Charles W. Schmidt

Environ Health Perspect. 2004;112(7) 

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Metabolite Biomarkers

In the long run, scientists are looking to metabolomics to fill important gaps in systems biology, a research paradigm focused on all the interconnected molecular pathways in cells and organisms. Short-term clinical goals for the field are more concerned with the search for biomarkers, or molecular indicators of pathology.

Individual metabolites have already been used as disease biomarkers for years. Elevated glucose, for instance, is indicative of diabetes mellitus. And cholesterol is a metabolite long associated with heart disease and stroke. Metabolomics enables the identification of biomarkers based on entire groupings of metabolites that are up- or downregulated in unison under specific conditions.

Bruce Hammock, a distinguished professor of entomology in the UC Davis Cancer Research Center and director of the NIEHS-UC Davis Superfund Basic Research Program, says these metabolic profiles could broaden insights into the cause of disease. "High cholesterol might tell you that you have a problem, but if you supplement with five other measures, you could determine why you have the problem," he explains. "We might be able to say it's because your transport proteins are poor, or because you're eating too much fat, and so on. The profile will give you knowledge and information rather than just data."

Some experts believe metabolomics could provide clinical uses sooner than either genomics or proteomics. Several factors contribute to this view. First, metabolite profiles are comparatively cheap to generate, assuming the requisite instruments have already been purchased—once purchase costs are subtracted, the standard instruments, particularly NMR, can identify a sample's metabolite spectrum quickly for a few dollars. In contrast, the DNA microarrays used in genomics research cost hundreds to thousands of dollars and are often unavailable to clinicians, while protein analysis is time-consuming and hindered by the much larger size and complexity of the molecules, which have more functional components. Furthermore, the functions of most genes and proteins remain unknown, whereas metabolites can often be assigned to particular tissues and disease categories, which allows fairly easy extrapolation of theirfunctions.

Finally, metabolomics is noninvasive and allows for repeated sampling over time. Gene expression profiles, on the other hand, can be generated only from cells that have been impacted by disease, such as tumor cells. Proteins can be obtained from tissues and blood plasma, but not from urine, where they generally only appear as symptoms of illness. But metabolites are present in tissues, blood, saliva, and urine. Some biofluid samples can be linked to anomalies in particular tissues. For instance, urine is more likely to reflect renal disease, whereas saliva may more accurately reflect lung disease.

Metabolomic biomarkers do have their limitations, however. Donald Robertson, a scientist at Pfizer, says that in some cases metabolic responses—which vary greatly in terms of their dynamic range—are so far removed from the source of pathology that they are almost impossible to interpret. "Usually drugs or disease unleash a cascade of biomolecular effects throughout the body," he explains. "Many of these are subtle and below analytical detection limits." But Robertson adds that most metabolic changes could be detected if researchers knew what to look for.

In a sense, Robertson says, the limitations of metabolomics are the exact opposite of those posed by genomics. Whereas the genetic source of a disease might be too far "upstream" of the pathology to identify, metabolic changes might be too far "downstream," and diluted by the activities of proteins, the environment, and other intermediate biochemical events. Metabolomic profiles are also subject to random fluctuations, and can be influenced by diet, sleep patterns, age, smoking, and many other variables that mask the effects of disease or toxicity. Teasing biomarkers out from this background noise is a complex analytical and statistical challenge, scientists say, although one that ultimately should be achievable.

It is for these reasons and others, stresses Teresa Fan, an associate professor of chemistry at the University of Louisville, Kentucky, that scientists should view all the "-omics" sciences as complementary. "The bottom line is that you're not going to get the full picture with any one '-omic' technique," she says. "Looking at the genome won't tell you much about the downstream function, but looking at the metabolome won't tell you much about the underlying regulation. It's the whole integration that's important."

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