Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

We propose a novel application of secondary electrospray ionization-mass spectrometry (SESI-MS) as a real-time clinical diagnostic tool for bacterial infection. It is known that volatile organic compounds (VOCs), produced in different combinations and quantities by bacteria as metabolites, generate characteristic odors for certain bacteria. These VOCs comprise a specific metabolic profile that can be used for species or serovar identification, but rapid and sensitive analytical methods are required for broad utility. In this study, the VOC profiles of five bacterial groups from four genera, Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Salmonella enterica serovar Typhimurium, and Salmonella enterica serovar Pullorum, were characterized by SESI-MS. Thirteen compounds were identified from these bacterial cultures, and the combination of these VOCs creates a unique pattern for each genus. In addition, principal component analysis (PCA) was applied for the purpose of species or serovar discrimination. The first three principal components exhibit a clear separation between the metabolic volatile profiles of these five bacterial groups that is independent of the growth medium. As a first step toward addressing the complexity of clinical application, in vitro tests for mixed cultures were conducted. The results show that individual species or serovars in a mixed culture are identifiable among a biological VOC background, and the ratios of the detected volatiles reflect the proportion of each bacterium in the mixture. Our data confirm the utility of SESI-MS in real-time identification of bacterial species or serovars in vitro, which, in the future, may play a promising clinical role in diagnosing infections.

The identification of bacteria by their volatilomes is of interest to many scientists and clinicians as it holds the promise of diagnosing infections in situ, particularly lung infections via breath analysis. While there are many studies reporting various bacterial volatile biomarkers or fingerprints using in vitro experiments, it has proven difficult to translate these data to in vivo breath analyses. Therefore, we aimed to create secondary electrospray ionization-mass spectrometry (SESI-MS) pathogen fingerprints directly from the breath of mice with lung infections. In this study we demonstrated that SESI-MS is capable of differentiating infected vs. uninfected mice, P. aeruginosa–infected vs. S. aureus–infected mice, as well as distinguish between infections caused by P. aeruginosa strains PAO1 vs. FRD1, with statistical significance (p < 0.05). In addition, we compared in vitro and in vivo volatiles and observed that only 25–34% of peaks are shared between the in vitro and in vivo SESI-MS fingerprints. To the best of our knowledge, these are the first breath volatiles measured for P. aeruginosa PAO1, FRD1, and S. aureus RN450, and the first comparison of in vivo and in vitro volatile profiles from the same strains using the murine infection model.

Targeted detection is one of the most important methods in mass spectrometry (MS)-based metabolomics; however, its major limitation is the reduced metabolome coverage that results from the limited set of targeted metabolites typically used in the analysis. In this study we describe a new approach, globally optimized targeted (GOT)-MS, that combines many of the advantages of targeted detection and global profiling in metabolomics analysis, including the capability to detect unknowns, broad metabolite coverage, and excellent quantitation. The key step in GOT-MS is a global search of precursor and product ions using a single liquid chromatography–triple quadrupole (LC–QQQ) mass spectrometer. Here, focused on measuring serum metabolites, we obtained 595 precursor ions and 1 890 multiple reaction monitoring (MRM) transitions, under positive and negative ionization modes in the mass range of 60–600 Da. For many of the MRMs/metabolites under investigation, the analytical performance of GOT-MS is better than or at least comparable to that obtained by global profiling using a quadrupole-time-of-flight (Q-TOF) instrument of similar vintage. Using a study of serum metabolites in colorectal cancer (CRC) as a representative example, GOT-MS significantly outperformed a large targeted MS assay containing ∼160 biologically important metabolites and provided a complementary approach to traditional global profiling using Q-TOF-MS. GOT-MS thus expands and optimizes the detection capabilities for QQQ-MS through a novel approach and should have the potential to significantly advance both basic and clinical metabolic research.

Microbial communities of human gut directly influence health and bear adaptive potential to different geography environment and lifestyles. However, knowledge about the influences of altitude and geography on the gut microbiota of Tibetans is currently limited. In this study, fecal microbiota from 208 Tibetans across six different locations were analyzed by MiSeq sequencing; these locations included Gannan, Gangcha, Tianzhu, Hongyuan, Lhasa and Nagqu, with altitudes above sea level ranging from 2800 m to 4500 m across the Tibetan plateau. Significant differences were observed in microbial diversity and richness in different locations. At the phylum level, gut populations of Tibetans comprised Bacteroidetes (60.00%), Firmicutes (29.04%), Proteobacteria (5.40%), and Actinobacteria (3.85%) and were marked by a low ratio (0.48) of Firmicutes to Bacteroidetes. Analysis based on operational taxonomic unit level revealed that core microbiotas included Prevotella, Faecalibacterium, and Blautia, whereas Prevotella predominated all locations, except Gangcha. Four community state types were detected in all samples, and they mainly belong to Prevotella, Bacteroides, and Ruminococcaceae. Principal component analysis and related correspondence analysis results revealed that bacterial profiles in Tibetan guts varied significantly with increasing altitude, BMI, and age, and facultative anaerobes were rich in Tibetan guts. Gut microbiota may play important roles in regulating high-altitude and geographical adaptations.

OBJECTIVE-Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign ovarian tumor.METHODS-Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. RESULTS-Global Author contributions HHS Public Access

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Aging is a major risk factor for age-related ocular diseases including age-related macular degeneration in the retina and retinal pigment epithelium (RPE), cataracts in the lens, glaucoma in the optic nerve, and dry eye syndrome in the cornea. We used targeted metabolomics to analyze metabolites from young (6 weeks) and old (73 weeks) eyes in C57 BL6/J mice. Old mice had diminished electroretinogram responses and decreased number of photoreceptors in their retinas. Among the 297 detected metabolites, 45-114 metabolites are significantly altered in aged eye tissues, mostly in the neuronal tissues (retina and optic nerve) and less in cornea, RPE/choroid, and lens. We noted that changes of metabolites in mitochondrial metabolism and glucose metabolism are common features in the aged retina, RPE/choroid, and optic nerve. The aging retina, cornea, and optic nerve also share similar changes in Nicotinamide adenine dinucleotide (NAD), 1-methylnicotinamides, 3-methylhistidine, and other methylated metabolites. Metabolites in taurine metabolism are strikingly influenced by aging in the cornea and lens. In conclusion, the aging eye has both common and tissue-specific metabolic signatures. These changes may be attributed to dysregulated mitochondrial metabolism, reprogrammed glucose metabolism and impaired methylation in the aging eye. Our findings provide biochemical insights into the mechanisms of age-related ocular changes.