There are four biochemical components that control biological systems by serving as building blocks and as information databases: genes, transcripts, proteins, and metabolites. The study of these four components have become entire fields of biological study and have often been referred to collectively as the omics, including genomics, transcriptomics, proteomics, and metabolomics. The ability to study each of these biological components in great detail and to study the relationship between them has led to significant advances in medical discovery and understanding. The goal of medical systems biology is to integrate all biological information to understand mechanistic information about cellular events and functions that may contribute to disease propensity, development, progression, diagnosis, and/or treatment. Having a systems perspective on human biology is desirable, where details of various system components can be integrated with increasing complexity to better understand properties of the entire system. The systems-oriented approach requires extensive and complex datasets; reliable analytical techniques; thoughtful data integration across platforms; and advanced biostatistical methods. Medical systems biology necessitates an unbiased and comprehensive approach when interpreting experimental results and biological interpretations need to be carefully explained, justified by the data, and tested on larger data sets. Traumatic brain injury (TBI) patients would benefit from a medical systems biology understanding of the systemic dysregulation and cellular changes that follow an insult to the head. A subspecialty in the critical care environment, neurocritical care, evolved from the acceptance that recovery from the primary injury to the brain tissue is affected by systemic alterations that can result in secondary injuries to the brain. The neurological intensive care unit (ICU) has realized significant improvements in patient outcomes due to protocols to address and prevent secondary injuries and due to neurointensivist-led teamwork, both aided by modern technological advances in multimodality neuromonitoring (Elf et al., 2002, Le Roux et al., 2012, Varelas et al., 2006). Considering the notable advances achieved through incorporating a systems-level approach to treating head injury and improving outcomes, in this review we discuss metabolomics applied to TBI. First, we will introduce metabolomics for readers not familiar with the field. Second, we summarize research on the metabolic changes following TBI to highlight what information has been translated to the clinic and what treatments exist. Finally, we discuss metabolomics techniques applied to TBI metabolism, reviewing the examples in the literature, and offering the authors’ suggestions for using NMR spectroscopy to study biofluids from head injured patients. As researchers and clinicians report and validate metabolomics findings, building a medical systems biology perspective on post-TBI metabolic dysfunction is likely to aid in informing physicians’ decisions and in integrating treatments into daily practice. Metabolomics refers to the study of the metabolome, which has been defined as “the quantitative complement of metabolites in a biological system” (Dunn et al., 2011). A metabolome, estimated to contain thousands of compounds, is organism-specific and sample type–specific. The human serum metabolome has been reported to contain 4,229 unique compounds, detection of which involved the use of several analytic techniques, and is still not considered exhaustive (Psychogios et al., 2011). Metabolomics studies aim to discriminate pathological metabolic profiles from that of a normal physiological state and to predict class assignment based on this set of metabolite biomarkers (Baker, 2011; Holmes et al., 2008; Nicholson et al., 2012). The field of metabolomics research consists of several investigative methods. First, there is a distinction to be made between targeted and exploratory metabolomics studies (Lenz and Wilson, 2007). In the latter, the goal is to generate a metabolomic fingerprint for each case and to use multivariate analysis to probe class-specific patterns. Generally, the focus of such studies is not to identify and quantify metabolites nor to propose mechanistic explanations of the results, but rather to predict class assignment based on the metabolomic fingerprint. Targeted metabolomics studies aim to identify and quantify specific metabolites. These metabolites may be hypothesized to be biomarkers of disease progression or may be considered an indicator of the severity of a physiological state. Targeted metabolomics studies may use the same multivariate statistical techniques as the metabolome fingerprint-type studies, but also typically include more traditional univariate and multivariate analyses on the metabolite concentrations. Targeted studies can be targeted to a set of endogenous metabolites or can be targeted to study an exogenous substance, including labeled tracer metabolites or a pharmaceutical. Blood plasma, blood serum, urine, and cerebrospinal fluid (CSF) have been extensively investigated in the metabolomics literature. These biofluids are readily available and are interpreted as an average representation of the surrounding tissue. Researchers working with animal models have access to tissue after sacrifice, which is considerably rarer in human studies. As the field has grown, online metabolite databases containing biological, structural, and experimental information have been developed and are a key tool for metabolomics researchers (Ulrich et al., 2008; Wishart et al., 2007). The term metabolomics resulted from research in the 1980s and 1990s (Nicholson et al., 1999), yet the concept behind metabolomics was a focus of research for several decades prior. What distinguishes contemporary metabolomics studies from past studies on metabolic changes is the technology available for analyzing such biofluid samples and, therefore, the extent and accuracy of the metabolome quantified. In addition to the larger data set, there have also been computational and statistical advances that make the prospect of drawing meaningful conclusions from thousands of metabolites and the changes that occur between classes possible. With improvements in technology, metabolomics research has reached a level of complexity requiring a multidisciplinary team and has made providing biological rationale for the findings challenging because of data set complexity. The Institute of Medicine of the National Academies published a report on translational omics that issued recommendations for improving the overall quality of the metabolomics research and for translating these findings to the clinical setting (Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, 2012). The use of mass spectrometry (MS)-based and nuclear magnetic resonance (NMR)-based quantification are the most common in the metabolomics literature. Both of these analytical instruments are reliable, accurate, and widely available. There are advantages and disadvantages associated with each, some of which will be briefly mentioned, and the reader is referred to a number of excellent metabolomics review articles (Dunn et al., 2011; Lenz and Wilson, 2007; Nicholson et al., 1999). Because an individual’s metabolome is highly influenced by environment and diet, population studies require a large number of subjects, and the reliability and reproducibility of these analytical techniques is key. The focus of this review is NMR-based metabolomics applied to TBI, but both analytical methods will be described. The reader is referred to extensive review articles focused on the application of MS and/or NMR to metabolomics (Dettmer et al., 2007; Zhang et al., 2010). MS detects compounds in the picomolar concentration range that become ionized after injection into the mass spectrometer; the readout is the mass-to-charge ratio of the detectable compounds in solution. MS-based metabolomics have used gas chromatography MS and liquid chromatography MS. Preparing samples for MS analysis requires extraction of metabolites and may require derivitization, which can be a labor-intensive process. Metabolite extraction involves a series of experimental steps in which metabolite loss can occur and where additional sample-to-sample variability may be introduced. The high sensitivity of MS-based quantification makes it a powerful tool in targeted metabolomics studies. In metabolome fingerprinting studies, it is challenging to measure all compounds with the same efficiency and accuracy for technical reasons. NMR spectroscopy is used to identify and quantify compounds in solution containing elements that are magnetic resonance–detectable (i.e., elemental isotopes that will absorb photons when placed in a magnetic field). NMR is considerably less sensitive than MS and is able to detect concentrations in the micromolar concentration range, but does not destroy the sample in the process of measurement. Application of a radiofrequency field at a known frequency and power excites the spin of the magnetic resonance–detectable isotopes. Spin is a fundamental property of elements akin to mass and charge and both the absorption and emission of radiofrequency photons is nondestructive and noninvasive. Each unique chemical structure in a molecule will resonate in the magnetic field at a specific frequency as the spins relax to equilibrium alignment with the magnetic field. The signal collected by the NMR spectrometer is then Fourier transformed into a NMR spectrum with spectral peaks at specific frequencies corresponding to the molecular structure of the compound being measured. The integrated area of the spectral peaks is proportional to the concentration of the compound. All compounds in solution above a certain concentration will be detected, unlike the variable efficiency of MS-based quantification. There is minimal sample preparation required when compared with MS. There are a number of biologically relevant isotopes that can be measured, including 1H, 13C, 31P, and 15N. 1H is the most abundant isotope of hydrogen (99.99%) and, because biologically relevant molecules contain hydrogen, 1H NMR is widely used. NMR spectrometers are standard equipment in research environments and increased spectral resolution is possible due to the prevalence of high-field spectrometers with field strengths ≥400 MHz (9.4 T). High-resolution magic angle spinning spectroscopy is able to quantify metabolites in intact tissue using solid-state NMR spectrometers (Beckonert et al., 2010). Another aspect of modern metabolomics research is application of multivariate statistical approaches. Unsupervised multivariate techniques such as principal component analysis (PCA) reduce the number of variables to a few principal components. Principal components are orthogonal to one another, are linear combinations of the original data, and can reduce hundreds of input variables to three or four. There are many NMR-based metabolomics fingerprint-type studies that use the complete NMR spectrum as the set of variables. Some metabolomics studies are designed to build a prediction model with supervised multivariate techniques, for example partial least squares (PLS) or PLS-discriminant analysis (PLS-DA) among others (Bylesjo et al., 2006). Most metabolomics studies generate a PCA model of the data to test whether the groups can be reasonably separated based on metabolic information. To build a predictive model, validation is vital and the data set is randomly separated into a larger training set and a smaller test set; the model generated from the training set is then tested on the test set. In reality, metabolomics studies generally quantify fewer than 100 metabolites per sample. Several advances are required to achieve high-throughput quantification of the entire metabolome and to translate metabolomics to the clinical setting. The steps following data collection, including processing and statistical analyses, will be discussed later in this chapter within the context of metabolomics of TBI.
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