A multi-objective genetic algorithm to find active modules in multiplex biological networks

Novoa-Del-Toro, Elva Maria; Mezura-Montes, Efrén; Vignes, Matthieu; Térézol, Morgane; Magdinier, Frédérique; Tichit, Laurent; Baudot, Anaı̈s

doi:10.1371/journal.pcbi.1009263

Cited by 12 publications

(19 citation statements)

References 55 publications

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“…Network‐based approaches have been widely used for efficient identification of new diseases genes or reveal biological processes perturbed in diseases. With this perspective, we used MOGAMUN, a novel algorithm that integrates expression data with the simultaneous exploration of multiplex networks (pathways, protein–protein interactions, and co‐expression) to retrieve active modules specifically dysregulated in diseases 15 . As compared with existing tools such as jActiveModules, 26 COSINE, 27 and PinnacleZ, limited to the analysis of single networks, MOGAMUN takes into account different biological sources of physical and/or functional interactions.…”

Section: Discussionmentioning

confidence: 99%

“…RNA‐Seq data were analysed using MOGAMUN, a multi‐objective genetic algorithm that identifies active modules (i.e. highly interconnected subnetworks with an overall deregulation) in a multiplex biological network composed of different layers, where each of them represents physical and/or functional interactions 15 . We used as input for MOGAMUN the resulting FDR‐corrected p‐values, and a multiplex biological network composed of three layers or undirected interactions from 17 .…”

Section: Methodsmentioning

confidence: 99%

“…To identify FSHD‐specific pathways and understand muscle alterations that are specific to FSHD cells, we used induced pluripotent stem cells differentiated into innervated muscle fibres 14 . We compared the transcriptome of FSHD‐derived cells with cells from healthy donors but also cells from patients affected with BAMS by RNA Sequencing using classical pipelines but also applied MOGAMUN, a recently developed algorithm able to find active modules from multiplex biological networks by integrating protein–protein interactions, biological pathways and co‐expression 15 . Interesting areas are located automatically, by finding highly deregulated and dense subnetworks.…”

Section: Introductionmentioning

confidence: 99%

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Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells‐derived innervated muscle fibres

Laberthonnière

Novoa-del-Toro

Delourme

et al. 2021

J cachexia sarcopenia muscle

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Background Facioscapulohumeral dystrophy (FSHD) is a late-onset autosomal dominant form of muscular dystrophy involving specific groups of muscles with variable weakness that precedes inflammatory response, fat infiltration, and muscle atrophy. As there is currently no cure for this disease, understanding and modelling the typical muscle weakness in FSHD remains a major milestone towards deciphering the disease pathogenesis as it will pave the way to therapeutic strategies aimed at correcting the functional muscular defect in patients. Methods To gain further insights into the specificity of the muscle alteration in this disease, we derived induced pluripotent stem cells from patients affected with Types 1 and 2 FSHD but also from patients affected with Bosma arhinia and microphthalmia. We differentiated these cells into contractile innervated muscle fibres and analysed their transcriptome by RNA Seq in comparison with cells derived from healthy donors. To uncover biological pathways altered in the disease, we applied MOGAMUN, a multi-objective genetic algorithm that integrates multiplex complex networks of biological interactions (protein-protein interactions, co-expression, and biological pathways) and RNA Seq expression data to identify active modules. Results We identified 132 differentially expressed genes that are specific to FSHD cells (false discovery rate < 0.05). In FSHD, the vast majority of active modules retrieved with MOGAMUN converges towards a decreased expression of genes encoding proteins involved in sarcomere organization (P value 2.63e À12 ), actin cytoskeleton (P value 9.4e À5 ), myofibril (P value 2.19e À12 ), actin-myosin sliding, and calcium handling (with P values ranging from 7.9e À35 to 7.9e À21 ). Combined with in vivo validations and functional investigations, our data emphasize a reduction in fibre contraction (P value < 0.0001) indicating that the muscle weakness that is typical of FSHD clinical spectrum might be associated with dysfunction of calcium release (P value < 0.0001), actin-myosin interactions, motor activity, mechano-transduction, and dysfunctional sarcomere contractility. Conclusions Identification of biomarkers of FSHD muscle remain critical for understanding the process leading to the pathology but also for the definition of readouts to be used for drug design, outcome measures, and monitoring of therapies. The different pathways identified through a system biology approach have been largely overlooked in the disease. Overall, our work opens new perspectives in the definition of biomarkers able to define the muscle alteration but also in the development of novel strategies to improve muscle function as it provides functional parameters for active molecule screening.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells‐derived innervated muscle fibres

Laberthonnière

Novoa-del-Toro

Delourme

et al. 2021

J cachexia sarcopenia muscle

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“…The success of functional genomics is involved in the rapid accumulation of diverse biological data about genes, proteins or other macromolecules [ 23 ]. We have access to multiple types of physical or functional interactions between proteins.…”

Section: Methodsmentioning

confidence: 99%

A tensor-based bi-random walks model for protein function prediction

Xiong

Jiang

et al. 2022

BMC Bioinformatics

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Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

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“…Toubiana et al developed a GA to optimize gene modules by gradually increasing the correlation between traits and a subset of gene modules [23]. Novoa et al proposed a multi-objective GA for identifying active modules in MUltiplex biological Networks, which optimized the density of interactions and the scores of the nodes [24]. However, GA was rarely applied to the optimization of functional similarity within gene modules.…”

Section: Introductionmentioning

confidence: 99%

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

2023

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Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

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A multi-objective genetic algorithm to find active modules in multiplex biological networks

Cited by 12 publications

References 55 publications

Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells‐derived innervated muscle fibres

Facioscapulohumeral dystrophy weakened sarcomeric contractility is mimicked in induced pluripotent stem cells‐derived innervated muscle fibres

A tensor-based bi-random walks model for protein function prediction

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

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