Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

Abstract Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological imp...

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Autores principales:	Tiago Azevedo, Giovanna Maria Dimitri, Pietro Lió, Eric R. Gamazon
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9a69e4735721400da7d906b6896f785e
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spelling	oai:doaj.org-article:9a69e4735721400da7d906b6896f785e2021-12-02T16:53:20ZMultilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits10.1038/s41540-021-00186-62056-7189https://doaj.org/article/9a69e4735721400da7d906b6896f785e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00186-6https://doaj.org/toc/2056-7189Abstract Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.Tiago AzevedoGiovanna Maria DimitriPietro LióEric R. GamazonNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tiago Azevedo Giovanna Maria Dimitri Pietro Lió Eric R. Gamazon Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
description	Abstract Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.
format	article
author	Tiago Azevedo Giovanna Maria Dimitri Pietro Lió Eric R. Gamazon
author_facet	Tiago Azevedo Giovanna Maria Dimitri Pietro Lió Eric R. Gamazon
author_sort	Tiago Azevedo
title	Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
title_short	Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
title_full	Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
title_fullStr	Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
title_full_unstemmed	Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
title_sort	multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9a69e4735721400da7d906b6896f785e
work_keys_str_mv	AT tiagoazevedo multilayermodellingofthehumantranscriptomeandbiologicalmechanismsofcomplexdiseasesandtraits AT giovannamariadimitri multilayermodellingofthehumantranscriptomeandbiologicalmechanismsofcomplexdiseasesandtraits AT pietrolio multilayermodellingofthehumantranscriptomeandbiologicalmechanismsofcomplexdiseasesandtraits AT ericrgamazon multilayermodellingofthehumantranscriptomeandbiologicalmechanismsofcomplexdiseasesandtraits
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Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

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