Metabolomics and Its Application to Acute Lung Diseases

Abstract

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

Keywords: acute respiratory distress syndrome; biomarkers; environmental exposure; mass spectroscopy; metabolites; nuclear magnetic resonance; pneumonia; precision medicine.

Figure 1

The metabolome is tightly connected with other “omes.” The metabolome interacts and reflects the activity of the genome, transcriptome, and proteome.

Figure 2

Analysis of metabolites by nuclear magnetic resonance (NMR). Samples are inserted into a magnet from which FID data are collected and analyzed to generate spectra. Positions of metabolites are determined by multiple peaks occurring across a spectrum that correspond to purified standards for each individual metabolite. Areas under the peak curve correspond to the concentration of the metabolite. Shown here is a human urine sample with urea, creatinine, and citrate shown as a few examples of metabolites present in the sample.

Figure 3

Representative mass spectroscopy (MS) spectrum following high performance liquid chromatography (HPLC). Initial data that are generated from liquid chromatography (e.g., HPLC, shown as an example in upper panel) which is often conducted prior to MS analysis (lower panel). The MS spectrum shows numerical values that correspond to the mass-to-charge ratio (m/z, x-axis) and relative intensity (y-axis) for each detected metabolite.

Figure 4

Analytical workflow of liquid chromatography (LC)-mass spectroscopy (MS). (A) An illustration of what happens to molecules during LC-MS. Neutral molecules may be ionized using a number of different techniques, but electrospray ionization is frequently used. Following ionization, negatively and positively charged compounds are generated. LC-MS conducted in negative and positive modes will detect negatively and positively charged ions, respectively. The read-out is a graphic representation of compounds as shown in Figure 3. (B) Elaborate equipment is needed to conduct LC-MS metabolomics. The initial step is chromatography followed by ionization and mass analysis of the molecules.

Figure 5

Representative scheme of silylation. In this case, silylation using N-methyl-N(trimethylsilyl) trifluoroacetamide (MSTFA) as a type of derivatization can be done in preparation for gas chromatography (GC)-mass spectroscopy (MS).

Figure 6

Different methods of analysis of metabolomic data. In this example, NMR spectra collected from control and diseased subjects may be analyzed by untargeted “binning” or targeted profiling, either of which can be subjected to PCA or PLS plotting.

Figure 7

Metscape network showing metabolites that differentiated ARDS BAL fluid samples from those of healthy controls. Red nodes represent experimentally measured metabolites that were used by Metscape as seeds for building the metabolic network. The program also provides information about metabolic reactions (gray nodes), metabolic enzymes (green nodes), and genes (light blue nodes). The most significant BAL metabolites of ARDS were those associated with purine metabolism, specifically hypoxanthine, xanthine, and guanosine.

Figure 8

Range of sensitivities of metabolomic technologies. At the lower end of sensitivity or lower detection limit (LDL), NMR is suitable for detection of smaller numbers of known metabolites, while at the higher end of sensitivity (at right), MS-based technologies are superior for detection of known as well as unknown metabolites. Adapted with permission from Wishart (6).

Figure 9

Differentiating between different types of pneumonia in human patients. Urinary metabolites were found to be distinct in pneumonia caused by S. pneumoniae and other pathogens. These graphs show OPLS-DA models based on 61 measured metabolites found in the urine from S. pneumoniae patients compared with those found in viral pneumonia and other bacteria (including Mycoplasma tuberculosis, Legionella pneumophila, S. aureus, and others). Note that the labeling for S. pneumoniae is shown in red at left while this is black in the middle and right panels. Adapted with permission from Slupsky et al. (92).

Figure 10

Distinct metabolic profiles in animals infected with S. pneumoniae and S. aureus. An inbred strain of mice (C57BL/6), maintained in specific virus antigen-free housing with autoclaved bedding and dietary supplies, was infected intratracheally with a clinical isolate of S. pneumoniae, serotype 14. After 24 h of infection, bronchoalveolar lavage (BAL) samples were analyzed for cell counts (A) and histology was carried out on lung sections (B) to confirm inflammation arising from infection. At the same time, urine samples were collected from animals that were subjected to NMR analysis, and a PCA model of urinary metabolite concentrations was generated (C). Macs, macrophages; PMNs, polymorphonuclear neutrophils. Adapted with permission from Slupsky (13). Copyright 2009 American Chemical Society.

Figure 11

Progression of disease in ARDS. The clinically challenging problem of acute respiratory distress syndrome (ARDS) is illustrated by the diversity in the underlying etiologies and the complex time course of the disease. Approximately 40% of patients with severe sepsis will develop ARDS. Patients who do not recover during the proliferative phase may go on to develop emphysematous regions in the lungs and ultimately fibrosis. While it is reasonable to expect that each of these phases will have a distinct metabolomics phenotype, these have yet to be realized. Reproduced with permission from MacLaren and Stringer (104). Illustration of lungs from "Lungs diagram simple" by Patrick J. Lynch, medical illustrator. Licensed under CC BY 2.5 via Wikimedia Commons – http://commons.wikimedia.org/wiki/File:Lungs_diagram_simple.svg#mediaviewer/File:Lungs_diagram_simple.svg.