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Metabolites
Special Issues
Integrative Metabolomics
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Integrative Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Integrative Metabolomics".

Deadline for manuscript submissions: closed (31 January 2014) | Viewed by 30746

Special Issue Editor

Dr. Miroslava Cuperlovic-Culf

E-Mail Website
Guest Editor
National Research Council Canada, 1200 Montreal Road, M-50 Room 353, Ottawa, ON K1A 0R6, Canada
Interests: metabolomics; metabolism modelling; computational biology; biomarker discovery; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data emerging from individual omics approaches are often insufficient to fully understand interactions and functions of biomolecules or processes underway in biological systems. Regardless of the number of molecules observed by any omics platform, isolation of each of these platforms still provides only a limited window into the biological activity of a system under study. In addition, omic measurements are often hampered by sampling issues (insufficient number of samples) as well as intrinsic experimental errors such as non-specific binding problems, over-lapping peaks and low sensitivity as well as assignment issues. Further, a lack of direct correlation between gene expression, protein expression and pathway activation and thus metabolite concentration leaves many unanswered questions. Integrated analysis of high throughput molecular data termed integromics or polyomics has been suggested for several years as a possible avenue to overcome the limitations of individual omics methods, in terms of intrinsic errors as well as biological process coverage, thus helping in furthering our understanding of biological systems as a whole. To understanding phenotype characteristics and to further define the biological processes that are leading to observed properties, it is arguably most appropriate to investigate cross-correlation of metabolic data with genetic, epigenetic and proteomics data.
In this issue we will consider research papers and reviewer that are focusing on integration of different types of omics data with metabolomics results aimed towards better understanding or more detailed description and models of biological systems, phenotype characteristics or phenotype changes. Manuscripts dealing with applications of polyomics approach or with the development for polyomics analysis tools are highly desired.

Miroslava Cuperlovic-Culf
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Integromics
  • Polyomics
  • Data integration
  • Meta-analysis
  • Systems biology
  • Computational biology
  • Bioinformatics

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Published Papers (3 papers)

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Research

31 pages, 975 KiB  
Article
Transcriptomics and Metabonomics Identify Essential Metabolic Signatures in Calorie Restriction (CR) Regulation across Multiple Mouse Strains
by Sebastiano Collino, François-Pierre J. Martin, Ivan Montoliu, Jamie L. Barger, Laeticia Da Silva, Tomas A. Prolla, Richard Weindruch and Sunil Kochhar
Metabolites 2013, 3(4), 881-911; https://doi.org/10.3390/metabo3040881 - 11 Oct 2013
Cited by 10 | Viewed by 10273
Abstract
Calorie restriction (CR) has long been used to study lifespan effects and oppose the development of a broad array of age-related biological and pathological changes (increase healthspan). Yet, a comprehensive comparison of the metabolic phenotype across different genetic backgrounds to identify common metabolic [...] Read more.
Calorie restriction (CR) has long been used to study lifespan effects and oppose the development of a broad array of age-related biological and pathological changes (increase healthspan). Yet, a comprehensive comparison of the metabolic phenotype across different genetic backgrounds to identify common metabolic markers affected by CR is still lacking. Using a system biology approach comprising metabonomics and liver transcriptomics we revealed the effect of CR across multiple mouse strains (129S1/SvlmJ, C57BL6/J, C3H/HeJ, CBA/J, DBA/2J, JC3F1/J). Oligonucleotide microarrays identified 76 genes as differentially expressed in all six strains confirmed. These genes were subjected to quantitative RT-PCR analysis in the C57BL/6J mouse strain, and a CR-induced change expression was confirmed for 14 genes. To fully depict the metabolic pathways affected by CR and complement the changes observed through differential gene expression, the metabolome of C57BL6/J was further characterized in liver tissues, urine and plasma levels using a combination or targeted mass spectrometry and proton nuclear magnetic resonance spectroscopy. Overall, our integrated approach commonly confirms that energy metabolism, stress response, lipids regulators and the insulin/IGF-1 are key determinants factors involved in CR regulation. Full article
(This article belongs to the Special Issue Integrative Metabolomics)
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18 pages, 1976 KiB  
Article
Physiological and Molecular Timing of the Glucose to Acetate Transition in Escherichia coli
by Brice Enjalbert, Fabien Letisse and Jean-Charles Portais
Metabolites 2013, 3(3), 820-837; https://doi.org/10.3390/metabo3030820 - 20 Sep 2013
Cited by 32 | Viewed by 8080
Abstract
The glucose-acetate transition in Escherichia coli is a classical model of metabolic adaptation. Here, we describe the dynamics of the molecular processes involved in this metabolic transition, with a particular focus on glucose exhaustion. Although changes in the metabolome were observed before glucose [...] Read more.
The glucose-acetate transition in Escherichia coli is a classical model of metabolic adaptation. Here, we describe the dynamics of the molecular processes involved in this metabolic transition, with a particular focus on glucose exhaustion. Although changes in the metabolome were observed before glucose exhaustion, our results point to a massive reshuffling at both the transcriptome and metabolome levels in the very first min following glucose exhaustion. A new transcriptional pattern, involving a change in genome expression in one-sixth of the E. coli genome, was established within 10 min and remained stable until the acetate was completely consumed. Changes in the metabolome took longer and stabilized 40 min after glucose exhaustion. Integration of multi-omics data revealed different modifications and timescales between the transcriptome and metabolome, but both point to a rapid adaptation of less than an hour. This work provides detailed information on the order, timing and extent of the molecular and physiological events that occur during the glucose-acetate transition and that are of particular interest for the development of dynamic models of metabolism. Full article
(This article belongs to the Special Issue Integrative Metabolomics)
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20 pages, 1066 KiB  
Article
Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile
by Larissa Stanberry, George I. Mias, Winston Haynes, Roger Higdon, Michael Snyder and Eugene Kolker
Metabolites 2013, 3(3), 741-760; https://doi.org/10.3390/metabo3030741 - 3 Sep 2013
Cited by 44 | Viewed by 11750
Abstract
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial [...] Read more.
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. Full article
(This article belongs to the Special Issue Integrative Metabolomics)
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