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Metabolites
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Metabolomics and Machine Learning for Improved Diagnostics and as a...
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Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 7823

Special Issue Editor

Dr. Elizabeth O’Day

E-Mail Website
Guest Editor
Olaris, Inc. 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA
Interests: metabolomics; machine learning; biomarkers; diagnostics; drug development; NMR; MS; precision medicine; personalized medicine; omics

Special Issue Information

Dear Colleagues,

Altered metabolism has been linked to nearly every disease, including cancer, neurodegeneration, cardiovascular disease, transplantation, aging and many more. Not unexpectedly, altered metabolites already provide powerful clinical biomarkers to diagnose diseases and guide treatments. However, due to challenges in analytical measurements, most clinical assays to date only measure a limited number of metabolites, leaving the true potential largely untapped.

With advancements in technology in NMR and MS, coupled with machine learning and a deeper understanding of biology, full metabolomics studies are on the horizon. The ability to measure thousands of metabolites in a single sample is now feasible. This should usher in a new era of clinical insights driving diagnostic innovation and accelerating drug development. However, reproducibility concerns, sample logistics, metabolite annotations and questions around complex statistics must be addressed. In this Special Issue, we highlight technical advancements (and lingering limitations) driving the field. Furthermore, we provide use cases in which metabolomics and machine learning are changing our ability to diagnose and treat disease. Finally, we provide tangible best practices and considerations for those looking to apply metabolomics and ML to their research.      

Dr. Elizabeth O’Day
Guest Editor

Manuscript Submission Information

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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
  • machine learning
  • biomarkers
  • diagnostics
  • drug development
  • NMR
  • MS
  • precision medicine
  • personalized medicine
  • omics

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Published Papers (4 papers)

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Research

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18 pages, 3846 KiB  
Article
Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning
by Mahsa Servati, Courtney N. Vaccaro, Emily E. Diller, Renata Pellegrino Da Silva, Fernanda Mafra, Sha Cao, Katherine B. Stanley, Aaron A. Cohen-Gadol and Jason G. Parker
Metabolites 2024, 14(6), 337; https://doi.org/10.3390/metabo14060337 - 16 Jun 2024
Viewed by 1566
Abstract
Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing [...] Read more.
Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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15 pages, 1576 KiB  
Article
Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning
by Tanvir Sajed, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam and David S. Wishart
Metabolites 2024, 14(5), 290; https://doi.org/10.3390/metabo14050290 - 19 May 2024
Cited by 3 | Viewed by 2062
Abstract
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound [...] Read more.
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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18 pages, 5080 KiB  
Article
Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery
by Masoumeh Dorrani, Jifang Zhao, Nihel Bekhti, Alessia Trimigno, Sangil Min, Jongwon Ha, Ahram Han, Elizabeth O’Day and Jurre J. Kamphorst
Metabolites 2024, 14(5), 280; https://doi.org/10.3390/metabo14050280 - 11 May 2024
Viewed by 1735
Abstract
Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity [...] Read more.
Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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Review

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24 pages, 377 KiB  
Review
An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples
by Amy Thachil, Li Wang, Rupasri Mandal, David Wishart and Tom Blydt-Hansen
Metabolites 2024, 14(9), 474; https://doi.org/10.3390/metabo14090474 - 28 Aug 2024
Cited by 1 | Viewed by 1316
Abstract
Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize [...] Read more.
Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze–thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage. Full article
(This article belongs to the Special Issue Metabolomics and Machine Learning for Improved Diagnostics and as a Tool to Accelerate Drug Development)
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