Metabolomics for Biomedical Research

Jerzy Adamski, Jerzy Adamski

• Editorial: Academic Press
• Año: 2020
• ISBN:
  9780128127858
• Categoría: Medicine
• Categoría: Medical Education

Metabolomics for Biomedical Research brings together recent progress on study design, analytics, biostatistics and bioinformatics for the success of metabolomics research. Metabolomics represents a very interdisciplinary research prominent in the functional analyses of living systems; hence, this book focuses on translation and medical aspects. The book discusses topics such as biomarkers and their requirements to be used in medical research, with the parameters and approaches on how to validate their quality; and animal models and other approaches, as stem cells and organoid culture. Additionally, it explains how metabolomics may be applied in prediction of individual response to drug or disease progression.

This book is a valuable source for researchers on systems biology and other members of biomedical field interested in metabolism-oriented studies for medical research.

• Focuses on metabolomics in translational and medical research
• Provides basics for, and concepts of, contemporary translational personalized medicine research with metabolomics
• Brings the major recent progresses on design, analytics, biostatistics and bioinformatics relating to the success of metabolomics research

Chapter 1

Introduction to metabolomics

Jerzy Adamski Helmholtz Zentrum München, Research Unit Molecular Endocrinology and Metabolism, Neuherberg, Germany

Abstract

Metabolomics addresses a comprehensive view of metabolites depicting biological and nonenzymatic processes. The metabolomics requests integrative application of epidemiology, analytics, and bioinformatics and provides information on specific metabolic signatures in health and disease. Processes behind a metabolomics experiment are described including study design, sample preparation, choice of analytical methods, and data processing. Major trends in future research are reviewed.

Keywords

Metabolomics; Study design; Confounders; Preanalytics; Quality assurance; Randomization; Replication; Precision medicine

Acknowledgments

I would like to thank my collaborators Dr. Janina Tokarz and Dr. Alexander Cecil from Helmholtz Zentrum München for their comments and critical review of this chapter.

1 What is metabolomics?

Living organisms are predetermined by their biological structures in how they could react to intrinsic or environmental challenges. While the genome contains a blueprint of an organism, the proteome and the metabolome are the functional agents. Thus, genomics can read out the predictive information, but proteomics and metabolomics depict ongoing processes [1, 2].

Metabolomics signatures (including the presence, concentrations, and changes of them) of metabolites originate from a biological system. This living system is represented by a sample taken from an organism, a cell culture or an environment under specific conditions. Further, the sample can be taken from an environment where organic life is no longer present but has left metabolites behind, for example, deep sea sediments or raw oil. The term metabolome is used to address the complete set of metabolites in a selected sample.

Developments in metabolomics resulted in improvement of analytical methods in their sensitivity and reproducibility, larger metabolite coverage, and higher sample throughput. This, in turn, promoted a growing number of applications in biomedical research, as well as applications in crop and food quality. Metabolomics is a very integrative research activity (Fig. 1).

Fig. 1 Metabolomics is an integrative research discipline.

The research in metabolomics combines elements of study design, analytics, and bioinformatics. Metabolomics profits from strategies and tools developed in each discipline. The study design benefits from approaches in epidemiological research, while bioinformatics profits from that in genomics. The specific contributions may vary depending on the scientific question addressed and have to be adapted to the requirements in a metabolomics experiment. These requirements may request different sample randomization [3] or data imputation [4, 5]. Because of the presence of multiple variables in sample preparation and analytics, the research in metabolomics requests specific (i.e., distinct to that of other omics) quality control (QC) and assurance procedures [6]. Analytical technologies that are used in metabolomics include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry coupled either to gas chromatography (GC-MS) or to liquid chromatography (LC-MS) [1, 7]. The number of compounds identified in metabolomics approaches exceeds over 100,000 individual metabolites but there are many compounds not resolved beyond their mass spectra [8]. Contrary to genomics, the metabolomics does not depict the whole metabolome in a given sample as yet.

2 Flow chart of metabolomic research

There are several phases of development of a metabolomic project (Fig. 2).

Fig. 2 Workflow of a metabolomics experiment. Different steps are interdependent and request distinct expertise.

Distinct phases are schematically placed over a spiral drawn in Fibonacci numbers proportions. This representation does not imply any mathematical structure in study design. The spiral rather reflects the impact of changes at a single level of freedom which then projects into the increase in complexity in the next step of experiment. Fibonacci approach originally explained the development of population [9]. This approach found further practical use in addressing study design in clinical studies [10, 11] and description or prediction of molecular lipid species [12].

2.1 Scientific question

The metabolomics project may start in any research field requesting assessment of changes or functional explanation of a phenotype by the use of metabolite analyses. Such requests are usually addressing an issue unsolved so far. The scientific question has to be very specific and seek for explanations than sole differences. A simple comparison of control with challenged sample will always show some differences but will not take full advantage of metabolomics. The latter has its strength in mechanistic analyses of the effects. In this step, the decision on the study model (i.e., human or animal) and type of metabolite detection (i.e., NMR or LC-MS) has to be made. The decision on analytics determines as well future replication...