Metabolomics meets systems immunology

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Abstract

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Abstract

This review discusses recent technological developments of metabolomic applications in immunological research and provides guidance to researchers using metabolomics and multiomics in an immunity context to apply systems immunology to disease research.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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mixOmics: An R package for ‘omics feature selection and multiple data integration

Florian Rohart, Benoît Gautier, Amrit Singh … (2017)

The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

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HMDB 4.0: the human metabolome database for 2018

David Wishart, Yannick Djoumbou Feunang, Ana Marcu … (2017)

Abstract The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

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Author and article information

Contributors

Yang Li: yang.li@helmholtz-hzi.de

Journal

Journal ID (nlm-ta): EMBO Rep

Journal ID (iso-abbrev): EMBO Rep

Journal ID (doi): 10.1002/(ISSN)1469-3178

Journal ID (publisher-id): EMBR

Journal ID (hwp): embor

Title: EMBO Reports

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1469-221X

ISSN (Electronic): 1469-3178

Publication date (Electronic): 14 March 2023

Publication date Collection: April 2023

Publication date PMC-release: 14 March 2023

Volume: 24

Issue: 4 ( doiID: 10.1002/embr.v24.4 )

Electronic Location Identifier: e55747

Affiliations

[ ¹ ] Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH) Hannover Germany

[ ² ] TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH) Hannover Germany

[ ³ ] College of Pharmaceutical Sciences Zhejiang University Hangzhou China

[ ⁴ ] Department of Internal Medicine and Radboud Center for Infectious Diseases Radboud University Medical Center Nijmegen The Netherlands

Author notes

[*] [* ]Corresponding author. Tel: +49 511 220 02 7200; E‐mail: yang.li@ 123456helmholtz-hzi.de

Author information

Jianbo Fu https://orcid.org/0000-0003-3358-8106

Article

Publisher ID: EMBR202255747

DOI: 10.15252/embr.202255747

PMC ID: 10074123

PubMed ID: 36916532

SO-VID: 8894f0c7-d8e7-43c8-a089-39c6e8fe6e38

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

History

Date revision received : 24 December 2022

Date received : 08 July 2022

Date accepted : 24 February 2023

Page count

Figures: 5, Tables: 1, Pages: 18, Words: 20181

Funding

Funded by: Deutsche Forschungsgemeinschaft (DFG)

Award ID: 497673685

Funded by: EC | European Research Council (ERC)

Award ID: 948207

Funded by: Radboud University Medical Centre Hypatia Grant

Award ID: 2018

Funded by: Helmholtz Initiative and Networking Fund

Award ID: 1800167

Funded by: International Postdoctoral Exchange Fellowship Program 2021 between Helmholtz Centre for Infection Research (HZI), OCPC, and Zhejiang University (Chinese home organization)

Award ID: ZD202126

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source-schema-version-number 2.0

cover-date 05 April 2023

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:05.04.2023

ScienceOpen disciplines: Molecular biology

Keywords: infection,metabolomics,multiomics,statistical analysis,systems immunology,computational biology,immunology,metabolism

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ScienceOpen disciplines: Molecular biology

Keywords: infection, metabolomics, multiomics, statistical analysis, systems immunology, computational biology, immunology, metabolism

Metabolomics meets systems immunology

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Entomologia Generalis

Most cited references 250

WGCNA: an R package for weighted correlation network analysis

mixOmics: An R package for ‘omics feature selection and multiple data integration

HMDB 4.0: the human metabolome database for 2018

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