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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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Van Hul M, Le Roy T, Prifti E, Dao MC, Paquot A, Zucker JD, Delzenne NM, Muccioli GG, Clément K, Cani PD. From correlation to causality: the case of Subdoligranulum. Gut Microbes 2020; 12:1-13. [PMID: 33323004 PMCID: PMC7744154 DOI: 10.1080/19490976.2020.1849998] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Gut microbes are considered as major factors contributing to human health. Nowadays, the vast majority of the data available in the literature are mostly exhibiting negative or positive correlations between specific bacteria and metabolic parameters. From these observations, putative detrimental or beneficial effects are then inferred. Akkermansia muciniphila is one of the unique examples for which the correlations with health benefits have been causally validated in vivo in rodents and humans. In this study, based on available metagenomic data in overweight/obese population and clinical variables that we obtained from two cohorts of individuals (n = 108) we identified several metagenomic species (MGS) strongly associated with A. muciniphila with one standing out: Subdoligranulum. By analyzing both qPCR and shotgun metagenomic data, we discovered that the abundance of Subdoligranulum was correlated positively with microbial richness and HDL-cholesterol levels and negatively correlated with fat mass, adipocyte diameter, insulin resistance, levels of leptin, insulin, CRP, and IL6 in humans. Therefore, to further explore whether these strong correlations could be translated into causation, we investigated the effects of the unique cultivated strain of Subdoligranulum (Subdoligranulum variabile DSM 15176 T) in obese and diabetic mice as a proof-of-concept. Strikingly, there were no significant difference in any of the hallmarks of obesity and diabetes measured (e.g., body weight gain, fat mass gain, glucose tolerance, liver weight, plasma lipids) at the end of the 8 weeks of treatment. Therefore, the absence of effect following the supplementation with S. variabile indicates that increasing the intestinal abundance of this bacterium is not translated into beneficial effects in mice. In conclusion, we demonstrated that despite the fact that numerous strong correlations exist between a given bacteria and health, proof-of-concept experiments are required to be further validated or not in vivo. Hence, an urgent need for causality studies is warranted to move from human observations to preclinical validations.
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Affiliation(s)
- Matthias Van Hul
- Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Tiphaine Le Roy
- Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Edi Prifti
- Institut de Recherche et Developpement, IRD, Sorbonne Unive.rsity, UMMISCO, Bondy, France,Sorbonne Université, INSERM, Nutrition and Obesities: Systemic Approaches (Nutriomics) Research Unit, Paris, France
| | - Maria Carlota Dao
- Sorbonne Université, INSERM, Nutrition and Obesities: Systemic Approaches (Nutriomics) Research Unit, Paris, France
| | - Adrien Paquot
- Bioanalysis and Pharmacology of Bioactive Lipids Research Group, Louvain Drug Research Institute, UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Jean-Daniel Zucker
- Institut de Recherche et Developpement, IRD, Sorbonne Unive.rsity, UMMISCO, Bondy, France,Sorbonne Université, INSERM, Nutrition and Obesities: Systemic Approaches (Nutriomics) Research Unit, Paris, France
| | - Nathalie M. Delzenne
- Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Giulio G. Muccioli
- Bioanalysis and Pharmacology of Bioactive Lipids Research Group, Louvain Drug Research Institute, UCLouvain, Université Catholique de Louvain, Brussels, Belgium
| | - Karine Clément
- Sorbonne Université, INSERM, Nutrition and Obesities: Systemic Approaches (Nutriomics) Research Unit, Paris, France,Assistance Publique Hôpitaux de Paris, Nutrition Department, Pitié-Salpêtrière Hospital, CRNH Ile de France, Paris, France
| | - Patrice D. Cani
- Metabolism and Nutrition Research Group, Louvain Drug Research Institute, Walloon Excellence in Life Sciences and BIOtechnology (WELBIO), UCLouvain, Université Catholique de Louvain, Brussels, Belgium,CONTACT Patrice D. Cani UCLouvain, Université Catholique de Louvain, LDRI, Metabolism and Nutrition Research Group, BrusselsB-1200, Belgium
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Prifti E, Chevaleyre Y, Hanczar B, Belda E, Danchin A, Clément K, Zucker JD. Interpretable and accurate prediction models for metagenomics data. Gigascience 2020; 9:giaa010. [PMID: 32150601 PMCID: PMC7062144 DOI: 10.1093/gigascience/giaa010] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 09/12/2019] [Accepted: 01/27/2020] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. RESULTS Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. CONCLUSIONS Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field.
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Affiliation(s)
- Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 Avenue Henri Varagnat, F-93143 Bondy, France
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Yann Chevaleyre
- Paris-Dauphine University, PSL Research University, CNRS, UMR 7243, LAMSADE, place du Mal. de Lattre de Tassigny, F-75016, Paris, France
| | - Blaise Hanczar
- IBISC, University Paris-Saclay, University Evry, Evry, 23 Boulevard de France, F-91034, France
| | - Eugeni Belda
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Antoine Danchin
- Institut Cochin INSERM U1016−CNRS UMR8104−Université Paris Descartes, 24 Rue du Faubourg Saint-Jacques, F-75014, Paris, France
| | - Karine Clément
- Sorbonne University, INSERM, Nutrition and Obesities; Systemic Approach Research Unit (NutriOmics), 91 Boulevard de l'Hopital, F-75013, Paris, France
- Assistance Publique-Hôpitaux de Paris, Nutrition Department, CRNH Ile de France, Pitié-Salpêtrière Hospital, 91 Boulevard de l'Hopital, F-75013, Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 Avenue Henri Varagnat, F-93143 Bondy, France
- Institute of Cardiometabolism and Nutrition, ICAN, Integromics, 91 Boulevard de l'Hopital, F-75013, Paris, France
- Sorbonne University, INSERM, Nutrition and Obesities; Systemic Approach Research Unit (NutriOmics), 91 Boulevard de l'Hopital, F-75013, Paris, France
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Dao MC, Sokolovska N, Brazeilles R, Affeldt S, Pelloux V, Prifti E, Chilloux J, Verger EO, Kayser BD, Aron-Wisnewsky J, Ichou F, Pujos-Guillot E, Hoyles L, Juste C, Doré J, Dumas ME, Rizkalla SW, Holmes BA, Zucker JD, Clément K. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front Physiol 2019; 9:1958. [PMID: 30804813 PMCID: PMC6371001 DOI: 10.3389/fphys.2018.01958] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 12/27/2018] [Indexed: 12/17/2022] Open
Abstract
Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 - baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).
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Affiliation(s)
- Maria Carlota Dao
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | - Nataliya Sokolovska
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | | | - Séverine Affeldt
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | - Véronique Pelloux
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | - Edi Prifti
- Institute of Cardiometabolism and Nutrition, Integromics, ICAN, Paris, France
- Sorbonne University, IRD, UMMISCO, Bondy, France
| | - Julien Chilloux
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Disease, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Eric O. Verger
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | - Brandon D. Kayser
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | - Judith Aron-Wisnewsky
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
- Assistance Publique Hôpitaux de Paris, Nutrition Department, CRNH Ile-de-France, Pitié-Salpêtrière Hospital, Paris, France
| | - Farid Ichou
- Institute of Cardiometabolism and Nutrition, ICANalytics, Paris, France
| | - Estelle Pujos-Guillot
- Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d’Exploration du Métabolisme, MetaboHUB, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Lesley Hoyles
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Disease, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Bioscience, School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, United Kingdom
| | - Catherine Juste
- National Institute of Agricultural Research, Micalis Institute, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Joël Doré
- National Institute of Agricultural Research, Micalis Institute, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Disease, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Salwa W. Rizkalla
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
| | | | - Jean-Daniel Zucker
- Institute of Cardiometabolism and Nutrition, Integromics, ICAN, Paris, France
- Sorbonne University, IRD, UMMISCO, Bondy, France
| | - Karine Clément
- Sorbonne University, French National Institute for Health and Medical Research, NutriOmics Unit, Institute of Cardiometabolism and Nutrition, Paris, France
- Assistance Publique Hôpitaux de Paris, Nutrition Department, CRNH Ile-de-France, Pitié-Salpêtrière Hospital, Paris, France
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van Dijk ADJ, Lähdesmäki H, de Ridder D, Rousu J. Selected proceedings of Machine Learning in Systems Biology: MLSB 2016. BMC Bioinformatics 2016; 17:437. [PMID: 28105910 PMCID: PMC5249013 DOI: 10.1186/s12859-016-1305-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Aalt D J van Dijk
- Biometris, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.,Applied Bioinformatics, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.,Bioinformatics Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, 00076, Aalto, Finland
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Juho Rousu
- Department of Computer Science, Aalto University, 00076, Aalto, Finland.
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