Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

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

PPARα contributes to protection against metabolic and inflammatory derangements associated with acute kidney injury in experimental sepsis

Abstract Sepsis‐associated acute kidney injury (AKI) is a significant problem in critically ill children and adults resulting in increased morbidity and mortality. Fundamental mechanisms contributing to sepsis‐associated AKI are poorly understood. Previous research has demonstrated that peroxisome proliferator‐activated receptor α (PPARα) expression is associated with reduced organ system failure in sepsis. Using an experimental model of polymicrobial sepsis, we demonstrate that mice deficient in PPARα have worse kidney function, which is likely related to reduced fatty acid oxidation and increased inflammation. Ultrastructural evaluation with electron microscopy reveals that the proximal convoluted tubule is specifically injured in septic PPARα deficient mice. In this experimental group, serum metabolomic analysis reveals unanticipated metabolic derangements in tryptophan‐kynurenine‐NAD+ and pantothenate pathways. We also show that a subgroup of children with sepsis whose genome‐wide expression profiles are characterized by repression of the PPARα signaling pathway has increased incidence of severe AKI. These findings point toward interesting associations between sepsis‐associated AKI and PPARα‐driven fatty acid metabolism that merit further investigation

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring

Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila.

In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as hub metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait

HPLC-MS and HPLC-NMR method development and applications for small molecule bioanalysis

The rapidly evolving field of metabolomics or metabolic profiling focuses on the quantitative and qualitative analysis of a large or limited number of small molecules in a given biological sample. Hyphenated analytical techniques combining separation technology such as HPLC with MS or NMR are highly effective and enabling tools for metabolic profiling of small biological molecules. In this thesis the development and applications of coupled HPLC-TQMS and HPLC-microcoil/NMR techniques for investigation of drug and endogenous metabolites are presented. First, an automated multiplex-HPLC-TQMS system capable of performing analytical separation on multiple columns following MS acquisition in multiple-reaction monitoring (MRM) mode is described. This system was utilized to perform targeted serum metabolic profiling of nucleosides in esophageal adenocarcinoma patients. A number of putative nucleoside biomarkers was discovered. Second, a novel automated HPLC-microcoil/NMR system was illustrated that performed analytical separation, pre-concentration, and NMR spectroscopy in rapid succession. The central component of the system was the online column-trapping pre-concentration technique that resulted in up to 14-fold enhancement in the NMR signal. This system was used for the separation and identification of anti-inflammatory drugs in a standard mixture, and for the identification of ibuprofen metabolites in human urine. Lastly, a chromatogram-resolved NMR spectroscopy technique for identification of low-abundance metabolites is described. This method, based on sample fractioning on an analytical column, allowed structural elucidation of low-concentration metabolites that were unidentifiable from NMR spectra of the whole urine

Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD

Rapid exposure of macrophages to drugs resolves four classes of effects on the leading edge sensory pseudopod: Non-perturbing, adaptive, disruptive, and activating.

Leukocyte migration is controlled by a membrane-based chemosensory pathway on the leading edge pseudopod that guides cell movement up attractant gradients during the innate immune and inflammatory responses. This study employed single cell and population imaging to investigate drug-induced perturbations of leading edge pseudopod morphology in cultured, polarized RAW macrophages. The drugs tested included representative therapeutics (acetylsalicylic acid, diclofenac, ibuprofen, acetaminophen) as well as control drugs (PDGF, Gö6976, wortmannin). Notably, slow addition of any of the four therapeutics to cultured macrophages, mimicking the slowly increasing plasma concentration reported for standard oral dosage in patients, yielded no detectable change in pseudopod morphology. This finding is consistent with the well established clinical safety of these drugs. However, rapid drug addition to cultured macrophages revealed four distinct classes of effects on the leading edge pseudopod: (i) non-perturbing drug exposures yielded no detectable change in pseudopod morphology (acetylsalicylic acid, diclofenac); (ii) adaptive exposures yielded temporary collapse of the extended pseudopod and its signature PI(3,4,5)P3 lipid signal followed by slow recovery of extended pseudopod morphology (ibuprofen, acetaminophen); (iii) disruptive exposures yielded long-term pseudopod collapse (Gö6976, wortmannin); and (iv) activating exposures yielded pseudopod expansion (PDGF). The novel observation of adaptive exposures leads us to hypothesize that rapid addition of an adaptive drug overwhelms an intrinsic or extrinsic adaptation system yielding temporary collapse followed by adaptive recovery, while slow addition enables gradual adaptation to counteract the drug perturbation in real time. Overall, the results illustrate an approach that may help identify therapeutic drugs that temporarily inhibit the leading edge pseudopod during extreme inflammation events, and toxic drugs that yield long term inhibition of the pseudopod with negative consequences for innate immunity. Future studies are needed to elucidate the mechanisms of drug-induced pseudopod collapse, as well as the mechanisms of adaptation and recovery following some inhibitory drug exposures