabetes mellitus (T2DM) disease and other pathologies (comorbidities), together with patient age variation, poses a challenge for medical research. There is evidence that patients affected by T2DM are more likely to develop comorbidities as they grow older. Variation of gene expression can be correlated to changes in T2DM comorbidities insurgence and progression. Understanding gene expression changes requires the analysis of large heterogeneous data at different scales as well as the integration of different data sources into network medicine models. Hence, we designed a framework to shed light on uncertainties related to age effects and comorbidity by integrating existing data sources with novel algorithms. The framework is based on integrating and analysing existing data sources under the hypothesis that changes in the basal expression of genes may be responsible for the higher prevalence of comorbidities in older patients. Using the proposed framework, we selected genes related to comorbidities from existing databases, and then analysed their expression with age at the tissues level. We found a set of genes that changes significantly in certain specific tissues over time. We also reconstructed the associated protein interaction networks and the related pathways for each tissue. Using this mechanistic framework, we detected interesting pathways related to T2DM whose genes change their expression with age. We also found many pathways related to insulin regulation and brain activities, which can be used to develop specific therapies. To the best of our knowledge, this is the first study that analyses such genes at the tissue level together with age variations.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy.
| | - Francesca Cortese
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
| | - Gaia Chiara Mannino
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
| | - Elisabetta Pedace
- Internal Medicine Unit, ASP Catanzaro, Soverato Hospital, Soverato, Italy
| | - Elena Succurro
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
- Internal Medicine Unit, R. Dulbecco Hospital, 88100, Catanzaro, Italy
| | - Francesco Andreozzi
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
- Internal Medicine Unit, R. Dulbecco Hospital, 88100, Catanzaro, Italy
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Roy S, Guzzi PH, Kalita J. Editorial: Graph representation learning in biological network. Front Bioinform 2023; 3:1222711. [PMID: 37359069 PMCID: PMC10289182 DOI: 10.3389/fbinf.2023.1222711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Affiliation(s)
- Swarup Roy
- Network Reconstruction & Analysis (NETRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Data Analytics Research Centre, Magna Graecia University, Catanzaro, Italy
| | - Jugal Kalita
- Department of Science, University of Colorado, Colorado Springs, CO, United States
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13
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Guzzi PH, di Paola L, Puccio B, Lomoio U, Giuliani A, Veltri P. Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks. Sci Rep 2023; 13:2837. [PMID: 36808182 PMCID: PMC9936485 DOI: 10.1038/s41598-023-30052-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/15/2023] [Indexed: 02/19/2023] Open
Abstract
The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy.
| | - Luisa di Paola
- grid.9657.d0000 0004 1757 5329Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, Universita Campus Bio-Medico di Roma, via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Barbara Puccio
- grid.411489.10000 0001 2168 2547Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Ugo Lomoio
- grid.411489.10000 0001 2168 2547Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Alessandro Giuliani
- grid.416651.10000 0000 9120 6856Environment and Health Department, Istituto Superiore di Sanita, Rome, Italy
| | - Pierangelo Veltri
- grid.411489.10000 0001 2168 2547Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy ,grid.7778.f0000 0004 1937 0319Department of Computer, Modeling, Electronics and System Engineering, University of Calabria, Rende, Italy
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Fragkiadaki S, Kontaxopoulou D, Stanitsa E, Angelopoulou E, Pavlou D, Šemrov D, Colnar S, Lustrek M, Blažica B, Vučica I, Matković R, Vukojevic K, Jelicic A, Guzzi PH, Martinović V, Medina AP, Piccoli G, Menon M, Kozetinac S, Miljković M, Kiskini C, Kokorotsikos T, Zilidou V, Radević I, Papatriantafyllou J, Thireos E, Tsouros A, Dimovski V, Papageorgiou SG. How Well Did the Healthcare System Respond to the Healthcare Needs of Older People with and without Dementia during the COVID-19 Pandemic? The Perception of Healthcare Providers and Older People from the SI4CARE Project in the ADRION Region. Geriatrics (Basel) 2023; 8:geriatrics8010021. [PMID: 36826363 PMCID: PMC9957093 DOI: 10.3390/geriatrics8010021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/23/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023] Open
Abstract
One major challenge during the COVID-19 pandemic was the limited accessibility to healthcare facilities, especially for the older population. The aim of the current study was the exploration of the extent to which the healthcare systems responded to the healthcare needs of the older people with or without cognitive impairment and their caregivers in the Adrion/Ionian region. Data were collected through e-questionnaires regarding the adequacy of the healthcare system and were anonymously administered to older individuals and stakeholder providers in the following countries: Slovenia, Italy (Calabria), Croatia, Bosnia and Herzegovina, Greece, Montenegro, and Serbia. Overall, 722 older people and 267 healthcare stakeholders participated in the study. During the COVID-19 pandemic, both healthcare stakeholders and the older population claimed that the healthcare needs of the older people and their caregivers increased dramatically in all countries, especially in Italy (Calabria), Croatia and BiH. According to our results, countries from the Adrion/Ionian regions faced significant challenges to adjust to the special needs of the older people during the COVID-19 pandemic, which was possibly due to limited accessibility opportunities to healthcare facilities. These results highlight the need for the development of alternative ways of providing medical assistance and supervision when in-person care is not possible.
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Affiliation(s)
- Stella Fragkiadaki
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Dionysia Kontaxopoulou
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Evangelia Stanitsa
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Efthalia Angelopoulou
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Dimosthenis Pavlou
- School of Topography and Geoinformatics, University of West Attica, Ag. Spyridonos Str., 12243 Aigalew, Greece
| | - Darja Šemrov
- Faculty of Civic and Geodetic Engineering, University of Ljubljana Jamova cesta 2, 1000 Ljubljana, Slovenia
| | - Simon Colnar
- School of Economics and Business, University of Ljubljana Kardeljeva ploščad 17, 1000 Ljubljana, Slovenia
| | - Mitja Lustrek
- Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Bojan Blažica
- Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Inga Vučica
- Department of Gerontology, Teaching Institute for Public Health of Split and Dalmatian County, Vukovarska 46, 21000 Split, Croatia
| | - Roberta Matković
- Department for Research Data Collecting and Analysis, Teaching Institute for Public Health of Split and Dalmatian County, Vukovarska 46, 21000 Split, Croatia
| | - Katarina Vukojevic
- Department of Anatomy, Histology and Embryology, University of Split School of Medicine, Šoltanska ul. 2, 21000 Split, Croatia
| | - Ana Jelicic
- Department of Anatomy, Histology and Embryology, University of Split School of Medicine, Šoltanska ul. 2, 21000 Split, Croatia
| | - Pietro Hiram Guzzi
- Municipality of Miglierina, Street B. Telesio 88040, Italy and University of Catanzaro, viale Europa, 88100 Catanzaro, Italy
| | - Vlatka Martinović
- Faculty of Medicine, University Hospital Mostar, 88000 Mostar, Bosnia and Herzegovina
| | - Amina Pekmez Medina
- Health Insurance and Reinsurance Fund of Federation of Bosnia and Herzegovina, Trg Heroja 14, 71000 Sarajevo, Bosnia and Herzegovina
| | - Guido Piccoli
- ALOT, SI4CARE-TEAM Street Cipro, 16, 25124 Brescia, Italy
| | | | - Srdjan Kozetinac
- Special Hospital Merkur, Cara Dusana 3, 36210 Vrnjaka Banja, Serbia
| | | | - Chrysanthi Kiskini
- Department of European Union, Projects of Regional Development Fund of Central Macedonia, Vas. Olgas 198, 54 655, Thessaloniki, Greece
| | - Themis Kokorotsikos
- Department of European Union, Projects of Regional Development Fund of Central Macedonia, Vas. Olgas 198, 54 655, Thessaloniki, Greece
| | - Vasiliki Zilidou
- Lab of Medical Physics & Digital Innovation, and Thessaloniki Active & Healthy Ageing Living Lab, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Ivan Radević
- Faculty of Economics, University of Montenegro, 37 Bulevar Jovana Tomaševića, 81000 Podgorica, Montenegro
| | - John Papatriantafyllou
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
| | - Eleftherios Thireos
- National Health System, Athens Medical Society, Meandrou 23, 115 28 Athens, Greece
| | - Agis Tsouros
- Department of Global Health, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA
| | - Vlado Dimovski
- School of Economics and Business, University of Ljubljana Kardeljeva ploščad 17, 1000 Ljubljana, Slovenia
| | - Sokratis G. Papageorgiou
- 1st Department of Neurology, Aiginition University Hospital, Vasilissis Sofias Street 72-74, 11528 Athens, Greece
- Correspondence:
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Succurro E, Cicone F, Papa A, Miceli S, Vizza P, Fiorentino TV, Perticone M, Sciacqua A, Guzzi PH, Veltri P, Cascini GL, Andreozzi F, Sesti G. Impaired insulin-stimulated myocardial glucose metabolic rate is associated with reduced estimated myocardial energetic efficiency in subjects with different degrees of glucose tolerance. Cardiovasc Diabetol 2023; 22:4. [PMID: 36624469 PMCID: PMC9827706 DOI: 10.1186/s12933-022-01733-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Alterations in myocardial mechano-energetic efficiency (MEEi), which represents the capability of the left ventricles to convert the chemical energy obtained by oxidative metabolism into mechanical work, have been associated with cardiovascular disease. Although whole-body insulin resistance has been related to impaired myocardial MEEi, it is unknown the relationship between cardiac insulin resistance and MEEi. Aim of this study was to evaluate the relationship between insulin-stimulated myocardial glucose metabolic rate (MrGlu) and myocardial MEEi in subjects having different degrees of glucose tolerance. METHODS We evaluated insulin-stimulated myocardial MrGlu using cardiac dynamic positron emission tomography (PET) with 18F-Fluorodeoxyglucose (18F-FDG) combined with euglycemic-hyperinsulinemic clamp, and myocardial MEEi in 57 individuals without history of coronary heart disease having different degrees of glucose tolerance. The subjects were stratified into tertiles according to their myocardial MrGlu values. RESULTS After adjusting for age, gender and BMI, subjects in I tertile showed a decrease in myocardial MEEi (0.31 ± 0.05 vs 0.42 ± 0.14 ml/s*g, P = 0.02), and an increase in myocardial oxygen consumption (MVO2) (10,153 ± 1375 vs 7816 ± 1229 mmHg*bpm, P < 0.0001) as compared with subjects in III tertile. Univariate correlations showed that insulin-stimulated myocardial MrGlu was positively correlated with MEEi and whole-body glucose disposal, and negatively correlated with waist circumference, fasting plasma glucose, HbA1c and MVO2. In a multivariate regression analysis running a model including several CV risk factors, the only variable that remained significantly associated with MEEi was myocardial MrGlu (β 0.346; P = 0.01). CONCLUSIONS These data suggest that an impairment in insulin-stimulated myocardial glucose metabolism is an independent contributor of depressed myocardial MEEi in subjects without history of CHD.
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Affiliation(s)
- Elena Succurro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy.
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy.
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Annalisa Papa
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Sofia Miceli
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Patrizia Vizza
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Maria Perticone
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Angela Sciacqua
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa, 88100, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, 00189, Rome, Italy
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Hosseinzadeh MM, Cannataro M, Guzzi PH, Dondi R. Temporal networks in biology and medicine: a survey on models, algorithms, and tools. Netw Model Anal Health Inform Bioinform 2022; 12:10. [PMID: 36618274 PMCID: PMC9803903 DOI: 10.1007/s13721-022-00406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 01/01/2023]
Abstract
The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.
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Affiliation(s)
| | - Mario Cannataro
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Riccardo Dondi
- Department of Literature, Philosophy, Communication Studies, University of Bergamo, Bergamo, Italy
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17
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Milano M, Guzzi PH, Cannataro M. Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks. Entropy (Basel) 2022; 24:1272. [PMID: 36141158 PMCID: PMC9497667 DOI: 10.3390/e24091272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Network alignment (NA) is a popular research field that aims to develop algorithms for comparing networks. Applications of network alignment span many fields, from biology to social network analysis. NA comes in two forms: global network alignment (GNA), which aims to find a global similarity, and LNA, which aims to find local regions of similarity. Recently, there has been an increasing interest in introducing complex network models such as multilayer networks. Multilayer networks are common in many application scenarios, such as modelling of relations among people in a social network or representing the interplay of different molecules in a cell or different cells in the brain. Consequently, the need to introduce algorithms for the comparison of such multilayer networks, i.e., local network alignment, arises. Existing algorithms for LNA do not perform well on multilayer networks since they cannot consider inter-layer edges. Thus, we propose local alignment of multilayer networks (MultiLoAl), a novel algorithm for the local alignment of multilayer networks. We define the local alignment of multilayer networks and propose a heuristic for solving it. We present an extensive assessment indicating the strength of the algorithm. Furthermore, we implemented a synthetic multilayer network generator to build the data for the algorithm's evaluation.
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18
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Guzzi PH, Di Paola L, Giuliani A, Veltri P. PCN-Miner: an open-source extensible tool for the analysis of Protein Contact Networks. Bioinformatics 2022; 38:4235-4237. [PMID: 35799364 DOI: 10.1093/bioinformatics/btac450] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 06/14/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches. RESULTS We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis. AVAILABILITY AND IMPLEMENTATION The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Luisa Di Paola
- Unit of Chemical-Physics Fundamentals in Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, 00161Rome, Italy
| | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
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19
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Ortuso F, Mercatelli D, Guzzi PH, Giorgi FM. Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex. J Biomol Struct Dyn 2022; 40:6545-6555. [PMID: 33583326 PMCID: PMC7885719 DOI: 10.1080/07391102.2021.1886175] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 02/01/2021] [Indexed: 01/08/2023]
Abstract
SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Francesco Ortuso
- Department of Health Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
- Net4Science srl, c/o University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Daniele Mercatelli
- Department of Surgical and Medical Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
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20
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Succurro E, Vizza P, Papa A, Cicone F, Monea G, Tradigo G, Fiorentino TV, Perticone M, Guzzi PH, Sciacqua A, Andreozzi F, Veltri P, Cascini GL, Sesti G. Metabolic Syndrome Is Associated With Impaired Insulin-Stimulated Myocardial Glucose Metabolic Rate in Individuals With Type 2 Diabetes: A Cardiac Dynamic 18F-FDG-PET Study. Front Cardiovasc Med 2022; 9:924787. [PMID: 35845046 PMCID: PMC9276995 DOI: 10.3389/fcvm.2022.924787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
Metabolic syndrome is a condition characterized by a clustering of metabolic abnormalities associated with an increased risk of type 2 diabetes and cardiovascular disease. An impaired insulin-stimulated myocardial glucose metabolism has been shown to be a risk factor for the development of cardiovascular disease in patients with type 2 diabetes. Whether cardiac insulin resistance occurs in subjects with metabolic syndrome remains uncertain. To investigate this issue, we evaluated myocardial glucose metabolic rate using cardiac dynamic 18F-FDG-PET combined with euglycemic-hyperinsulinemic clamp in three groups: a group of normal glucose tolerant individuals without metabolic syndrome (n = 10), a group of individuals with type 2 diabetes and metabolic syndrome (n = 19), and a group of subjects with type 2 diabetes without metabolic syndrome (n = 6). After adjusting for age and gender, individuals with type 2 diabetes and metabolic syndrome exhibited a significant reduction in insulin-stimulated myocardial glucose metabolic rate (10.5 ± 9.04 μmol/min/100 g) as compared with both control subjects (32.9 ± 9.7 μmol/min/100 g; P < 0.0001) and subjects with type 2 diabetes without metabolic syndrome (25.15 ± 4.92 μmol/min/100 g; P = 0.01). Conversely, as compared with control subjects (13.01 ± 8.53 mg/min x Kg FFM), both diabetic individuals with metabolic syndrome (3.06 ± 1.7 mg/min × Kg FFM, P = 0.008) and those without metabolic syndrome (2.91 ± 1.54 mg/min × Kg FFM, P = 0.01) exhibited a significant reduction in whole-body insulin-stimulated glucose disposal, while no difference was observed between the 2 groups of subjects with type 2 diabetes with or without metabolic syndrome. Univariate correlations showed that myocardial glucose metabolism was positively correlated with insulin-stimulated glucose disposal (r = 0.488, P = 0.003), and negatively correlated with the presence of metabolic syndrome (r = −0.743, P < 0.0001) and with its individual components. In conclusion, our data suggest that an impaired myocardial glucose metabolism may represent an early cardio-metabolic defect in individuals with the coexistence of type 2 diabetes and metabolic syndrome, regardless of whole-body insulin resistance.
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Affiliation(s)
- Elena Succurro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
- *Correspondence: Elena Succurro
| | - Patrizia Vizza
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Annalisa Papa
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Francesco Cicone
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Giuseppe Monea
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | | | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Maria Perticone
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Angela Sciacqua
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
- Research Center for the Prevention and Treatment of Metabolic Diseases (CR METDIS), University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, Rome, Italy
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21
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Hiram Guzzi P, Petrizzelli F, Mazza T. Disease spreading modeling and analysis: a survey. Brief Bioinform 2022; 23:6606045. [PMID: 35692095 DOI: 10.1093/bib/bbac230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization. RESULTS Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88110, Italy
| | - Francesco Petrizzelli
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
| | - Tommaso Mazza
- Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy
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22
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Petrizzelli F, Guzzi PH, Mazza T. Beyond COVID-19 Pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading. Comput Struct Biotechnol J 2022; 20:2664-2671. [PMID: 35664237 PMCID: PMC9135485 DOI: 10.1016/j.csbj.2022.05.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 12/12/2022] Open
Abstract
Paper discusses the relevance of the adoption of ad-hoc vaccination strategies. Paper shows how to evaluate the impact of different vaccination strategy by considering network-based models. Tailored interventions, e.g., vaccination, applied on central nodes of these networks may efficiently stop the propagation of an infection. The way node "centrality" is defined is the key to curb infection spreading.
The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks. We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at https://github.com/mazzalab/playgrounds.
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Affiliation(s)
- Francesco Petrizzelli
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Campus S Venuta, 88100, Italy
- Corresponding authors.
| | - Tommaso Mazza
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy
- Corresponding authors.
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23
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Gu S, Jiang M, Guzzi PH, Milenković T. Modeling multi-scale data via a network of networks. Bioinformatics 2022; 38:2544-2553. [PMID: 35238343 PMCID: PMC9048659 DOI: 10.1093/bioinformatics/btac133] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a 'network of networks' (NoNs) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e. than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. RESULTS For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important. AVAILABILITY AND IMPLEMENTATION The software and data are available at https://nd.edu/~cone/NoNs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shawn Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
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24
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Abstract
BACKGROUND Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. RESULTS We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. CONCLUSION The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | | | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
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25
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Das JK, Roy S, Guzzi PH. Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis. Infect Genet Evol 2021; 93:104921. [PMID: 34004362 PMCID: PMC8123524 DOI: 10.1016/j.meegid.2021.104921] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/04/2021] [Accepted: 05/07/2021] [Indexed: 02/07/2023]
Abstract
The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.
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Affiliation(s)
- Jayanta Kumar Das
- Department of Pediatrics, School of Medicine, Johns Hopkins University, MD, USA
| | - Swarup Roy
- Network Reconstruction & Analysis (NetRA) Lab, Department of Computer Applications, Sikkim University, Gangtok, India,Corresponding authors
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy,Corresponding authors
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26
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Mercatelli D, Pedace E, Veltri P, Giorgi FM, Guzzi PH. Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections. Comput Struct Biotechnol J 2021; 19:4092-4100. [PMID: 34306570 PMCID: PMC8271029 DOI: 10.1016/j.csbj.2021.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 12/15/2022] Open
Abstract
Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. While ageing has been extensively demonstrated to be accompanied by some modifications at the gene expression level, a possible link with COVID-19 manifestation still need to be investigated at the molecular level. Objectives: This study aims to shed out light on a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people. Methods: We considered public datasets of ageing-related genes and their expression at the tissue level. We selected human proteins interacting with viral ones that are known to be related to the ageing process. Finally, we investigated changes in the expression level of coding genes at the tissue, gender and age level. Results: We observed a significant intersection between some SARS-CoV-2 interactors and ageing-related genes, suggesting that those genes are particularly affected by COVID-19 infection. Our analysis evidenced that virus infection particularly involves ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. We found that HMGA1 and NPM1 have different expressions in the lung of males, while HMGA1, APEX1, CHEK1, EEF2, and NPM1 present changes in expression in males due to ageing effects. Conclusion: Our study generated a mechanistic framework to clarify the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. We also provide testable hypotheses for future investigation and pharmacological solutions tailored to specific age ranges.
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Affiliation(s)
| | | | - Pierangelo Veltri
- University of Catanzaro, Department of Medical and Surgical Sciences, Italy
| | | | - Pietro Hiram Guzzi
- University of Catanzaro, Department of Medical and Surgical Sciences, Italy
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27
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Agapito G, Pastrello C, Guzzi PH, Jurisica I, Cannataro M. BioPAX-Parser: parsing and enrichment analysis of BioPAX pathways. Bioinformatics 2021; 36:4377-4378. [PMID: 32437515 DOI: 10.1093/bioinformatics/btaa529] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 05/08/2020] [Accepted: 05/15/2020] [Indexed: 12/12/2022] Open
Abstract
SUMMARY Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, e.g. BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or stand-alone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform pathway enrichment analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap, we introduce BiP (BioPAX-Parser), an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with PEA by using information coming from pathways encoded in BioPAX. AVAILABILITY AND IMPLEMENTATION BiP is freely available for academic and non-profit organizations at https://gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giuseppe Agapito
- Department of Legal, Economic and Social Sciences.,Data Analytics Research Center, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Pietro Hiram Guzzi
- Data Analytics Research Center, Magna Graecia University of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Mario Cannataro
- Data Analytics Research Center, Magna Graecia University of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
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28
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Grillone K, Riillo C, Scionti F, Rocca R, Tradigo G, Guzzi PH, Alcaro S, Di Martino MT, Tagliaferri P, Tassone P. Non-coding RNAs in cancer: platforms and strategies for investigating the genomic "dark matter". J Exp Clin Cancer Res 2020; 39:117. [PMID: 32563270 PMCID: PMC7305591 DOI: 10.1186/s13046-020-01622-x] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 06/11/2020] [Indexed: 12/18/2022] Open
Abstract
The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the "dark matter" of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation.
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Affiliation(s)
- Katia Grillone
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Caterina Riillo
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Medical and Translational Oncology Units, AOU Mater Domini, 88100 Catanzaro, Italy
| | - Francesca Scionti
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Roberta Rocca
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Net4science srl, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Giuseppe Tradigo
- Laboratory of Bioinformatics, Department of Medical and Surgical Sciences, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Laboratory of Bioinformatics, Department of Medical and Surgical Sciences, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Stefano Alcaro
- Net4science srl, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Department of Health Sciences, Magna Græcia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
| | - Maria Teresa Di Martino
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Medical and Translational Oncology Units, AOU Mater Domini, 88100 Catanzaro, Italy
| | - Pierosandro Tagliaferri
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Medical and Translational Oncology Units, AOU Mater Domini, 88100 Catanzaro, Italy
| | - Pierfrancesco Tassone
- Laboratory of Translational Medical Oncology, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy
- Medical and Translational Oncology Units, AOU Mater Domini, 88100 Catanzaro, Italy
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29
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Agapito G, Settino M, Scionti F, Altomare E, Guzzi PH, Tassone P, Tagliaferri P, Cannataro M, Arbitrio M, Di Martino MT. DMET TM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine. High Throughput 2020; 9:ht9020008. [PMID: 32235355 PMCID: PMC7362183 DOI: 10.3390/ht9020008] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/05/2020] [Accepted: 03/24/2020] [Indexed: 12/30/2022] Open
Abstract
The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMETTM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMETTM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMETTM Platform).
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Affiliation(s)
- Giuseppe Agapito
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy; (G.A.); (M.S.); (P.H.G.); (M.C.)
| | - Marzia Settino
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy; (G.A.); (M.S.); (P.H.G.); (M.C.)
| | - Francesca Scionti
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy; (F.S.); (E.A.); (P.T.); (P.T.)
| | - Emanuela Altomare
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy; (F.S.); (E.A.); (P.T.); (P.T.)
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy; (G.A.); (M.S.); (P.H.G.); (M.C.)
| | - Pierfrancesco Tassone
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy; (F.S.); (E.A.); (P.T.); (P.T.)
| | - Pierosandro Tagliaferri
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy; (F.S.); (E.A.); (P.T.); (P.T.)
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy; (G.A.); (M.S.); (P.H.G.); (M.C.)
| | - Mariamena Arbitrio
- CNR-Institute for Biomedical Research and Innovation, 88100 Catanzaro, Italy
- Correspondence: (M.A.); (M.T.D.M.)
| | - Maria Teresa Di Martino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy; (F.S.); (E.A.); (P.T.); (P.T.)
- Correspondence: (M.A.); (M.T.D.M.)
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30
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Milano M, Milenković T, Cannataro M, Guzzi PH. L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks. Sci Rep 2020; 10:3901. [PMID: 32127586 PMCID: PMC7054427 DOI: 10.1038/s41598-020-60737-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 02/11/2020] [Indexed: 11/10/2022] Open
Abstract
Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. Therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. To the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code.
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Affiliation(s)
- Marianna Milano
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, 88040, Italy
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA
| | - Mario Cannataro
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, 88040, Italy
- Data Analytics Research Center, University of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, 88040, Italy.
- Data Analytics Research Center, University of Catanzaro, Catanzaro, Italy.
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Guzzi PH, Milenkovic T. Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin. Brief Bioinform 2019; 19:472-481. [PMID: 28062413 DOI: 10.1093/bib/bbw132] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Indexed: 12/23/2022] Open
Abstract
Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.
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Affiliation(s)
- Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University Magna Graecia, Catanzaro, 88100 Italy
| | - Tijana Milenkovic
- Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications (iCeNSA), ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA
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Gallo Cantafio ME, Grillone K, Caracciolo D, Scionti F, Arbitrio M, Barbieri V, Pensabene L, Guzzi PH, Di Martino MT. From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology. High Throughput 2018; 7:ht7040033. [PMID: 30373182 PMCID: PMC6306876 DOI: 10.3390/ht7040033] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 10/09/2018] [Accepted: 10/22/2018] [Indexed: 02/06/2023] Open
Abstract
Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.
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Affiliation(s)
- Maria Eugenia Gallo Cantafio
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
| | - Katia Grillone
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
| | - Daniele Caracciolo
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
| | - Francesca Scionti
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
| | | | - Vito Barbieri
- Medical Oncology Unit, Mater Domini Hospital, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
| | - Licia Pensabene
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, 88100 Catanzaro, Italy.
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy.
| | - Maria Teresa Di Martino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, 88100 Catanzaro, Italy.
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Vizza P, Tradigo G, Guzzi PH, Curia R, Sisca L, Aiello F, Fragomeni G, Cannataro M, Cascini GL, Veltri P. An Innovative Framework for Bioimage Annotation and Studies. Interdiscip Sci 2018; 10:544-557. [PMID: 29094319 DOI: 10.1007/s12539-017-0264-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 09/11/2017] [Accepted: 09/13/2017] [Indexed: 06/07/2023]
Abstract
The collection and analysis of clinical data are needed to investigate diseases and to define medical protocols and treatments. Bioimages, medical annotations and patient history are clinical data acquired and studied to perform a correct diagnosis and to propose an appropriate therapy. Currently, hospital departments manage these data using legacy systems which do not often allow data integration among different departments or health structures. Thus, in many cases clinical information sharing and exchange are difficult to implement. This is also the case for biomedical images for which data integration or data overlapping is usually not available. Image annotations and comparison can be crucial for physicians in many case studies. In this paper, a general purpose framework for bioimage management and annotations is proposed. Moreover, a simple-to-use information system has been developed to integrate clinical and diagnosis codes. The framework allows physicians (1) to integrate DICOM images from different platforms and (2) to report notes and highlights directly on images, thus offering, among the others, to query and compare similar clinical cases. This contribution is the result of a framework aimed to support oncologists in managing DICOM images and clinical data from different departments. Data integration is performed using a here-proposed XML-based module also utilized to trace temporal changes in image annotations.
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Affiliation(s)
- Patrizia Vizza
- Department of Surgical and Medical Science, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Tradigo
- Department of Computer, Modeling, Electronics and Systems Engineering, University of Calabria, Cosenza, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Science, Magna Graecia University, Catanzaro, Italy
| | | | | | | | - Gionata Fragomeni
- Department of Surgical and Medical Science, Magna Graecia University, Catanzaro, Italy
| | - Mario Cannataro
- Department of Surgical and Medical Science, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Pierangelo Veltri
- Department of Surgical and Clinical Science, University Magna Graecia of Catanzaro, Catanzaro, Italy.
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Agapito G, Guzzi PH, Cannataro M. A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis. High Throughput 2018; 7:ht7020017. [PMID: 29904017 PMCID: PMC6023446 DOI: 10.3390/ht7020017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 05/21/2018] [Accepted: 06/07/2018] [Indexed: 12/13/2022] Open
Abstract
Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP) genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions.
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Affiliation(s)
- Giuseppe Agapito
- Data Analytics Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.
| | - Pietro Hiram Guzzi
- Data Analytics Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.
| | - Mario Cannataro
- Data Analytics Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Viale Europa, 88100 Catanzaro, Italy.
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Milano M, Guzzi PH, Cannataro M. GLAlign: A Novel Algorithm for Local Network Alignment. IEEE/ACM Trans Comput Biol Bioinform 2018; 16:1958-1969. [PMID: 29993696 DOI: 10.1109/tcbb.2018.2830323] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Networks are successfully used as a modelling framework in many application domains. For instance, Protein-Protein Interaction Networks (PPINs) model the set of interactions among proteins in a cell. A critical application of network analysis is the comparison among PPINs of different organisms to reveal similarities among the underlying biological processes. Algorithms for comparing networks (also referred to as network aligners) fall into two main classes: global aligners, which aim to compare two networks on a global scale, and local aligners that evidence single sub-regions of similarity among networks. The possibility to improve the performance of the aligners by mixing the two approaches is a growing research area. In our previous work, we started to explore the possibility to use global alignment to improve the local one. We here examine further this possibility by using topological information extracted from global alignment to guide the steps of the local alignment. Therefore, we present GLAlign (Global Local Aligner), a methodology that improves the performances of local network aligners by exploiting a preliminary global alignment. Furthermore, we provide an implementation of GLAlign. As a proof-of-principle, we evaluated the performance of the GLAlign prototype. Results show that GLAlign methodology outperforms the state-of-the-art local alignment algorithms. GLAlign is publicly available for academic use and can be downloaded here: https://sites.google.com/site/globallocalalignment/.
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Scionti F, Di Martino MT, Sestito S, Nicoletti A, Falvo F, Roppa K, Arbitrio M, Guzzi PH, Agapito G, Pisani A, Riccio E, Concolino D, Pensabene L. Genetic variants associated with Fabry disease progression despite enzyme replacement therapy. Oncotarget 2017; 8:107558-107564. [PMID: 29296186 PMCID: PMC5746088 DOI: 10.18632/oncotarget.22505] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 10/29/2017] [Indexed: 01/29/2023] Open
Abstract
Enzyme replacement therapy (ERT) has been widely used for the treatment of Fabry disease, a rare X-linked recessive disorder due to absent or reduced activity of lysosomal enzyme α-galactosidase A. It is still unclear why some patients under ERT show disease progression typically with renal, cardiovascular and cerebrovascular dysfunctions. Here, we investigated the involvement of drug absorption, distribution, metabolism, and excretion gene variants in response variability to ERT, genotyping 37 patients with the Affymetrix Drug Metabolizing Enzyme and Transporters (DMET) Plus microarray. We found three single nucleotide polymorphisms in human alcohol dehydrogenase (ADH)4 gene (rs1126670, rs1126671, rs2032349) and one in ADH5 gene (rs2602836) associated with disease progression (p < 0.05). Our data provide a basic tool for identification of patient with ERT non-response risk that may represent a framework for personalized treatment of this rare disease.
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Affiliation(s)
- Francesca Scionti
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, Catanzaro, Italy
| | - Maria Teresa Di Martino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, Catanzaro, Italy
| | - Simona Sestito
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
| | - Angela Nicoletti
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
| | - Francesca Falvo
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
| | - Katia Roppa
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
| | | | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Giuseppe Agapito
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Antonio Pisani
- Department of Nephrology, University Federico II, Naples, Italy
| | - Eleonora Riccio
- Department of Nephrology, University Federico II, Naples, Italy
| | - Daniela Concolino
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
| | - Licia Pensabene
- Department of Medical and Surgical Sciences Pediatric Unit, Magna Graecia University, Catanzaro, Italy
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Milano M, Guzzi PH, Tymofieva O, Xu D, Hess C, Veltri P, Cannataro M. An extensive assessment of network alignment algorithms for comparison of brain connectomes. BMC Bioinformatics 2017; 18:235. [PMID: 28617222 PMCID: PMC5471963 DOI: 10.1186/s12859-017-1635-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. Results In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. Conclusion The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.
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Affiliation(s)
- Marianna Milano
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.
| | - Olga Tymofieva
- Department of Radiology University of California, San Francisco, USA
| | - Duan Xu
- Department of Radiology University of California, San Francisco, USA
| | - Christofer Hess
- Department of Radiology University of California, San Francisco, USA
| | - Pierangelo Veltri
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
| | - Mario Cannataro
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy
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Di Martino MT, Scionti F, Sestito S, Nicoletti A, Arbitrio M, Guzzi PH, Talarico V, Altomare F, Sanseviero MT, Agapito G, Pisani A, Riccio E, Borrelli O, Concolino D, Pensabene L. Genetic variants associated with gastrointestinal symptoms in Fabry disease. Oncotarget 2016; 7:85895-85904. [PMID: 27825144 PMCID: PMC5349883 DOI: 10.18632/oncotarget.13135] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 10/29/2016] [Indexed: 12/13/2022] Open
Abstract
Gastrointestinal symptoms (GIS) are often among the earliest presenting events in Fabry disease (FD), an X-linked lysosomal disorder caused by the deficiency of α-galactosidase A. Despite recent advances in clinical and molecular characterization of FD, the pathophysiology of the GIS is still poorly understood. To shed light either on differential clinical presentation or on intervariability of GIS in FD, we genotyped 1936 genetic markers across 231 genes that encode for drug-metabolizing enzymes and drug transport proteins in 49 FD patients, using the DMET Plus platform. All nine single nucleotide polymorphisms (SNPs) mapped within four genes showed statistically significant differences in genotype frequencies between FD patients who experienced GIS and patients without GIS: ABCB11 (odd ratio (OR) = 18.07, P = 0,0019; OR = 8.21, P = 0,0083; OR=8.21, P = 0,0083; OR = 8.21, P = 0,0083),SLCO1B1 (OR = 9.23, P = 0,0065; OR = 5.08, P = 0,0289; OR = 8.21, P = 0,0083), NR1I3 (OR = 5.40, P = 0,0191) and ABCC5 (OR = 14.44, P = 0,0060). This is the first study that investigates the relationships between genetic heterogeneity in drug absorption, distribution, metabolism and excretion (ADME) related genes and GIS in FD. Our findings provide a novel genetic variant framework which warrants further investigation for precision medicine in FD.
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Affiliation(s)
- Maria Teresa Di Martino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, Catanzaro, 88100 Italy
| | - Francesca Scionti
- Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, Catanzaro, 88100 Italy
| | - Simona Sestito
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | - Angela Nicoletti
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | | | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, 88100 Italy
| | - Valentina Talarico
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | - Federica Altomare
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | - Maria Teresa Sanseviero
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | - Giuseppe Agapito
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, 88100 Italy
| | - Antonio Pisani
- Departement of Nephrology University Federico II, Naples, 80138 Italy
| | - Eleonora Riccio
- Departement of Nephrology University Federico II, Naples, 80138 Italy
| | - Osvaldo Borrelli
- Department of Pediatric Gastroenterology, Great Ormond Street Hospital for Sick Children, University College of London (UCL), London, WC1E 6BT, UK
| | - Daniela Concolino
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
| | - Licia Pensabene
- Department of Medical and Surgical Sciences, Pediatric Unit, Magna Graecia University, Catanzaro, 88100 Italy
- Department of Pediatric Gastroenterology, Great Ormond Street Hospital for Sick Children, University College of London (UCL), London, WC1E 6BT, UK
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Agapito G, Botta C, Guzzi PH, Arbitrio M, Di Martino MT, Tassone P, Tagliaferri P, Cannataro M. OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes. Microarrays (Basel) 2016; 5:microarrays5040024. [PMID: 27669316 PMCID: PMC5197943 DOI: 10.3390/microarrays5040024] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 08/27/2016] [Accepted: 09/19/2016] [Indexed: 11/16/2022]
Abstract
BACKGROUND The identification of biomarkers for the estimation of cancer patients' survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. METHODS In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. RESULTS The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. CONCLUSIONS Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.
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Affiliation(s)
- Giuseppe Agapito
- Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | - Cirino Botta
- Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | | | - Maria Teresa Di Martino
- Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | - Pierfrancesco Tassone
- Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | - Pierosandro Tagliaferri
- Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
| | - Mario Cannataro
- Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
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Agapito G, Milano M, Guzzi PH, Cannataro M. Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations. IEEE/ACM Trans Comput Biol Bioinform 2016; 13:197-208. [PMID: 27045823 DOI: 10.1109/tcbb.2015.2462348] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.
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Di Martino MT, Arbitrio M, Guzzi PH, Cannataro M, Tagliaferri P, Tassone P. Experimental treatment of multiple myeloma in the era of precision medicine. Expert Review of Precision Medicine and Drug Development 2016. [DOI: 10.1080/23808993.2016.1142356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Abstract
Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process.
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Affiliation(s)
- Swarup Roy
- Department of Information Technology, North-Eastern Hill University, Shillong, India.
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.
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Arbitrio M, Di Martino MT, Barbieri V, Agapito G, Guzzi PH, Botta C, Iuliano E, Scionti F, Altomare E, Codispoti S, Conforti S, Cannataro M, Tassone P, Tagliaferri P. Identification of polymorphic variants associated with erlotinib-related skin toxicity in advanced non-small cell lung cancer patients by DMET microarray analysis. Cancer Chemother Pharmacol 2015; 77:205-9. [DOI: 10.1007/s00280-015-2916-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Accepted: 11/10/2015] [Indexed: 11/27/2022]
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Abenavoli L, DI Renzo L, Guzzi PH, Pellicano R, Milic N, DE Lorenzo A. Non-alcoholic fatty liver disease severity, central fat mass and adinopectin: a close relationship. ACTA ACUST UNITED AC 2015; 88:489-93. [PMID: 26733747 PMCID: PMC4689242 DOI: 10.15386/cjmed-595] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 10/09/2015] [Indexed: 12/15/2022]
Abstract
AIM Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the general population. Overweight is a common condition in patients with NAFLD, and body composition (BC) assessment is useful to evaluate nutritional status and the efficacy of nutritional strategies. A valid tool for assessing BC is dual-energy X-ray absorptiometry (DXA). Adiponectin has been shown to be relevant to the pathogenesis of NAFLD. The aim of this observational study is to define the relationship between the severity of NAFLD, the central fat mass evaluated by DXA, and the circulating levels of adiponectin. METHODS The study was carried out in 31 overweight patients. The degree of liver steatosis was evaluated by ultrasound (US) examination. Anthropometric parameters were measured according to standard methods. Fasting glucose and insulin level were used also to calculate insulin resistance (IR), according to the homeostasis model assessment-insulin resistance (HOMA-IR). The enzyme-linked immunosorbent assay technique was performed to dose fasting serum levels of adiponectin. RESULTS NAFLD progression was significantly associated with increased central fat (p<0.05). Using DXA, we quantified the regional distribution of adipose tissue and found the expected association between central fat and the US severity of NAFLD. Serum levels of adiponectin, were inversely related to NAFLD progression (p<0.05). CONCLUSION BC evaluated by anthropometry and DXA, may be used as indicator of NAFLD severity in overweight patients. The evaluation of BC in clinical practice, can improve the nutritional strategies and follow-up. In the clinical setting adiponectin may represent a potential marker for the staging of NAFLD.
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Affiliation(s)
- Ludovico Abenavoli
- Department of Health Sciences, University "Magna Græcia", Catanzaro, Italy; Division of Clinical Nutrition and Nutrigenomics, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Laura DI Renzo
- Division of Clinical Nutrition and Nutrigenomics, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University "Magna Græcia", Catanzaro, Italy
| | - Rinaldo Pellicano
- Department of Gastroenterology and Hepatology, Molinette Hospital, Turin, Italy
| | - Natasa Milic
- Department of Pharmacy, University of Novi Sad, Novi Sad, Serbia
| | - Antonino DE Lorenzo
- Division of Clinical Nutrition and Nutrigenomics, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
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Agapito G, Cannataro M, Guzzi PH, Milano M. Using GO-WAR for mining cross-ontology weighted association rules. Comput Methods Programs Biomed 2015; 120:113-122. [PMID: 25921876 DOI: 10.1016/j.cmpb.2015.03.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 03/16/2015] [Accepted: 03/23/2015] [Indexed: 06/04/2023]
Abstract
The Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associated to one or more gene products. The process of association is referred to as annotation. The relevance and the specificity of both GO terms and annotations are evaluated by a measure defined as information content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of association rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents GO-WAR (Gene Ontology-based Weighted Association Rules) a methodology for extracting weighted association rules. GO-WAR can extract association rules with a high level of IC without loss of support and confidence from a dataset of annotated data. A case study on using of GO-WAR on publicly available GO annotation datasets is used to demonstrate that our method outperforms current state of the art approaches.
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Affiliation(s)
- Giuseppe Agapito
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy.
| | - Marianna Milano
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy
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Chiarella G, Tognini S, Nacci A, Sieli R, Costante G, Petrolo C, Mancini V, Guzzi PH, Pasqualetti G, Cassandro E, Fattori B, Russo D, Monzani F. Vestibular disorders in euthyroid patients with Hashimoto's thyroiditis: role of thyroid autoimmunity. Clin Endocrinol (Oxf) 2014; 81:600-5. [PMID: 24735417 DOI: 10.1111/cen.12471] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 02/17/2014] [Accepted: 04/11/2014] [Indexed: 12/21/2022]
Abstract
INTRODUCTION A relationship between vestibular disorders and thyroid autoimmunity independently from thyroid function has been postulated. AIM To shed more light on the actual relationship between vestibular lesions and Hashimoto's thyroiditis (HT) regardless of thyroid function. METHODS Forty-seven patients with HT (89·4% F; aged 48·3 ± 12·7 years), 21 with multinodular goitre (MNG; 57·1% F; 54·1 ± 9·8 years) and 30 healthy volunteers (56·7% F; 50·7 ± 13·9 years) were enrolled. Inclusion criteria were the presence of normal thyroid function tests and no clinical history of vestibular dysfunction. Each subject was submitted to complete vestibular evaluation [Caloric Test, Vestibular evoked myogenic potentials (VEMPs), Head Shaking Test (HST)]. RESULTS 52·2% of HT patients showed an alteration of VEMPs and 44·7% of caloric test (P < 0·0001 for both). None of the MNG patients showed any vestibular alteration, while one healthy control showed an altered caloric test. A correlation was found between vestibular alterations of HT patients and the degree of serum TPOAb level, not affected by age and serum TSH value. By logistic regression analysis, the absence of thyroid autoimmunity significantly reduced the risk of vestibular alterations: HR 0.19 (95%CI: 0·003-0.25, P = 0·0004) for caloric test; HR 0·07 (95%CI: 0·02-0·425, P < 0·0001) for VEMPs; and HR 0·22 (95%CI: 0·06-0·7, P = 0·01) for HST. CONCLUSION In euthyroid HT patients, a significant relationship between subclinical vestibular damage and the degree of TPOAb titre was documented. This finding suggests that circulating antithyroid autoantibodies may represent a risk factor for developing vestibular dysfunction. An accurate vestibular evaluation of HT patients with or without symptoms is therefore warranted.
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Affiliation(s)
- Giuseppe Chiarella
- Department of Experimental and Clinical Medicine, Audiology and Phoniatrics Unit, University of Catanzaro Magna Graecia, Catanzaro, Italy
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Guzzi PH, Cho YR. Editorial: Special issue on computational approaches for extracting knowledge from biological networks. Interdiscip Sci 2014; 5:165-6. [PMID: 24307407 DOI: 10.1007/s12539-013-0175-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Pietro Hiram Guzzi
- Bioinformatics Laboratory, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100, Catanzaro, Italy,
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Abenavoli L, Luigiano C, Guzzi PH, Milic N, Morace C, Stelitano L, Consolo P, Miraglia S, Fagoonee S, Virgilio C, Luzza F, De Lorenzo A, Pellicano R. Serum adipokine levels in overweight patients and their relationship with non-alcoholic fatty liver disease. Panminerva Med 2014; 56:189-193. [PMID: 24994581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
AIM Non-alcoholic fatty liver disease (NAFLD) is a relevant public health matter in Western countries. The pathogenetic link between visceral fat, insulin resistance (IR) and NAFLD has been reported in literature. However, there are contradictions on the changes of adipokine levels in serum related to the presence of NAFLD. The aim of the present study was to evaluate the serum concentrations of a selected set of adipokines, that is, adiponectin, leptin, resistin and the pro-inflammatory cytokine interleukin-6 (IL-6) in overweight patients, and to clarify their relationship with NAFLD. METHODS Fasting serum levels of adipokines were determined in 42 consecutive overweight patients and in 25 lean controls. The degree of ultrasound (US) liver steatosis was graded according to the Hamaguchi score. RESULTS Liver steatosis was detected in 33 patients (78%) by US examination. Twelve patients with elevated transaminases levels showed significantly higher values of IR, leptin and resistin levels (P<0.05). Patients with steatosis presented a significantly higher leptin and a lower adiponectin levels (P<0.05) than controls. A significant inverse correlation was found between US steatosis progression and adiponectin and resistin levels (p<0.05). Considering the multiple logistic regression, adiponectin and leptin were good predictors to detect the presence of steatosis (p<0.05). CONCLUSION Our data support the concept that adipokine level changes are closely linked with IR. In addition, serum adiponectin and leptin levels may be used as diagnostic markers to determine the presence of NAFLD in overweight patients.
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Affiliation(s)
- L Abenavoli
- Department of Health Science, University "Magna Græcia", Catanzaro, Italy -
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Mina M, Guzzi PH. Improving the Robustness of Local Network Alignment: Design and Extensive Assessment of a Markov Clustering-Based Approach. IEEE/ACM Trans Comput Biol Bioinform 2014; 11:561-572. [PMID: 26356023 DOI: 10.1109/tcbb.2014.2318707] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The analysis of protein behavior at the network level had been applied to elucidate the mechanisms of protein interaction that are similar in different species. Published network alignment algorithms proved to be able to recapitulate known conserved modules and protein complexes, and infer new conserved interactions confirmed by wet lab experiments. In the meantime, however, a plethora of continuously evolving protein-protein interaction (PPI) data sets have been developed, each featuring different levels of completeness and reliability. For instance, algorithms performance may vary significantly when changing the data set used in their assessment. Moreover, existing papers did not deeply investigate the robustness of alignment algorithms. For instance, some algorithms performances vary significantly when changing the data set used in their assessment. In this work, we design an extensive assessment of current algorithms discussing the robustness of the results on the basis of input networks. We also present AlignMCL, a local network alignment algorithm based on an improved model of alignment graph and Markov Clustering. AlignMCL performs better than other state-of-the-art local alignment algorithms over different updated data sets. In addition, AlignMCL features high levels of robustness, producing similar results regardless the selected data set.
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