The human gastrointestinal tract is home to hundreds of species of bacteria and the balance between beneficial and pathogenic bacteria plays a critical role in human health and disease. The human infant, however, is born with a sterile gut and the complex gastrointestinal host/bacterial ecosystem is only established after birth by rapid bacterial colonization. Composition of newborn gut flora depends on several factors including type of birth (Ceasarian or natural), manner of early feeding (breast milk or formula), and exposure to local, physical environment. Imbalance in normal, healthy gut flora contributes to several adult human diseases including inflammatory bowel (ulcerative colitis and Crohn's disease) and Clostridium difficile associated disease, and early childhood diseases such as necrotizing enterocolitis. As a first step towards characterization of the role of gut bacteria in human health and disease, we conducted an 850 MHz (1)H nuclear magnetic resonance spectroscopy study to monitor changes in metabolic profiles of urine and fecal extracts of 15 mice following gut sterilization by the broad-spectrum antibiotic enrofloxacin (also known as Baytril). Ten metabolites changed in urine following enrofloxacin treatment including decreased acetate due to loss of microbial catabolism of sugars and polysaccharides, decreased trimethylamine-N-oxide due to loss of microbial catabolism of choline, and increased creatine and creatinine due to loss of microbial enzyme degradation. Eight metabolites changed in fecal extracts of mice treated with enrofloxacin including depletion of amino acids produced by microbial proteases, reduction in metabolites generated by lactate-utilizing bacteria, and increased urea caused by loss of microbial ureases.

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Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures makes it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported by a standard and rigorous assessment of whether or not the cluster separation is statistically significant. Here quantification and statistical significance of separation of group centroids in PCA and PLS-DA scores plots are considered. The Mahalanobis distance is used to quantify the distance between group centroids, and the two-sample Hotelling's T2 test is computed for the data, related to an F-statistic, and then an F-test is applied to determine if the cluster separation is statistically significant. We demonstrate the value of this approach using four datasets containing various degrees of separation, ranging from groups that had no apparent visual cluster separation to groups that had no visual cluster overlap. Widespread adoption of such concrete metrics to quantify and evaluate the statistical significance of PCA and PLS-DA cluster separation would help standardize reporting of metabonomics data.

A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15–30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5–10%). The inverse relationship between CV and SNR roughly obeyed a log10 dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log10(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.

Nuclear magnetic resonance (NMR) and liquid chromatography/mass spectrometry (LC/MS) based metabonomics screening of urine has great potential for discovery of biomarkers for diseases that afflict newborn and preterm infants. However, urine collection from newborn infants presents a potential confounding problem due to the possibility that contaminants might leach from materials used for urine collection and influence statistical analysis of metabonomics data. In this manuscript, we have analyzed diaper and cotton ball contamination using synthetic urine to assess its potential to influence the outcome of NMR- and LC/MS-based metabonomics studies of human infant urine. Eight diaper brands were examined using the "diaper plus cotton ball" technique. Data were analyzed using conventional principal components analysis, as well as a statistical significance algorithm developed for, and applied to, NMR data. Results showed most diaper brands had distinct contaminant profiles that could potentially influence NMR- and LC/MS-based metabonomics studies. On the basis of this study, it is recommended that diaper and cotton ball brands be characterized using metabonomics methodologies prior to initiating a metabonomics study to ensure that contaminant profiles are minimal or manageable and that the same diaper and cotton ball brands be used throughout a study to minimize variation.