MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

Fan, Zhong; Zhou, Yuan; Ressom, Habtom W.

doi:10.3390/metabo10040144

Cited by 14 publications

(11 citation statements)

References 36 publications

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“…The known chemical defensome genes can also be used as prior knowledge in identifying biomarker genes in response to a chemical [152; 153]. Integration of multi-omics data can also strengthen the predictive power of identifying biomarkers at a more systematic level [154][155][156].…”

Section: Evaluation Of Biomarker Discovery Methodsmentioning

confidence: 99%

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol

et al. 2018

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Polycyclic aromatic hydrocarbons such as benzo[a]pyrene (BaP) that activate the aryl hydrocarbon receptor (Ahr) pathway, and endocrine disruptors acting through the estrogen receptor pathway are among environmental pollutants of major concern. In this work, we exposed Atlantic cod (Gadus morhua) precision-cut liver slices (PCLS) to BaP (10 nM and 1000 nM), ethynylestradiol (EE2) (10 nM and 1000 nM), and equimolar mixtures of BaP and EE2 (10 nM and 1000 nM) for 48 h, and performed RNA-Seq based transcriptome mapping followed by systematic bioinformatics analyses. Our gene expression analysis showed that several genes were differentially expressed in response to BaP and EE2 treatments in PCLS. Strong up-regulation of genes coding for the cytochrome P450 1a (Cyp1a) enzyme and the Ahr repressor (Ahrrb) was observed in BaP treated PCLS. EE2 treatment of liver slices strongly up-regulated genes coding for precursors of vitellogenin (Vtg) and eggshell zona pellucida (Zp) proteins. As expected, pathway enrichment and network analysis showed that the Ahr and estrogen receptor pathways are among the top affected by BaP and EE2 treatments, respectively. Interestingly, two genes coding for fibroblast growth factor 3 (Fgf3) and fibroblast growth factor 4 (Fgf4) were up-regulated by EE2 in this study. To our knowledge, the fgf3 and fgf4 genes have not previously been described in relation to estrogen signaling in fish liver, and these results suggest the modulation of the FGF signaling pathway by estrogens in fish. The signature expression profiles of top differentially expressed genes in response to the single compound (BaP or EE2) treatment were generally maintained in the expression responses to the equimolar binary mixtures. However, in the mixture-treated groups, BaP appeared to have anti-estrogenic effects as observed by lower number of differentially expressed putative EE2 responsive genes. Our in-depth quantitative analysis of changes in liver transcriptome in response to BaP and EE2, using PCLS tissue culture provides further mechanistic insights into effects of the compounds. Moreover, the analyses demonstrate the usefulness of PCLS in cod for omics experiments.

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Section: Evaluation Of Biomarker Discovery Methodsmentioning

confidence: 99%

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol

et al. 2018

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“…'MixOmics' is an R package with tools for univariate, multivariate, and multi-omics analysis. Other tools use matrix decomposition [124,125], graph-based methods [126][127][128], or integrate the omics data into genome-scale metabolic models [129,130]. Overall, if enough data is available, ANN and RF methods are well suited to capture nonlinearity and provide interpretability to understand the biological context.…”

Section: Multi-omics Integrationmentioning

confidence: 99%

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

et al. 2020

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The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

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“…However, in a multi-omics integrations context one seeks above all to connect information from different omics fields (transcriptomics, proteomics, metabolomics, lipidomics, and metabolomics ( Haas et al, 2017 ; Fan, Zhou and Ressom, 2020 ; Cansu Demirel, Kaan Arici and Tuncbag, 2022 ). In this context, multi-layer algorithms for visualization are preferable to force-directed algorithms ( Bodein et al, 2021 ; Dursun, Kwitek and Bozdag, 2021 ; Marín-Llaó et al, 2021 ).…”

Section: Methods Based On Text Miningmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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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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MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

Cited by 14 publications

References 36 publications

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

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