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MEETING NEWS
Diet and time of day strongly influence metabolomic studies Dean Jones and colleagues at Emory University’s School of Medicine have put the adage “You are what you eat” to the test. They have run 1H-NMR spectra of plasma samples collected throughout the day from various subjects and can distinguish samples taken in the morning (while subjects are fasting and after breakfast) from those taken in the afternoon or evening (when subjects have eaten two or more meals). In fact, they determined that the time of day (i.e., time since last meal) accounted for 79% of the total variation in the NMR spectra, whereas only 21% corresponded to differences among individuals. The researchers also manipulated the subjects’ diets and could group the spectra on the basis of which diet the participants were fed. “In a certain way, it’s predictable that there would be time-of-day variation,” Jones says. Researchers have conducted many studies that show how the levels of particular nutrients change during the day. Knowing this, doctors who want to measure analytes such as glucose collect samples while the patients are fasting. But Jones adds, “I would never have expected that the magnitude would be as great as it is.” Jones’s group investigates the link between oxidative stress and human health. They are particularly interested in glutathione and cysteine, a critical precursor of glutathione. Because cysteine is found in so many human proteins, they needed a global method— metabolomics—to look for links between the oxidation–reduction state of cysteine and disease. They chose 1H-NMR because it is comprehensive. “All we were going to do is look at the spectrum as a function of time of day and then compare that back to the measured change in the thiol/disulfide redox state,” Jones explains. “At that
National Meeting—San Francisco point, I had no expectation of what we might see.” In the first experiment, Jones and colleagues took blood samples hourly from 8 subjects who were fed standardized meals at 9 a.m., 1 p.m., 6 p.m., and 10 p.m. A total of 200 samples were collected (25 time points per subject). The NMR spectra were subjected to both principal component analysis, which separates classes according to the
PHOTODISC
Elizabeth Zubritsky reports from the ACS
differences, and k-means clustering, which groups together the most similar data. The two methods approach the problem from opposite directions, but both yielded the same three classifications: morning, afternoon-and-evening, and night. The levels of lipids were among the biggest differences in the profiles. That makes sense, Jones says, because, compared with carbohydrates and amino acids, lipids are slower to accumulate in and be cleared from the bloodstream. The team’s ultimate goal is to find biological response indicators that are linked to environmental and dietary exposures. Ideally, these indicators would remain long after a chemical is cleared from the body or after a dietary deficiency has been restored. Could an NMR spectrum reveal deficiencies in the intake of a single dietary component? Because of the group’s interest in cysteine, they focused on sulfur amino
acids (SAAs). These experiments had a modified crossover design that eliminated variability among individuals due to age, gender, race, genetics, smoking habits, and so on; each person was essentially his or her own control. The subjects spent 5 days on either a normal diet or a chemically defined diet without SAAs and then 5 days on the same diet with SAAs. As before, plasma samples were collected and NMR spectra were analyzed. The spectra fell into four distinct classes: normal diet without SAAs, normal diet with SAAs, chemically defined diet without SAAs, and chemically defined diet with SAAs. Thus, the influence of SAAs could be detected whether the subjects were on the chemically modified or the normal diet, though the chemically modified diet simplified the metabolic profiles. Encouraged by those results, the researchers removed taurine, one of the direct products of cysteine metabolism, from the spectra. Taurine levels are maintained remarkably well by the body, so one way to measure cysteine intake is to look for taurine. But the team wanted to see whether the metabolic signature would indicate the presence or absence of cysteine even without taurine. “Can things that aren’t direct products give us that information?” Jones asks. Indeed, when the researchers removed taurine from the spectrum, the response to SAAs was still there. “The power of chemistry here is the ability to reliably measure so many different chemicals to get a metabolic signature,” he says. Jones knows that the search is just beginning for biological response indicators for various nutrients, chemicals, and enzymes. But he thinks that his team has established a proof of principle that this type of experiment can work. And he adds, “The classification tells us . . . what we have to be concerned about when we start collecting samples for human metabolomics studies.”
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