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Biomedicines
Special Issues
Metabolomics Technology and Bioinformatics for Precision Medicine
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Metabolomics Technology and Bioinformatics for Precision Medicine

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2907

Special Issue Editor

Dr. Paniz Jasbi

E-Mail Website
Guest Editor
Biodesign Center for Personalized Diagnostics, School of Molecular Sciences, Arizona State University, Phoenix, AZ 85004, USA
Interests: bioinformatics; biomedical diagnostics; mass spectrometry; metabolomics; systems biology

Special Issue Information

Dear Colleagues,

Metabolomics, the interdisciplinary study of metabolites, has seen tremendous advancements since both its initial practice by ancient Greek, Arab, and Chinese scholars, as well as its formal introduction to the scientific literature in the late 1990s. Given its proximity to phenotype, the metabolome has proven to be a sensitive and reliable indicator of health. In the field of precision medicine, metabolomics has facilitated numerous clinical advancements in diagnostic testing, prognostic staging, drug target identification, patient stratification, and therapeutic monitoring.

Despite this remarkable progress, numerous challenges still impede the full realization of metabolomics-based precision medicine including metabolome coverage, compound identification, data processing and analysis, and biomarker validation, among others. Regrettably, these barriers have stymied efforts to achieve optimal individual health decisions.

I cordially invite authors and investigators within this complex and dynamic field to submit original research, reviews, communications, and opinions pertaining to metabolomics technology and bioinformatics for precision medicine. Potential topics include but are not limited to:

  • Analytical chemistry;
  • Developments in nuclear magnetic resonance, mass spectrometry, or other analytical platforms;
  • Bioinformatic techniques, particularly artificial intelligence and Bayesian inference;
  • Development and implementation of specialized chemical databases for compound identification, spectral processing, or comparative analysis.

Dr. Paniz Jasbi
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. Biomedicines 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 2600 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

  • artificial intelligence
  • bioinformatics
  • mass spectrometry
  • metabolites
  • metabolomics
  • nuclear magnetic resonance
  • personalized
  • precision medicine

Published Papers (2 papers)

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Research

17 pages, 1479 KiB  
Article
Uremic Toxins and Inflammation: Metabolic Pathways Affected in Non-Dialysis-Dependent Stage 5 Chronic Kidney Disease
by María Peris-Fernández, Marta Roca-Marugán, Julià L. Amengual, Ángel Balaguer-Timor, Iris Viejo-Boyano, Amparo Soldevila-Orient, Ramon Devesa-Such, Pilar Sánchez-Pérez and Julio Hernández-Jaras
Biomedicines 2024, 12(3), 607; https://doi.org/10.3390/biomedicines12030607 - 07 Mar 2024
Viewed by 827
Abstract
Chronic kidney disease (CKD) affects approximately 12% of the global population, posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of non-dialysis-dependent (NDD) stage 5 CKD, contributing to elevated cardiovascular and overall mortality in affected individuals. This study [...] Read more.
Chronic kidney disease (CKD) affects approximately 12% of the global population, posing a significant health threat. Inflammation plays a crucial role in the uremic phenotype of non-dialysis-dependent (NDD) stage 5 CKD, contributing to elevated cardiovascular and overall mortality in affected individuals. This study aimed to explore novel metabolic pathways in this population using semi-targeted metabolomics, which allowed us to quantify numerous metabolites with known identities before data acquisition through an in-house polar compound library. In a prospective observational design with 50 patients, blood samples collected before the initial hemodialysis session underwent liquid chromatography and high-resolution mass spectrometer analysis. Univariate (Mann–Whitney test) and multivariate (logistic regression with LASSO regularization) methods identified metabolomic variables associated with inflammation. Notably, adenosine-5′-phosphosulfate (APS), dimethylglycine, pyruvate, lactate, and 2-ketobutyric acid exhibited significant differences in the presence of inflammation. Cholic acid, homogentisic acid, and 2-phenylpropionic acid displayed opposing patterns. Multivariate analysis indicated increased inflammation risk with certain metabolites (N-Butyrylglycine, dimethylglycine, 2-Oxoisopentanoic acid, and pyruvate), while others (homogentisic acid, 2-Phenylpropionic acid, and 2-Methylglutaric acid) suggested decreased probability. These findings unveil potential inflammation-associated biomarkers related to defective mitochondrial fatty acid beta oxidation and branched-chain amino acid breakdown in NDD stage 5 CKD, shedding light on cellular energy production and offering insights for further clinical validation. Full article
(This article belongs to the Special Issue Metabolomics Technology and Bioinformatics for Precision Medicine)
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14 pages, 2038 KiB  
Article
Determination of the Minimum Sample Amount for Capillary Electrophoresis-Fourier Transform Mass Spectrometry (CE-FTMS)-Based Metabolomics of Colorectal Cancer Biopsies
by Tetsuo Sugishita, Masanori Tokunaga, Kenjiro Kami, Kozue Terai, Hiroyuki Yamamoto, Hajime Shinohara and Yusuke Kinugasa
Biomedicines 2023, 11(6), 1706; https://doi.org/10.3390/biomedicines11061706 - 13 Jun 2023
Cited by 3 | Viewed by 1231
Abstract
The minimum sample volume for capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) useful for analyzing hydrophilic metabolites was investigated using samples obtained from colorectal cancer patients. One, two, five, and ten biopsies were collected from tumor and nontumor parts of the surgically removed specimens [...] Read more.
The minimum sample volume for capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) useful for analyzing hydrophilic metabolites was investigated using samples obtained from colorectal cancer patients. One, two, five, and ten biopsies were collected from tumor and nontumor parts of the surgically removed specimens from each of the five patients who had undergone colorectal cancer surgery. Metabolomics was performed on the collected samples using CE-FTMS. To determine the minimum number of specimens based on data volume and biological interpretability, we compared the number of annotated metabolites in each sample with different numbers of biopsies and conducted principal component analysis (PCA), hierarchical cluster analysis (HCA), quantitative enrichment analysis (QEA), and random forest analysis (RFA). The number of metabolites detected in one biopsy was significantly lower than those in 2, 5, and 10 biopsies, whereas those detected among 2, 5, and 10 pieces were not significantly different. Moreover, a binary classification model developed by RFA based on 2-biopsy data perfectly distinguished tumor and nontumor samples with 5- and 10-biopsy data. Taken together, two biopsies would be sufficient for CE-FTMS-based metabolomics from a data content and biological interpretability viewpoint, which opens the gate of biopsy metabolomics for practical clinical applications. Full article
(This article belongs to the Special Issue Metabolomics Technology and Bioinformatics for Precision Medicine)
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