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Metabolites
Special Issues
Metabolomics and Multi-Omics Integration
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Metabolomics and Multi-Omics Integration

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

Deadline for manuscript submissions: closed (1 February 2020) | Viewed by 26072

Special Issue Editor

Dr. Ewy Mathé

E-Mail Website
Guest Editor
Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA
Interests: metabolomics; multi-omics data analysis; pathway analysis; cancer biomarkers; high-throughput data analysis

Special Issue Information

Dear Colleagues,

Metabolomics, the measurement of small molecule (<1500 Daltons) abundances in biospecimens such as blood, tissue, urine and breath, is playing an increasing role in epidemiological, clinical, translational, and basic research.  Metabolite profiles generated from these studies help define disease phenotypes and reflect alterations in the genome, epigenome, proteome, microbiome, and environment (exposures and lifestyle). Importantly, the interplay between analytes, including genes, proteins, metabolites, and microbes, is well known to affect cellular development and alterations in this interplay can lead to disease development.  The research community is thus increasingly generating large omics datasets to study disease processes and to uncover novel biomarkers and therapies.  With this in mind, the focus of this Special Issue is to highlight the latest developments in analytical and computational approaches and methodologies that integrate these large complex datasets to facilitate data interpretation and discovery of biological processes.

Dr. Ewy Mathé
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

  • Metabolomics
  • Multi-omics integration
  • Computational methods
  • Analytical methods
  • Statistical methods
  • Data interpretation
  • Biomarkers
  • Disease phenotypes

Published Papers (4 papers)

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Research

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13 pages, 1110 KiB  
Article
MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery
by Ziling Fan, Yuan Zhou and Habtom W. Ressom
Metabolites 2020, 10(4), 144; https://doi.org/10.3390/metabo10040144 - 8 Apr 2020
Cited by 13 | Viewed by 4265
Abstract
The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity [...] Read more.
The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods. Full article
(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)
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17 pages, 1562 KiB  
Article
Identifying Protein–metabolite Networks Associated with COPD Phenotypes
by Emily Mastej, Lucas Gillenwater, Yonghua Zhuang, Katherine A. Pratte, Russell P. Bowler and Katerina Kechris
Metabolites 2020, 10(4), 124; https://doi.org/10.3390/metabo10040124 - 25 Mar 2020
Cited by 19 | Viewed by 3339
Abstract
Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To [...] Read more.
Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein–metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein–metabolite network consisting of 13 proteins and 7 metabolites which had a −0.34 correlation (p-value = 2.5 × 10−28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a −0.27 correlation (p-value = 2.6 × 10−17) to percent emphysema. Protein–metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses. Full article
(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)
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14 pages, 1401 KiB  
Article
Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections
by Nataliya Chorna, Josefina Romaguera and Filipa Godoy-Vitorino
Metabolites 2020, 10(1), 36; https://doi.org/10.3390/metabo10010036 - 15 Jan 2020
Cited by 23 | Viewed by 4952
Abstract
In this study, we evaluate the association between vaginal and cervical human papillomavirus infections high-risk types (HPV+H), negative controls (HPV−), the bacterial biota, and urinary metabolites via integration of metagenomics, metabolomics, and bioinformatics analysis. We recently proposed that testing urine as a biofluid [...] Read more.
In this study, we evaluate the association between vaginal and cervical human papillomavirus infections high-risk types (HPV+H), negative controls (HPV−), the bacterial biota, and urinary metabolites via integration of metagenomics, metabolomics, and bioinformatics analysis. We recently proposed that testing urine as a biofluid could be a non-invasive method for the detection of cervical HPV+H infections by evaluating the association between cervical HPV types and a total of 24 urinary metabolites identified in the samples. As a follow-up study, we expanded the analysis by pairing the urine metabolome data with vaginal and cervical microbiota in selected samples from 19 Puerto Rican women diagnosed with HPV+H infections and HPV− controls, using a novel comprehensive framework, Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2). This approach enabled us to estimate the functional activities of the cervicovaginal microbiome associated with HPV+H infections. Our results suggest that HPV+H infections could induce changes in physicochemical properties of the genital tract through which niche partitioning may occur. As a result, Lactobacillus sp. enrichment coincided with the depletion of L. iners and Shuttleworthia, which dominate under normal physiological conditions. Changes in the diversity of microbial species in HPV+H groups influence the capacity of new community members to produce or consume metabolites. In particular, the functionalities of four metabolic enzymes were predicted to be associated with the microbiota, including acylphosphatase, prolyl aminopeptidase, prolyl-tRNA synthetase, and threonyl-tRNA synthetase. Such metabolic changes may influence systemic health effects in women at risk of developing cervical cancer. Overall, even assuming the limitation of the power due to the small sample number, our study adds to current knowledge by suggesting how microbial taxonomic and metabolic shifts induced by HPV infections may influence the maintenance of microbial homeostasis and indicate that HPV+H infections may alter the ecological balance of the cervicovaginal microbiota, resulting in higher bacterial diversity. Full article
(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)
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Review

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35 pages, 1844 KiB  
Review
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources
by Tara Eicher, Garrett Kinnebrew, Andrew Patt, Kyle Spencer, Kevin Ying, Qin Ma, Raghu Machiraju and Ewy A. Mathé
Metabolites 2020, 10(5), 202; https://doi.org/10.3390/metabo10050202 - 15 May 2020
Cited by 66 | Viewed by 12848
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical [...] Read more.
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study. Full article
(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)
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