Omic Studies of Neurodegenerative Disease - Part B

1. Auflage 2015
ISBN: 978-0-12-804763-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band Volume 122, 250 Seiten

Reihe: International Review of Neurobiology

Omic Studies of Neurodegenerative Disease: Part B is part of a well-established international series on neuroscience that examines major areas of basic and clinical research, along with emerging and promising subfields. The book informs the reader on the current state of the studies used to evaluate the mechanisms, causes, and treatment of neurodegeneration through a combination of literature reviews and examples of current research. - Provides the expertise of leading contributors in the field - Demonstrates how findings in the fields of genomics, proteomics, and metabolomic studies can combined for further insights - Informs the reader on the current state of the studies used to study the mechanisms, causes, and treatment of neurodegeneration through a combination of literature reviews and examples of current research

Autoren/Hrsg.

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Chapter Two Metabolomics of Neurodegenerative Diseases
Alejandro Botas*,1; Hannah Moore Campbell†,1; Xu Han‡,§; Mirjana Maletic-Savatic‡,§,¶,||,#,2    * BioSciences Department, Rice University, Houston, Texas, USA
† Medical Scientist Training Program, Baylor College of Medicine, Houston, Texas, USA
‡ Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
§ Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, Texas, USA
¶ Program in Developmental Biology, Baylor College of Medicine, Houston, Texas, USA
|| Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA
# Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA
2 Corresponding author: email address: [e-mail]
1 Equal contribution. Abstract
Neurodegenerative diseases are progressive, devastating, and terminal, carrying both personal and societal burden. Currently, their diagnosis depends on their clinical presentation. No quantitative biomarkers exist to enable early verdict and commencement of therapy. The lack of diagnostic biomarkers stems from the unavailability of brain tissue, the complexity and heterogeneity of the brain and neurodegenerative pathology, and the fact that peripheral tissues such as blood, urine, and even cerebrospinal fluid might not reflect early stages of brain pathology. Moreover, accumulated evidence indicates the majority of these diseases are not genetically inherited; rather, the genes bring about the risk to develop them, but the trigger is not known. As metabolites are at the intersection between the genetic background of a cell or a tissue and the environmental effects on the same, metabolomics has emerged as a field with great promise to deliver new, biologically, and clinically relevant biomarkers for neurodegenerative disorders. Here, we review the basic principles of metabolomics and focus on studies performed in most common neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Huntington's diseases, Multiple sclerosis, and Amyotrophic lateral sclerosis. Keywords Metabolomics Neurodegeneration Magnetic resonance spectroscopy Mass spectrometry The notion that changes in biological fluids can be “read” to reveal an organism's state of health or disease has been with us since ancient Greek physicians observed that ants were attracted to the sweet urine of diabetics. More recent, non-formican technologies such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry have furthered our ability to identify specific metabolites and quantify the products of cellular metabolism (de Graaf, 2007). The large-scale study of the small-molecule compounds (< 2 kDa) or metabolites in a particular biological sample is termed metabolomics (Dunn, Bailey, & Johnson, 2005; Hollywood, Brison, & Goodacre, 2006; Metz, 2011). The sum of all metabolites in the sample composes the metabolome, and the human metabolome is thought to contain about 42,000 metabolites (Wishart, 2007). These consist of both endogenous (products of the enzymatic reactions within the cell) and exogenous (ingested xenobiotic chemicals) small molecules that human body generates or comes in contact with. The level of each metabolite within the metabolome depends on the specific physiological, developmental, and pathological state of a cell or tissue (Weckwerth & Morgenthal, 2005). Therefore, the metabolome reflects the phenotype of a cell or tissue, which resulted in direct response to different genetic or environmental influences (Fiehn, 2002). As a field, metabolomics is a systems biology science similar to genomics, transcriptomics, proteomics, and other ‘omics sciences, focusing on the metabolome as a whole (Fig. 1). Metabolomics has a few advantages compared to other ‘omics studies. First, humans have far fewer metabolites than genes, RNA transcripts, or proteins. With fewer variables, it is easier to identify biologically meaningful patterns. Second, metabolites tend to be more conserved across species, from bacteria to mammals, and thus insights gained from metabolomic studies in model organisms can be more readily translated to human data. Further, metabolomics builds on more than 100 years of knowledge in biochemistry (German, Hammock, & Watkins, 2005), and thus, pathway mapping faces less difficulty compared to other ‘omics fields. Additionally, since metabolism is the product not just of an organism's native processes, but also of its interactions with the environment, we may glean information from metabolomics that cannot be found using other modes of study. Finally, metabolites are easily accessible in vivo, allowing the assessment of organismal health in real time, indispensable for medical diagnostics. Figure 1 Metabolomics is an integral part of several ‘omics systems biology approaches for studies of cellular function. 1 Metabolomics in Medicine
In the field of medicine, metabolomics has two prime objectives: (1) identification of aberrant metabolites and metabolic pathways to generate hypotheses about the etiology of a given disease (Jove, Portero-Otin, Naudi, Ferrer, & Pamplona, 2014) and (2) identification of aberrant metabolites for discovery of reliable and reproducible biomarkers for early diagnosis of disease or for therapeutic monitoring. The logic and practicality behind the first goal is relatively straightforward—metabolomics is a new field and rapidly progressing technologies and data analysis tools are increasingly allowing a new dimension for studies of disease pathology. Importantly, metabolomics also provides the unique opportunity for discovery of new biomarkers. Identification of clinically relevant biomarkers of neurological diseases, including neurodegenerative diseases, has been notoriously challenging due to the unavailability of the human brain tissue for investigations, the complexity and heterogeneity of the brain, and the deficiency of models for functional validation of candidate biomarkers. A recent review of metabolomics and human brain aging (Jove et al., 2014), pointed to a study revealing that of 900 patients in the United States with Alzheimer's disease (AD), the most prevalent of neurodegenerative diseases, clinical diagnosis was inconsistent with autopsy diagnosis 17–30% of the time (Beach, Monsell, Phillips, & Kukull, 2012). In addition, diagnosis often comes too late because in most cases it relies on clinical and/or radiological presentation. However, the underlying pathology of many neurodegenerative diseases precedes the appearance of symptoms, sometimes by years or even decades (Trushina & Mielke, 2014). As metabolome is a dynamic and sensitive biological system, reflective of both innate processes and environmental influences, it encodes to a great extent an organism's health and homeostasis. Thus, early or even presymptomatic diagnosis could be possible by metabolomics biomarkers of disease, as some studies have demonstrated. However, research in this field faces several challenges, both technological and biological. 2 Metabolomics Biomarkers of Disease
A biomarker is “a measurable substance in an organism whose presence is indicative of some phenomenon such as disease, infection, or environmental exposure.” In the field of metabolomics, however, a biomarker may frequently be a composite of several molecules as metabolites are in constant flux. In addition, as the human metabolome comprises both endogenous and exogenous compounds whose concentrations heavily depend on the tissue being analyzed, it is very important to link the metabolomics biomarker not only to a given disease but also to the tissue where it is found. As illustrated in Fig. 2, the search for metabolomics biomarkers of disease starts from the sample that is analyzed by a chosen platform, followed by complex analytical approaches that can be divided into two main types: chemometric analysis and targeted metabolic profiling (Richards et al., 2010; Wishart, 2007). The aim of chemometric analysis is to discern the spectral profiles of biofluids and tissues in normal and disease states, not to identify metabolites per se (Wishart, 2007). In contrast, the aim of targeted metabolic profiling is to identify and quantify metabolites in a sample by comparison with a reference library of experimentally acquired pure compound spectra (Weljie, Newton, Mercier, Carlson, & Slupsky, 2006). Eventually, both pattern analysis and identification of a specific metabolite may lead to biomarker discovery (Maletic-Savatic et al., 2008). Figure 2 Schematic representation of metabolomics flow, from samples to biomarker discovery. The choice of tissue sample for metabolomics biomarker discovery in neurodegenerative diseases is critical. The biggest challenge is that neurodegeneration starts locally and...