Publications

Obesity and the accompanying metabolic syndrome are strongly associated with heightened morbidity and mortality in older adults. In our review of more than 20 epidemiologic studies of major infectious diseases, including leaders such as tuberculosis, community-acquired pneumonia, and sepsis, obesity was associated with better outcomes. A cause-and-effect relationship between over-nutrition and survival with infection is suggested by results of two preliminary studies of infections in mice, where high fat feeding for 8-10 weeks provided much better outcomes. The better outcomes of infections with obesity are reminiscent of many recent studies of "sterile" non-infectious medical and surgical conditions where outcomes for obese patients are better than for their thinner counterparts –- and given the tag "obesity paradox". Turning to the history of medicine and biological evolution, we hypothesize that the metabolic syndrome has very ancient origins and is part of a lifelong metabolic program. While part of that program (the metabolic syndrome) promotes morbidity and mortality with aging, it helps infants and children as well as adults in their fight against infections and recovery from injuries, key roles in the hundreds of centuries before the public health advances of the 20th century. We conclude with speculation on how understanding the biological elements that protect obese patients with infections or injuries might be applied advantageously to thin patients with the same medical challenges.

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

In this paper, an optimized method based on gas chromatography/time-of-flight mass spectrometry (GC-TOFMS) platform has been developed for the analysis of gut microbial-host related co-metabolites in fecal samples. The optimization was performed with proportion of chloroform (C), methanol (M) and water (W) for the extraction of specific metabolic pathways of interest. Loading Bi-plots from the PLS regression model revealed that high concentration of chloroform emphasized the extraction of short chain fatty acids and TCA intermediates, while the higher concentration of methanol emphasized indole and phenyl derivatives. Low level of organic solution emphasized some TCA intermediates but not for indole and phenyl species. The highest sum of the peak area and the distribution of metabolites corresponded to the extraction of methanol/chloroform/water of 225:75:300 (v/v/v), which was then selected for method validation and utilized in our application. Excellent linearity was obtained with 62 reference standards representing different classes of gut microbial-host related co-metabolites, with correlation coefficients (r2) higher than 0.99. Limit of detections (LODs) and limit of qualifications (LOQs) for these standards were below 0.9 nmol and 1.6 nmol, respectively. The reproducibility and repeatability of the majority of tested metabolites in fecal samples were observed with RSDs lower than 15%. Chinese rhubarb-treated rats had elevated indole and phenyl species, and decreased levels of polyamine such as putrescine, and several amino acids. Our optimized method has revealed host-microbe relationships of potential importance for intestinal microbial metabolite receptors such as pregnane X receptor (PXR) and aryl hydrocarbon receptor (AHR) activity, and for enzymes such as ornithine decarboxylase (ODC).

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Isotopic ratio outlier analysis (IROA) is a (13)C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% (13)C, or 5%(13)C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%(13)C extracts, or light isotopologues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the (12)C monoisotopic and the (13)C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study.

Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.

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BACKGROUND: Radiation-induced liver disease (RILD) is a dose-limiting factor in curative radiation therapy (RT) for liver cancers, making early detection of radiation-associated liver injury absolutely essential for medical intervention. A metabolomic approach was used to determine metabolic signatures that could serve as biomarkers for early detection of RILD in mice.

METHODS: Anesthetized C57BL/6 mice received 0, 10 or 50 Gy Whole Liver Irradiation (WLI) and were contrasted to mice, which received 10 Gy whole body irradiation (WBI). Liver and plasma samples were collected at 24 hours after irradiation. The samples were processed using Gas Chromatography/Mass Spectrometry and Liquid Chromatography/Mass Spectrometry.

RESULTS: Twenty four hours after WLI, 407 metabolites were detected in liver samples while 347 metabolites were detected in plasma. Plasma metabolites associated with 50 Gy WLI included several amino acids, purine and pyrimidine metabolites, microbial metabolites, and most prominently bradykinin and 3-indoxyl-sulfate. Liver metabolites associated with 50 Gy WLI included pentose phosphate, purine, and pyrimidine metabolites in liver. Plasma biomarkers in common between WLI and WBI were enriched in microbial metabolites such as 3 indoxyl sulfate, indole-3-lactic acid, phenyllactic acid, pipecolic acid, hippuric acid, and markers of DNA damage such as 2-deoxyuridine. Metabolites associated with tryptophan and indoles may reflect radiation-induced gut microbiome effects. Predominant liver biomarkers in common between WBI and WLI were amino acids, sugars, TCA metabolites (fumarate), fatty acids (lineolate, n-hexadecanoic acid) and DNA damage markers (uridine).

CONCLUSIONS: We identified a set of metabolomic markers that may prove useful as plasma biomarkers of RILD and WBI. Pathway analysis also suggested that the unique metabolic changes observed after liver irradiation was an integrative response of the intestine, liver and kidney.

PURPOSE: Assessing whole-body radiation injury and absorbed dose is essential for remediation efforts following accidental or deliberate exposure in medical, industrial, military, or terrorist incidents. We hypothesize that variations in specific metabolite concentrations extracted from blood plasma would correlate with whole-body radiation injury and dose.

METHODS AND MATERIALS: Groups of C57BL/6 mice (n=12 per group) were exposed to 0, 2, 4, 8, and 10.4 Gy of whole-body gamma radiation. At 24 hours after treatment, all animals were euthanized, and both plasma and liver biopsy samples were obtained, the latter being used to identify a distinct hepatic radiation injury response within plasma. A semiquantitative, untargeted metabolite/lipid profile was developed using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry, which identified 354 biochemical compounds. A second set of C57BL/6 mice (n=6 per group) were used to assess a subset of identified plasma markers beyond 24 hours.

RESULTS: We identified a cohort of 37 biochemical compounds in plasma that yielded the optimal separation of the irradiated sample groups, with the most correlated metabolites associated with pyrimidine (positively correlated) and tryptophan (negatively correlated) metabolism. The latter were predominantly associated with indole compounds, and there was evidence that these were also correlated between liver and plasma. No evidence of saturation as a function of dose was observed, as has been noted for studies involving metabolite analysis of urine.

CONCLUSIONS: Plasma profiling of specific metabolites related to pyrimidine and tryptophan pathways can be used to differentiate whole-body radiation injury and dose response. As the tryptophan-associated indole compounds have their origin in the intestinal microbiome and subsequently the liver, these metabolites particularly represent an attractive marker for radiation injury within blood plasma.

CONTEXT: Intrauterine HIV/antiretroviral (ARV) and postnatal ARVs are known to perturb energy metabolism and could have permanent effects on future metabolic health. Such maladaptive effects could be mediated by changes in mitochondrial function and intermediary metabolism due to fetal and early-life ARV exposure in HIV/ARV-exposed uninfected (HEU) infants.

OBJECTIVE: The objective of the study was to understand the relationship(s) between mitochondrial fuel use (assessed via acylcarnitines and branched chain amino acids) and preprandial insulin in infants exposed to in utero HIV/ARV plus postnatal zidovudine or nevirapine compared with HIV/ARV-unexposed uninfected (HUU) infants.

DESIGN: This was a prospective cohort study with the following three groups: 1) intrauterine HIV/ARV/postnatal zidovudine-exposed (HEU-A), 2) intrauterine HIV/ARV/postnatal nevirapine-exposed (HEU-N), and 3) HUU infants. Principal component analysis and linear regression modeling were performed to assess the association between in utero HIV/ARV exposure and infant insulin.

SETTING: The study was conducted at Cameroonian urban antenatal centers.

PARTICIPANTS: HIV-infected and -uninfected pregnant woman/infant dyads participated in the study.

MAIN OUTCOME: Preprandial insulin was the main outcome measured.

RESULTS: Of 366 infants, 38 were HEU-A, 118 HEU-N. Forty intermediary metabolites were consolidated into seven principal components. In a multivariate analysis, both HEU-A (β = -.116, P= .012) and HEU-N (β = -.070, P= .022) demonstrated lower insulin compared with HUU infants. However, at high levels of plasma metabolites, HEU-A (β = .027, P= .050) exhibited higher insulin levels than HEU-N or HUU infants. A unique array of short-chain acylcarnitines (β = .044, P= .001) and branched-chain amino acids (β = .033, P= .012) was associated with insulin.

CONCLUSION: HEU-A and HEU-N infants have lower preprandial insulin levels at 6 weeks of age and appear to use metabolic fuel substrates differently than HUU infants. Future studies are warranted to determine whether observed differences have lasting metabolic implications, such as later insulin resistance.