The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses

Abstract

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a "snapshot" reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis.

Figures

Figure 1.

Identification of clinically relevant biomarkers requires a rigorous multistep process that can take years and includes analytical and prospective validation to demonstrate accuracy and prognostic value of the marker(s). The qualification phase includes the validation of the marker. This also encompasses the assessment of assay accuracy and the association of the marker with a clinical feature or outcome. Presently, the use of metabolomics for this purpose in critical illnesses is in the exploratory phase.

Figure 2.

Schematic representation of the “top-down” relationship between the components of a systems biology approach. Metabolomics is part of the continuum of systems biology and represents a read of gene function and the physiological landscape. Pathological events can be measured by changes in the genome, transcriptome, proteome, and metabolome. Integration of these systems biology components can be viewed as host-specific rather than tissue- or site-specific. Bioinformatics is a key element in data management and analysis of collected data sets arising from genomics, transcriptomics, proteomics, and metabolomics. Identified proteins and metabolites can be assessed by fast and highly specific clinical laboratory assays in the body fluids or, ultimately, by tracer-based molecular imaging. Magnetic resonance imaging/spectroscopical imaging (MRI/MRSI) and positron emission tomography (PET) provide attractive noninvasive platforms for biomarker detection in organs. (Partially adapted from Reference .)

Figure 3.

Representative high-resolution 1H-nuclear magnetic resonance (NMR) spectra obtained by a Bruker500 MHz spectrometer. Spectra peak sizes are directly proportional to metabolite concentration, which is an advantage of NMR compared with MS. Peak positions on the x-axis are indicative of the chemical properties of metabolites and allow for metabolite identification. Body fluids containing water-soluble small-molecule metabolites do not require any sample extraction such as (A) human expressed prostatic secretions (EPS), or (B) rat urine. Blood products that contain large lipoproteins and other hydrophobic molecules will yield more metabolic information if extracted: (C) nonextracted human serum versus (D) hydrophilic fraction of the whole rat blood after dual methanol/ chloroform extraction. Finally, tissue samples require either extraction (E) hydrophilic fraction of a human brain biopsy (9 mg) after dual acid extraction using a 1-mm Bruker microprobe, or (F) use of high-resolution magic-angle-spinning (HR-MAS) NMR on intact rat muscle. Selected peak assignments: (B) rat urine: 1, valine, leucine, isoleucine; 2, lactate; 3, CH3-acetyl groups; 4, succinate; 5, 2-oxoglutarate; 6, citrate; 7, creatinine (and creatine); 8, trimethylamine-N-oxide (TMAO with betaine and taurine); 9, trans-aconitate; 10, hippurate; 11, allantoin; 12, urea; 13, trans-aconitate. (D) Extracted blood: 1, valine, leucine, isoleucine; 2, hydroxybutyrate; 3, threonine; 4, lactate; 5, alanine; 6, arginine and lysine; 7, acetate; 8, CH3-acetyl groups; 9, glutamate and hydroxybutyrate; 10, glutamine; 11, total glutathione; 12, reduced glutathione (GSH); 13, creatine and creatinine; 14, trimethylamine-N-oxide (TMAO with betaine, taurine, glucose and arginine). (E) Extracted brain biopsy: 1, valine, leucine, isoleucine; 2, threonine; 3, lactate; 4, alanine; 5, N-acetyl aspartate (NAA); 6, CH3-acetyl groups; 7, glutamate; 8, glutamine; 9, xenobiotics; 10, phosphocreatine and creatine; 11, cholines and taurine; 12, glucose; 13, myo-inositol. Abbreviations: Cho = cholines; Cr = creatine; Lac = lactate; Tau = taurine; TSP = d-trimethyl-silyl-propionic acid; UFA = unsaturated fatty acids; (Adapted from Reference 13).

Figure 4.

A representative three-step scheme for nuclear magnetic resonance (NMR)-based metabolomics analysis using human transplantation (Tx) as an example. Step 1: Analysis begins with NMR spectral data processing and pattern recognition. (A) From raw spectra (B) spectral patterns and intensities are recorded and compared (compounds are not initially identified). (C) This finds relevant spectral features so that sample groups (e.g., treatment failure versus success) can be distinguished. Step 2: Metabolite identification typically starts with an extension of pattern recognition using principal component analysis (PCA). (A) Metabolites can be subjected to analysis by (B) PCA software to (C) identify which metabolites are responsible for the patterns observed in step 1. Step 3: To quantify metabolites, spectra are matched to those in a spectral library. Two examples of metabolite quantification (“validation”) are shown here in which longitudinal changes in amounts of (A) lactate and (B) glutamine were indicators of outcome. (Partly adapted from Reference .)