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Madeira D, Madeira C, Calosi P, Vermandele F, Carrier-Belleau C, Barria-Araya A, Daigle R, Findlay HS, Poisot T. Multilayer biological networks to upscale marine research to global change-smart management and sustainable resource use. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173837. [PMID: 38866145 DOI: 10.1016/j.scitotenv.2024.173837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 05/30/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
Human activities are having a massive negative impact on biodiversity and ecological processes worldwide. The rate and magnitude of ecological transformations induced by climate change, habitat destruction, overexploitation and pollution are now so substantial that a sixth mass extinction event is currently underway. The biodiversity crisis of the Anthropocene urges scientists to put forward a transformative vision to promote the conservation of biodiversity, and thus indirectly the preservation of ecosystem functions. Here, we identify pressing issues in global change biology research and propose an integrative framework based on multilayer biological networks as a tool to support conservation actions and marine risk assessments in multi-stressor scenarios. Multilayer networks can integrate different levels of environmental and biotic complexity, enabling us to combine information on molecular, physiological and behaviour responses, species interactions and biotic communities. The ultimate aim of this framework is to link human-induced environmental changes to species physiology, fitness, biogeography and ecosystem impacts across vast seascapes and time frames, to help guide solutions to address biodiversity loss and ecological tipping points. Further, we also define our current ability to adopt a widespread use of multilayer networks within ecology, evolution and conservation by providing examples of case-studies. We also assess which approaches are ready to be transferred and which ones require further development before use. We conclude that multilayer biological networks will be crucial to inform (using reliable multi-levels integrative indicators) stakeholders and support their decision-making concerning the sustainable use of resources and marine conservation.
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Affiliation(s)
- Diana Madeira
- Laboratory for Innovation and Sustainability of Marine Biological Resources (ECOMARE), Centre for Environmental and Marine Studies (CESAM), Department of Biology, University of Aveiro, Aveiro, Portugal.
| | - Carolina Madeira
- Applied Molecular Biosciences Unit, Department of Life Sciences, School of Science and Technology, NOVA University of Lisbon, Caparica, Portugal; i4HB - Institute for Health and Bioeconomy, School of Science and Technology, NOVA University of Lisbon, Caparica, Portugal
| | - Piero Calosi
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | - Fanny Vermandele
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | | | - Aura Barria-Araya
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | - Remi Daigle
- Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada; Marine Affairs Program, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | - Timothée Poisot
- Department of Biological Sciences, University of Montreal, Montreal, Canada
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2
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Beust C, Valdeolivas A, Baptista A, Brière G, Lévy N, Ozisik O, Baudot A. The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration. Adv Biol (Weinh) 2024:e2400134. [PMID: 39123285 DOI: 10.1002/adbi.202400134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 06/26/2024] [Indexed: 08/12/2024]
Abstract
Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.
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Affiliation(s)
- Cécile Beust
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Alberto Valdeolivas
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Anthony Baptista
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Galadriel Brière
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
- Aix Marseille Univ, CNRS, I2M, Marseille, France
| | - Nicolas Lévy
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Ozan Ozisik
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Anaïs Baudot
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
- CNRS, Marseille, France
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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3
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Kurbucz MT, Keresztély T, Szikszai S, Sugár A, Banász Z. Dataset on global trade networks of COVID-19 medical products. Data Brief 2024; 55:110606. [PMID: 38988730 PMCID: PMC11233917 DOI: 10.1016/j.dib.2024.110606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/20/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
This paper presents a comprehensive dataset on the global trade dynamics of COVID-19-related medical products for the years 2019 and 2020. The dataset, derived from the BACI database, focuses on eight distinct product categories identified by six-digit codes. The trade flow data for 224 countries is structured as a multilevel network, with countries as nodes and product categories as layers. Directed edges represent trading activities, and edge weights are determined by the difference in exported values between 2019 and 2020. The dataset is provided in an edges-and-nodes format. Additionally, the associated R script transforms the data into the MuxViz R package format, facilitating further analysis and visualization of the dataset. The dataset is valuable for researchers in the field of foreign trade or medical products, and for decision-makers in these fields, whether at company or national level.
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Affiliation(s)
- Marcell T Kurbucz
- Department of Computational Sciences, Institute for Particle and Nuclear Physics, HUN-REN Wigner Research Centre for Physics, 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Tibor Keresztély
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Szabolcs Szikszai
- Department of Economics, University of Pannonia, 10 Egyetem Street, H-8200 Veszprém, Hungary
| | - András Sugár
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Zsuzsanna Banász
- Department of Economics, University of Pannonia, 10 Egyetem Street, H-8200 Veszprém, Hungary
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4
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Robinson B, Bhamidi S, Dayan E. The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. Neurobiol Aging 2024; 136:70-77. [PMID: 38330641 PMCID: PMC10940182 DOI: 10.1016/j.neurobiolaging.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Synergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aβ positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aβ+ , relative to Aβ- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aβ burden and cognition in this sample.
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Affiliation(s)
- Belfin Robinson
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Shankar Bhamidi
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Kniha E, Dvořák V, Koblmüller S, Prudhomme J, Ivović V, Hoxha I, Oerther S, Heitmann A, Lühken R, Bañuls AL, Sereno D, Michelutti A, Toniolo F, Alarcón-Elbal PM, Bravo-Barriga D, González MA, Lucientes J, Colella V, Otranto D, Bezerra-Santos MA, Kunz G, Obwaller AG, Depaquit J, Alić A, Kasap OE, Alten B, Omeragic J, Volf P, Walochnik J, Sebestyén V, Trájer AJ. Reconstructing the post-glacial spread of the sand fly Phlebotomus mascittii Grassi, 1908 (Diptera: Psychodidae) in Europe. Commun Biol 2023; 6:1244. [PMID: 38066195 PMCID: PMC10709326 DOI: 10.1038/s42003-023-05616-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023] Open
Abstract
Phlebotomine sand flies (Diptera: Phlebotominae) are the principal vectors of Leishmania spp. (Kinetoplastida: Trypanosomatidae). In Central Europe, Phlebotomus mascittii is the predominant species, but largely understudied. To better understand factors driving its current distribution, we infer patterns of genetic diversity by testing for signals of population expansion based on two mitochondrial genes and model current and past climate and habitat suitability for seven post-glacial maximum periods, taking 19 climatic variables into account. Consequently, we elucidate their connections by environmental-geographical network analysis. Most analyzed populations share a main haplotype tracing back to a single glacial maximum refuge area on the Mediterranean coasts of South France, which is supported by network analysis. The rapid range expansion of Ph. mascittii likely started in the early mid-Holocene epoch until today and its spread possibly followed two routes. The first one was through northern France to Germany and then Belgium, and the second across the Ligurian coast through present-day Slovenia to Austria, toward the northern Balkans. Here we present a combined approach to reveal glacial refugia and post-glacial spread of Ph. mascittii and observed discrepancies between the modelled and the current known distribution might reveal yet overlooked populations and potential further spread.
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Affiliation(s)
- Edwin Kniha
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Vít Dvořák
- Department of Parasitology, Faculty of Science, Charles University, Prague, Czech Republic
| | | | - Jorian Prudhomme
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
- INTHERES, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Vladimir Ivović
- Department of Biodiversity, FAMNIT, University of Primorska, Koper-Capodistria, Slovenia
| | - Ina Hoxha
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Sandra Oerther
- Institute of Global Health, Heidelberg University, Heidelberg, Germany
- German Mosquito Control Association (KABS), Speyer, Germany
- Institute for Dipterology (IfD), Speyer, Germany
| | - Anna Heitmann
- Department of Arbovirology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Renke Lühken
- Department of Arbovirology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
- Research Group Vector Control, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Anne-Laure Bañuls
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
| | - Denis Sereno
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
- Institut de Recherche pour le Développement, Université de Montpellier, UMR INTERTRYP, Parasite Infectiology and Public Health Research group. IRD, CIRAD, Montpellier, France
| | - Alice Michelutti
- Laboratory of Parasitology, Micology and Medical Entomology, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
| | - Federica Toniolo
- Laboratory of Parasitology, Micology and Medical Entomology, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
| | - Pedro M Alarcón-Elbal
- Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos (PASAPTA), Facultad de Veterinaria, Universidad CEU Cardenal Herrera, Valencia, Spain
- Laboratorio de investigación de Entomología, Departamento de Zoología, Facultad de Ciencias Biológicas, Bloque B, Universidad de Valencia, Valencia, Spain
| | - Daniel Bravo-Barriga
- Department of Animal Health, Animal Health and Zoonosis Research Group (GISAZ), UIC Zoonosis and Emerging Diseases (ENZOEM), University of Cordoba, Cordoba, Spain
| | - Mikel A González
- Department of Animal Production and Health, Veterinary Public Health and Food Science and Technology (PASAPTA), Facultad de Veterinaria, Universidad Cardenal Herrera-CEU, CEU Universities, Valencia, Spain
- Applied Zoology and Animal Conservation Group, University of the Balearic Islands (UIB), Palma de Mallorca, Spain
| | - Javier Lucientes
- Animal Health Department, The AgriFood Institute of Aragon (IA2), School of Veterinary Medicine, University of Zaragoza, Zaragoza, Spain
| | - Vito Colella
- Faculty of Science, The University of Melbourne, Parkville, Australia
| | - Domenico Otranto
- Department of Veterinary Medicine, University of Bari, Bari, Italy
- Faculty of Veterinary Sciences, Bu-Ali Sina University, Hamedan, Iran
| | | | - Gernot Kunz
- Institute of Biology, University of Graz, Graz, Austria
| | - Adelheid G Obwaller
- Division of Science, Research and Development, Federal Ministry of Defence, Vienna, Austria
| | - Jerome Depaquit
- Université de Reims Champagne Ardenne, ESCAPE EA7510, USC ANSES VECPAR, SFR Cap Santé, UFR de Pharmacie, Reims, France
| | - Amer Alić
- Department of Clinical Sciences of Veterinary Medicine, Faculty of Veterinary Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Ozge Erisoz Kasap
- Department of Biology, Ecology Section, Faculty of Science, VERG Laboratories, Hacettepe University, Ankara, Turkey
| | - Bulent Alten
- Department of Biology, Ecology Section, Faculty of Science, VERG Laboratories, Hacettepe University, Ankara, Turkey
| | - Jasmin Omeragic
- Department of Pathobiology and Epidemiology, Veterinary Faculty, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Petr Volf
- Department of Parasitology, Faculty of Science, Charles University, Prague, Czech Republic
| | - Julia Walochnik
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Viktor Sebestyén
- University of Pannonia, Sustainability Solutions Research Lab, Veszprém, Hungary
| | - Attila J Trájer
- University of Pannonia, Sustainability Solutions Research Lab, Veszprém, Hungary.
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6
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Cinaglia P, Milano M, Cannataro M. Multilayer network alignment based on topological assessment via embeddings. BMC Bioinformatics 2023; 24:416. [PMID: 37932663 PMCID: PMC10629033 DOI: 10.1186/s12859-023-05508-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University, 88100, Catanzaro, Italy.
| | - Marianna Milano
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100, Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
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Russell M, Aqil A, Saitou M, Gokcumen O, Masuda N. Gene communities in co-expression networks across different tissues. PLoS Comput Biol 2023; 19:e1011616. [PMID: 37976327 PMCID: PMC10691702 DOI: 10.1371/journal.pcbi.1011616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 12/01/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.
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Affiliation(s)
- Madison Russell
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Alber Aqil
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Marie Saitou
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Omer Gokcumen
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, United States of America
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8
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Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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9
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Yousefi B, Firoozbakht F, Melograna F, Schwikowski B, Van Steen K. PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation. Front Genet 2023; 14:1274637. [PMID: 37928248 PMCID: PMC10620964 DOI: 10.3389/fgene.2023.1274637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
- École Doctorale Complexite du Vivant, Sorbonne Université, Paris, France
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
| | - Farzaneh Firoozbakht
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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10
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Milano M, Agapito G, Cannataro M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes (Basel) 2023; 14:1915. [PMID: 37895264 PMCID: PMC10606656 DOI: 10.3390/genes14101915] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/26/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
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11
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Khilwani R, Singh S. Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis. Biomedicines 2023; 11:2706. [PMID: 37893079 PMCID: PMC10604646 DOI: 10.3390/biomedicines11102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.
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Affiliation(s)
| | - Shailza Singh
- Systems Medicine Laboratory, National Centre for Cell Science, SPPU Campus, Ganeshkhind Road, Pune 411007, India;
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12
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Hasman M, Mayr M, Theofilatos K. Uncovering Protein Networks in Cardiovascular Proteomics. Mol Cell Proteomics 2023; 22:100607. [PMID: 37356494 PMCID: PMC10460687 DOI: 10.1016/j.mcpro.2023.100607] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023] Open
Abstract
Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
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Affiliation(s)
- Maria Hasman
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
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13
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Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
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14
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Kumar T, Sethuraman R, Mitra S, Ravindran B, Narayanan M. MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication. PLoS Comput Biol 2023; 19:e1011022. [PMID: 37093889 PMCID: PMC10159362 DOI: 10.1371/journal.pcbi.1011022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 05/04/2023] [Accepted: 03/12/2023] [Indexed: 04/25/2023] Open
Abstract
With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level systems investigations in humans.
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Affiliation(s)
- Tarun Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | | | - Sanga Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Balaraman Ravindran
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
- The Centre for Integrative Biology and Systems medicinE (IBSE), IIT Madras, Chennai, India
- Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India
- Multiscale Digital Neuroanatomy (MDN), IIT Madras, Chennai, India
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15
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Recanatini M, Menestrina L. Network modeling helps to tackle the complexity of drug-disease systems. WIREs Mech Dis 2023:e1607. [PMID: 36958762 DOI: 10.1002/wsbm.1607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
| | - Luca Menestrina
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
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16
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Kumar S, Sarmah DT, Asthana S, Chatterjee S. konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network. Bioinformatics 2022; 39:6955601. [PMID: 36545703 PMCID: PMC9848060 DOI: 10.1093/bioinformatics/btac815] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/30/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network. RESULTS We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not. AVAILABILITY AND IMPLEMENTATION konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.
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Affiliation(s)
| | | | - Shailendra Asthana
- Non-communicable Disease Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad 121001, India
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17
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Timilsina M, Nováček V, d’Aquin M, Yang H. Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding. Neural Netw 2022; 156:205-217. [DOI: 10.1016/j.neunet.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/16/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022]
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18
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Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nat Commun 2022; 13:6656. [PMID: 36333358 PMCID: PMC9636193 DOI: 10.1038/s41467-022-34537-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.
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19
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Milano M, Guzzi PH, Cannataro M. Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1272. [PMID: 36141158 PMCID: PMC9497667 DOI: 10.3390/e24091272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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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20
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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21
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Stožer A, Šterk M, Paradiž Leitgeb E, Markovič R, Skelin Klemen M, Ellis CE, Križančić Bombek L, Dolenšek J, MacDonald PE, Gosak M. From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science. Front Endocrinol (Lausanne) 2022; 13:922640. [PMID: 35784543 PMCID: PMC9240343 DOI: 10.3389/fendo.2022.922640] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.
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Affiliation(s)
- Andraž Stožer
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Marko Šterk
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Eva Paradiž Leitgeb
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Rene Markovič
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Institute of Mathematics and Physics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Maša Skelin Klemen
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Cara E. Ellis
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Jurij Dolenšek
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Patrick E. MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Marko Gosak
- Institute of Physiology, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
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22
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Amara A, Frainay C, Jourdan F, Naake T, Neumann S, Novoa-del-Toro EM, Salek RM, Salzer L, Scharfenberg S, Witting M. Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation. Front Mol Biosci 2022; 9:841373. [PMID: 35350714 PMCID: PMC8957799 DOI: 10.3389/fmolb.2022.841373] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/18/2022] [Indexed: 01/19/2023] Open
Abstract
Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
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Affiliation(s)
- Adam Amara
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Thomas Naake
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Elva María Novoa-del-Toro
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | | | - Liesa Salzer
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sarah Scharfenberg
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Freising, Germany
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23
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Naro A, Pignolo L, Calabrò RS. Brain Network Organization Following Post-Stroke Neurorehabilitation. Int J Neural Syst 2022; 32:2250009. [PMID: 35139774 DOI: 10.1142/s0129065722500095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection models based on multiplex-multilayer network analysis (MMN) to explore functional network connectivity changes induced by robot-aided gait training (RAGT) using the Ekso, a wearable exoskeleton, and compared it to conventional overground gait training (COGT) in chronic stroke patients. We extracted the coreness of individual nodes at multiple locations in the brain from EEG recordings obtained before and after gait training in a resting state. We found that patients provided with RAGT achieved a greater motor function recovery than those receiving COGT. This difference in clinical outcome was paralleled by greater changes in connectivity patterns among different brain areas central to motor programming and execution, as well as a recruitment of other areas beyond the sensorimotor cortices and at multiple frequency ranges, contemporarily. The magnitude of these changes correlated with motor function recovery chances. Our data suggest that the use of RAGT as an add-on treatment to COGT may provide post-stroke patients with a greater modification of the functional brain network impairment following a stroke. This might have potential clinical implications if confirmed in large clinical trials.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
| | - Loris Pignolo
- Sant'Anna Institute, Via Siris, 11, 88900 Crotone, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy. Via Palermo, SS 113, Ctr. Casazza, 98124, Messina, Italy
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Abstract
Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.
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Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing. Microbiol Spectr 2021; 9:e0101821. [PMID: 34668739 PMCID: PMC8528132 DOI: 10.1128/spectrum.01018-21] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Leishmania parasites are the causal agent of leishmaniasis, an endemic disease in more than 90 countries worldwide. Over the years, traditional approaches focused on the parasite when developing treatments against leishmaniasis. Despite numerous attempts, there is not yet a universal treatment, and those available have allowed for the appearance of resistance. Here, we propose and follow a host-directed approach that aims to overcome the current lack of treatment. Our approach identifies potential therapeutic targets in the host cell and proposes known drug interactions aiming to improve the immune response and to block the host machinery necessary for the survival of the parasite. We started analyzing transcription factor regulatory networks of macrophages infected with Leishmania major. Next, based on the regulatory dynamics of the infection and available gene expression profiles, we selected potential therapeutic target proteins. The function of these proteins was then analyzed following a multilayered network scheme in which we combined information on metabolic pathways with known drugs that have a direct connection with the activity carried out by these proteins. Using our approach, we were able to identify five host protein-coding gene products that are potential therapeutic targets for treating leishmaniasis. Moreover, from the 11 drugs known to interact with the function performed by these proteins, 3 have already been tested against this parasite, verifying in this way our novel methodology. More importantly, the remaining eight drugs previously employed to treat other diseases, remain as promising yet-untested antileishmanial therapies. IMPORTANCE This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles. Once proteins have been selected, our approach employs a multilayered network methodology that relates proteins to functions to drugs that alter these functions. By applying our novel approach to macrophages during the Leishmania infection process, we both validated our work and found eight drugs already approved for use in humans that to the best of our knowledge were never employed to treat leishmaniasis, rendering our work as a new tool in the box available to the scientific community fighting parasites.
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Thomas JP, Modos D, Korcsmaros T, Brooks-Warburton J. Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease. Front Genet 2021; 12:760501. [PMID: 34745229 PMCID: PMC8566351 DOI: 10.3389/fgene.2021.760501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/08/2021] [Indexed: 12/22/2022] Open
Abstract
Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.
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Affiliation(s)
- John P Thomas
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
- Department of Gastroenterology, Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Johanne Brooks-Warburton
- Department of Gastroenterology, Lister Hospital, Stevenage, United Kingdom
- Department of Clinical, Pharmaceutical and Biological Sciences, University of Hertfordshire, Hatfield, United Kingdom
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27
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Zaharchuk HA, Karuza EA. Multilayer networks: An untapped tool for understanding bilingual neurocognition. BRAIN AND LANGUAGE 2021; 220:104977. [PMID: 34166942 DOI: 10.1016/j.bandl.2021.104977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 06/13/2023]
Abstract
Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. The degree of overlap between languages is known to affect lexical competition during online processing and production, and its relevance for second language acquisition has also been established. Nevertheless, determining what makes two languages similar (or not) increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax) are considered. How can we feasibly account for the patterns of convergence and divergence at each level of representation, as well as the interactions between them? The growing field of network science brings new methodologies to bear on this longstanding question. Below, we summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. Critically, we stress the particular value of multilayer techniques, unique and powerful in their ability to simultaneously accommodate an array of node-to-node (or word-to-word) relationships.
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Affiliation(s)
- Holly A Zaharchuk
- Department of Psychology and The Center for Language Science, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Elisabeth A Karuza
- Department of Psychology and The Center for Language Science, The Pennsylvania State University, University Park, PA 16802, USA.
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28
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Ovens K, Eames BF, McQuillan I. Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution. Front Genet 2021; 12:695399. [PMID: 34484293 PMCID: PMC8414652 DOI: 10.3389/fgene.2021.695399] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.
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Affiliation(s)
- Katie Ovens
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - B. Frank Eames
- Department of Anatomy, Physiology, & Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Xiang J, Zhang J, Zheng R, Li X, Li M. NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction. Brief Bioinform 2021; 22:6236070. [PMID: 33866352 DOI: 10.1093/bib/bbab080] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/11/2021] [Accepted: 02/21/2021] [Indexed: 12/12/2022] Open
Abstract
The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, Human, China
| | - Jiashuai Zhang
- School of Computer Science and Engineering, Central South University, Human, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, China
| | - Xingyi Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
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