Multi-level integration of environmentally perturbed internal phenotypes reveals key points of connectivity between them

Nirupama Benis*, Soumya K. Kar, Vitor A.P. Martins dos Santos, Mari A. Smits, Dirkjan Schokker, Maria Suarez-Diez

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them "connectivity hubs." In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

Original languageEnglish
Article number388
Number of pages11
JournalFrontiers in Physiology
Volume8
DOIs
Publication statusPublished - 2017

Fingerprint

Phenotype
Genotype
Microbiota
Cytokines
Gene Expression

Keywords

  • Data integration
  • Gastrointestinal tract
  • Internal phenotype
  • Metabolomics
  • Microbiota
  • Proteomics
  • Systems biology
  • Transcriptomics

Cite this

@article{a7d482e4baa04793972c6a852031e673,
title = "Multi-level integration of environmentally perturbed internal phenotypes reveals key points of connectivity between them",
abstract = "The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them {"}connectivity hubs.{"} In literature, we found proof of 6{\%} of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.",
keywords = "Data integration, Gastrointestinal tract, Internal phenotype, Metabolomics, Microbiota, Proteomics, Systems biology, Transcriptomics",
author = "Nirupama Benis and Kar, {Soumya K.} and {Martins dos Santos}, {Vitor A.P.} and Smits, {Mari A.} and Dirkjan Schokker and Maria Suarez-Diez",
year = "2017",
doi = "10.3389/fphys.2017.00388",
language = "English",
volume = "8",
journal = "Frontiers in Physiology",
issn = "1664-042X",
publisher = "Frontiers",

}

TY - JOUR

T1 - Multi-level integration of environmentally perturbed internal phenotypes reveals key points of connectivity between them

AU - Benis, Nirupama

AU - Kar, Soumya K.

AU - Martins dos Santos, Vitor A.P.

AU - Smits, Mari A.

AU - Schokker, Dirkjan

AU - Suarez-Diez, Maria

PY - 2017

Y1 - 2017

N2 - The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them "connectivity hubs." In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

AB - The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them "connectivity hubs." In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

KW - Data integration

KW - Gastrointestinal tract

KW - Internal phenotype

KW - Metabolomics

KW - Microbiota

KW - Proteomics

KW - Systems biology

KW - Transcriptomics

U2 - 10.3389/fphys.2017.00388

DO - 10.3389/fphys.2017.00388

M3 - Article

VL - 8

JO - Frontiers in Physiology

JF - Frontiers in Physiology

SN - 1664-042X

M1 - 388

ER -