Comparison of N-Glycopeptide to Released N-Glycan Abundances and the Influence of Glycopeptide Mass and Charge States on N-Linked Glycosylation of IgG Antibodies.

We report the comparison of mass-spectral-based abundances of tryptic glycopeptides to fluorescence abundances of released labeled glycans and the effects of mass and charge state and in-source fragmentation on glycopeptide abundances. The primary glycoforms derived from Rituximab, NISTmAb, Evolocumab, and Infliximab were high-mannose and biantennary complex galactosylated and fucosylated N-glycans. Except for Evolocumab, in-source ions derived from the loss of HexNAc or HexNAc-Hex sugars are prominent for other therapeutic IgGs. After excluding in-source fragmentation of glycopeptide ions from the results, a linear correlation was observed between fluorescently labeled N-glycan and glycopeptide abundances over a dynamic range of 500. Different charge states of human IgG-derived glycopeptides containing a wider variety of abundant attached glycans were also investigated to examine the effects of the charge state on ion abundances. These revealed a linear dependence of glycopeptide abundance on the mass of the glycan with higher charge states favoring higher-mass glycans. Findings indicate that the mass spectrometry-based bottom-up approach can provide results as accurate as those of glycan release studies while revealing the origin of each attached glycan. These site-specific relative abundances are conveniently displayed and compared using previously described glycopeptide abundance distribution spectra "GADS" representations. Mass spectrometry data are available from the MAssIVE repository (MSV000093562).

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence.

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Gut microbe-targeted choline trimethylamine lyase inhibition improves obesity via rewiring of host circadian rhythms.

Obesity has repeatedly been linked to reorganization of the gut microbiome, yet to this point obesity therapeutics have been targeted exclusively toward the human host. Here, we show that gut microbe-targeted inhibition of the trimethylamine N-oxide (TMAO) pathway protects mice against the metabolic disturbances associated with diet-induced obesity (DIO) or leptin deficiency (Lepob/ob). Small molecule inhibition of the gut microbial enzyme choline TMA-lyase (CutC) does not reduce food intake but is instead associated with alterations in the gut microbiome, improvement in glucose tolerance, and enhanced energy expenditure. We also show that gut microbial CutC inhibition is associated with reorganization of host circadian control of both phosphatidylcholine and energy metabolism. This study underscores the relationship between microbe and host metabolism and provides evidence that gut microbe-derived trimethylamine (TMA) is a key regulator of the host circadian clock. This work also demonstrates that gut microbe-targeted enzyme inhibitors have potential as anti-obesity therapeutics.

A Single Human-Relevant Fast Food Meal Rapidly Reorganizes Metabolomic and Transcriptomic Signatures in a Gut Microbiota-Dependent Manner.

BACKGROUND: A major contributor to cardiometabolic disease is caloric excess, often a result of consuming low cost, high calorie fast food. Studies have demonstrated the pivotal role of gut microbes contributing to cardiovascular disease in a diet-dependent manner. Given the central contributions of diet and gut microbiota to cardiometabolic disease, we hypothesized that microbial metabolites originating after fast food consumption can elicit acute metabolic responses in the liver. METHODS: We gave conventionally raised mice or mice that had their microbiomes depleted with antibiotics a single oral gavage of a liquified fast food meal or liquified control rodent chow meal. After four hours, mice were sacrificed and we used untargeted metabolomics of portal and peripheral blood, 16S rRNA gene sequencing, targeted liver metabolomics, and host liver RNA sequencing to identify novel fast food-derived microbial metabolites and their acute effects on liver function. RESULTS: Several candidate microbial metabolites were enriched in portal blood upon fast food feeding, and were essentially absent in antibiotic-treated mice. Strikingly, at four hours post-gavage, fast food consumption resulted in rapid reorganization of the gut microbial community and drastically altered hepatic gene expression. Importantly, diet-driven reshaping of the microbiome and liver transcriptome was dependent on an intact microbial community and not observed in antibiotic ablated animals. CONCLUSIONS: Collectively, these data suggest a single fast food meal is sufficient to reshape the gut microbial community in mice, yielding a unique signature of food-derived microbial metabolites. Future studies are in progress to determine the contribution of select metabolites to cardiometabolic disease progression and the translational relevance of these animal studies.

Universal Spectrum Identifier for mass spectra.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

Increasing the Coverage of a Mass Spectral Library of Milk Oligosaccharides Using a Hybrid-Search-Based Bootstrapping Method and Milks from a Wide Variety of Mammals.

This study significantly expands both the scope and method of identification for construction of a previously reported tandem mass spectral library of 74 human milk oligosaccharides (HMOs) derived from results of combined LC-MS/MS experiments and comprehensive structural analysis of HMOs. In the present work, a hybrid search "bootstrap" identification method was employed that substantially broadens the coverage of milk oligosaccharides and thereby increases utility use of a spectrum library-based method for the rapid tentative identification of all distinguishable glycans in milk. This involved hybrid searching of the previous library, which was itself constructed using the hybrid search of oligosaccharide spectra in the NIST 17 Tandem MS Library. The general approach appears applicable to library construction of other classes of compounds. The coverage of oligosaccharides was significantly extended using milks from a variety of mammals, including bovine, Asian buffalo, African lion, and goat. This new method led to the identification of another 145 oligosaccharides, including an additional 80 HMOs from reanalysis of human milk. The newly identified compounds were added to a freely available mass spectral reference database of 219 milk oligosaccharides. We also provide suggestions to overcome several limitations and pitfalls in the interpretation of spectra of unknown oligosaccharides.

The ABRF Metabolomics Research Group 2016 Exploratory Study: Investigation of Data Analysis Methods for Untargeted Metabolomics.

Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

Disparate Metabolomics Data Reassembler: A Novel Algorithm for Agglomerating Incongruent LC-MS Metabolomics Datasets.

In the past decade, the field of LC-MS-based metabolomics has transformed from an obscure specialty into a major "-omics" platform for studying metabolic processes and biomolecular characterization. However, as a whole the field is still very fractured, as the nature of the instrumentation and the information produced by the platform essentially creates incompatible "islands" of datasets. This lack of data coherency results in the inability to accumulate a critical mass of metabolomics data that has enabled other -omics platforms to make impactful discoveries and meaningful advances. As such, we have developed a novel algorithm, called Disparate Metabolomics Data Reassembler (DIMEDR), which attempts to bridge the inconsistencies between incongruent LC-MS metabolomics datasets of the same biological sample type. A single "primary" dataset is postprocessed via traditional means of peak identification, alignment, and grouping. DIMEDR utilizes this primary dataset as a progenitor template by which data from subsequent disparate datasets are reassembled and integrated into a unified framework that maximizes spectral feature similarity across all samples. This is accomplished by a novel procedure for universal retention time correction and comparison via identification of ubiquitous features in the initial primary dataset, which are subsequently utilized as endogenous internal standards during integration. For demonstration purposes, two human and two mouse urine metabolomics datasets from four unrelated studies acquired over 4 years were unified via DIMEDR, which enabled meaningful analysis across otherwise incomparable and unrelated datasets.

Global metabolomic responses in urine from atm deficient mice in response to LD50/30 gamma irradiation doses.

Exposures to ionizing radiation (IR) may either be accidental or intentional, for medical purposes or even through terrorist actions. As certain populations emerge to be more radiosensitive than others, it is imperative to assess those individuals and treat them accordingly. To demonstrate the feasibility of rapid identification of such cases, we utilized the highly radiosensitive mouse model Atm-/- in the C57BL/6 background, and evaluated the urinary responses in 8-10 week old male mice at early time points (4, 24, and 72 h) after exposure to their respective LD50/30 doses [4 Gy for Atm-/- , and 8 Gy for wild type (WT)]. Urinary profiles from heterozygous animals exhibited remarkably similar responses to WT before and after radiation exposure. However, genotypic differences (WT or Atm-/- ) were the primary driver to responses to radiation. Putative metabolites were validated through tandem mass spectrometry and included riboflavin, uric acid, d-ribose, d-glucose, pantothenic acid, taurine, kynurenic acid, xanthurenic acid, 2-oxoadipic acid, glutaric acid, 5'-deoxy-5'-methylthioadenosine, and hippuric acid. These metabolites mapped to several interconnected metabolic pathways which suggest that radiosensitive mouse models have underlying differences significantly impacting overall metabolism. This was further amplified by ionizing radiation at different time points. This study further emphasizes that genetically based radiosensitivity is reflected in the metabolic processes, and can be directly observed in urine. These differences in turn can potentially be used to identify individuals that may require altered medical treatment in an emergency radiological situation or modification of a regimen during a radiotherapy session. Environ. Mol. Mutagen. 59:576-585, 2018. © 2018 Wiley Periodicals, Inc.

Creating a Mass Spectral Reference Library for Oligosaccharides in Human Milk.

We report the development and availability of a mass spectral reference library for oligosaccharides in human milk. This represents a new variety of spectral library that includes consensus spectra of compounds annotated through various data analysis methods, a concept that can be extended to other varieties of biological fluids. Oligosaccharides from the NIST Standard Reference Material (SRM) 1953, composed of human milk pooled from 100 breastfeeding mothers, were identified and characterized using hydrophilic interaction liquid chromatography electrospray ionization tandem mass spectrometry (HILIC-ESI-MS/MS) and the NIST 17 Tandem MS Library. Consensus reference spectra were generated, incorporated into a searchable library, and matched using the newly developed hybrid search algorithm to elucidate unknown oligosaccharides. The NIST hybrid search program facilitates the structural assignment of complex oligosaccharides especially when reference standards are not commercially available. High accuracy mass measurement for precursor and product ions, as well as the relatively high MS/MS signal intensities of various oligosaccharide precursors with Fourier transform ion trap (FT-IT) and higher energy dissociation (HCD) fragmentation techniques, enabled the assignment of multiple free and underivatized fucosyllacto- and sialyllacto-oligosaccharide spectra. Neutral and sialylated isomeric oligosaccharides have distinct retention times, allowing the identification of 74 oligosaccharides in the reference material. This collection of newly characterized spectra based on a searchable, reference MS library of annotated oligosaccharides can be applied to analyze similar compounds in other types of milk or any biological fluid containing milk oligosaccharides.

A Serum Small Molecule Biosignature of Radiation Exposure from Total Body Irradiated Patients.

The potential for radiological accidents and nuclear terrorism has increased the need for the development of new rapid biodosimetry methods. In addition, in a clinical setting the issue of an individual's radiosensitivity should be taken into consideration during radiotherapy. We utilized metabolomics and lipidomics to investigate changes of metabolites in serum samples following exposure to total body ionizing radiation in humans. Serum was collected prior to irradiation, at 3-8 h after a single dose of 1.25-2 Gy, and at 24 h with a total delivered dose of 2-3.75 Gy. Metabolomics revealed perturbations in glycerophosphocholine, phenylalanine, ubiquinone Q2, and oxalic acid. Alterations were observed in circulating levels of lipids from monoacylglycerol, triacylglycerol, phosphatidylcholine, and phosphatidylglycerol lipid classes. Polyunsaturated fatty acids were some of the most dysregulated lipids, with increased levels linked to proinflammatory processes. A targeted metabolomics approach for eicosanoids was also employed. The results showed a rapid response for proinflammatory eicosanoids, with a dampening of the signal at the later time point. Sex differences were observed in the markers from the untargeted approach but not the targeted method. The ability to identify and quantify small molecules in blood can therefore be utilized to monitor radiation exposure in human populations.

Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease.

Background. Cystic fibrosis (CF) is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE) that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples) to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable). Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF) and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events) as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset.

An Integrated Multi-Omic Approach to Assess Radiation Injury on the Host-Microbiome Axis.

Medical responders to radiological and nuclear disasters currently lack sufficient high-throughput and minimally invasive biodosimetry tools to assess exposure and injury in the affected populations. For this reason, we have focused on developing robust radiation exposure biomarkers in easily accessible biofluids such as urine, serum and feces. While we have previously reported on urine and serum biomarkers, here we assessed perturbations in the fecal metabolome resulting from exposure to external X radiation in vivo. The gastrointestinal (GI) system is of particular importance in radiation biodosimetry due to its constant cell renewal and sensitivity to radiation-induced injury. While the clinical GI symptoms such as pain, bloating, nausea, vomiting and diarrhea are manifested after radiation exposure, no reliable bioindicator has been identified for radiation-induced gastrointestinal injuries. To this end, we focused on determining a fecal metabolomic signature in X-ray irradiated mice. There is overwhelming evidence that the gut microbiota play an essential role in gut homeostasis and overall health. Because the fecal metabolome is tightly correlated with the composition and diversity of the microorganism in the gut, we also performed fecal 16S rRNA sequencing analysis to determine the changes in the microbial composition postirradiation. We used in-house bioinformatics tools to integrate the 16S rRNA sequencing and metabolomic data, and to elucidate the gut integrated ecosystem and its deviations from a stable host-microbiome state that result from irradiation. The 16S rRNA sequencing results indicated that radiation caused remarkable alterations of the microbiome in feces at the family level. Increased abundance of common members of Lactobacillaceae and Staphylococcaceae families, and decreased abundances of Lachnospiraceae, Ruminococcaceae and Clostridiaceae families were found after 5 and 12 Gy irradiation. The metabolomic data revealed statistically significant changes in the microbial-derived products such as pipecolic acid, glutaconic acid, urobilinogen and homogentisic acid. In addition, significant changes were detected in bile acids such as taurocholic acid and 12-ketodeoxycholic acid. These changes may be associated with the observed shifts in the abundance of intestinal microbes, such as R. gnavus , which can transform bile acids.

Implications of genotypic differences in the generation of a urinary metabolomics radiation signature.

The increased threat of radiological terrorism and accidental nuclear exposures, together with increased usage of radiation-based medical procedures, has made necessary the development of minimally invasive methods for rapid identification of exposed individuals. Genetically predisposed radiosensitive individuals comprise a significant number of the population and require specialized attention and treatments after such events. Metabolomics, the assessment of the collective small molecule content in a given biofluid or tissue, has proven effective in the rapid identification of radiation biomarkers and metabolic perturbations. To investigate how the genotypic background may alter the ionizing radiation (IR) signature, we analyzed urine from Parp1(-/-) mice, as a model radiosensitive genotype, exposed to IR by utilizing the analytical power of liquid chromatography coupled with mass spectrometry (LC-MS), as urine has been thoroughly investigated in wild type (WT) mice in previous studies from our laboratory. Samples were collected at days one and three after irradiation, time points that are important for the early and efficient triage of exposed individuals. Time-dependent perturbations in metabolites were observed in the tricarboxylic acid pathway (TCA). Other differentially excreted metabolites included amino acids and metabolites associated with dysregulation of energy metabolism pathways. Time-dependent apoptotic pathway activation between WT and mutant mice following IR exposure may explain the altered excretion patterns, although the origin of the metabolites remains to be determined. This first metabolomics study in urine from radiation exposed genetic mutant animal models provides evidence that this technology can be used to dissect the effects of genotoxic agents on metabolism by assessing easily accessible biofluids and identify biomarkers of radiation exposure. Applications of metabolomics could be incorporated in the future to further elucidate the effects of IR on the metabolism of Parp1(-/-) genotype by assessing individual tissues.

A Lipidomic and Metabolomic Serum Signature from Nonhuman Primates Exposed to Ionizing Radiation.

INTRODUCTION: Due to dangers associated with potential accidents from nuclear energy and terrorist threats, there is a need for high-throughput biodosimetry to rapidly assess individual doses of radiation exposure. Lipidomics and metabolomics are becoming common tools for determining global signatures after disease or other physical insult and provide a "snapshot" of potential cellular damage. OBJECTIVES: The current study assesses changes in the nonhuman primate (NHP) serum lipidome and metabolome 7 days following exposure to ionizing radiation (IR). METHODS: Serum sample lipids and metabolites were extracted using a biphasic liquid-liquid extraction and analyzed by ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Global radiation signatures were acquired in data-independent mode. RESULTS: Radiation exposure caused significant perturbations in lipid metabolism, affecting all major lipid species, including free fatty acids, glycerolipids, glycerophospholipids and esterified sterols. In particular, we observed a significant increase in the levels of polyunsaturated fatty acids (PUFA)-containing lipids in the serum of NHPs exposed to 10 Gy radiation, suggesting a primary role played by PUFAs in the physiological response to IR. Metabolomics profiling indicated an increase in the levels of amino acids, carnitine, and purine metabolites in the serum of NHPs exposed to 10 Gy radiation, suggesting perturbations to protein digestion/absorption, biological oxidations, and fatty acid ß-oxidation. CONCLUSIONS: This is the first report to determine changes in the global NHP serum lipidome and metabolome following radiation exposure and provides information for developing metabolomic biomarker panels in human-based biodosimetry.

Exposure to ionizing radiation reveals global dose- and time-dependent changes in the urinary metabolome of rat.

The potential for exposures to ionizing radiation has increased in recent years. Although advances have been made, understanding the global metabolic response as a function of both dose and exposure time is challenging considering the complexity of the responses. Herein we report our findings on the dose- and time-dependency of the urinary response to ionizing radiation in the male rat using radiation metabolomics. Urine samples were collected from adult male rats, exposed to 0.5 to 10 Gy γ-radiation, both before from 6 to 72 h following exposures. Samples were analyzed by liquid chromatography coupled with time-of-flight mass spectrometry, and deconvoluted mass chromatographic data were initially analyzed by principal component analysis. However, the breadth and complexity of the data necessitated the development of a novel approach to summarizing biofluid constituents after exposure, called Visual Analysis of Metabolomics Package (VAMP). VAMP revealed clear urine metabolite profile differences to as little as 0.5 Gy after 6 h exposure. Via VAMP, it was discovered that the response to radiation exposure found in rat urine is characterized by an overall net down-regulation of ion excretion with only a modest number of ions excreted in excess over pre-exposure levels. Our results show both similarities and differences with the published mouse urine response and a dose- and time-dependent net decrease in urine ion excretion associated with radiation exposure. These findings mark an important step in the development of minimally invasive radiation biodosimetry. VAMP should have general applicability in metabolomics to visualize overall differences and trends in many sample sets.

Serum Dyslipidemia Is Induced by Internal Exposure to Strontium-90 in Mice, Lipidomic Profiling Using a Data-Independent Liquid Chromatography-Mass Spectrometry Approach.

Despite considerable research into the environmental risks and biological effects of exposure to external beam γ rays, incorporation of radionuclides has largely been understudied. This dosimetry and exposure risk assessment is challenging for first responders in the field during a nuclear or radiological event. Therefore, we have developed a workflow for assessing injury responses in easily obtainable biofluids, such as urine and serum, as the result of exposure to internal emitters cesium-137 ((137)Cs) and strontium-90 ((90)Sr) in mice. Here we report on the results of the untargeted lipidomic profiling of serum from mice exposed to (90)Sr. We also compared these results to those from previously published (137)Cs exposure to determine any differences in cellular responses based on exposure type. The results of this study conclude that there is a gross increase in the serum abundance of triacylglycerides and cholesterol esters, while phostaphatidylcholines and lysophosphatidylcholines displayed decreases in their serum levels postexposure at study days 4, 7, 9, 25, and 30, with corresponding average cumulative skeleton doses ranging from 1.2 ± 0.1 to 5.2 ± 0.73 Gy. The results show significant perturbations in serum lipidome as early as 2 days postexposure persisting until the end of the study (day 30).

A Comprehensive Metabolomic Investigation in Urine of Mice Exposed to Strontium-90.

Internal emitters such as Strontium-90 ((90)Sr) pose a substantial health risk during and immediately after a nuclear disaster or detonation of an improvised device. The environmental persistency and potency of (90)Sr calls for urgent development of high-throughput tests to establish levels of exposure and to help triage potentially exposed individuals who were in the immediate area of the disaster. In response to these concerns, our team focused on developing a robust metabolomic profile for (90)Sr exposure in urine using a mouse model. The sensitivity of modern time-of-flight mass spectrometry (TOFMS) combined with the separation power of ultra performance liquid chromatography (UPLC) was used to determine perturbations in the urinary metabolome of mice exposed to (90)Sr. The recently developed statistical suite, MetaboLyzer, was used to explore the mass spectrometry data. The results indicated a significant change in the urinary abundances of metabolites pertaining to butanoate metabolism, vitamin B metabolism, glutamate and fatty acid oxidation. All of these pathways are either directly or indirectly connected to the central energy production pathway, the tricarboxylic acid (TCA) cycle. To our knowledge, this is the first in vivo metabolomics to evaluate the effects of exposure to (90)Sr using the easily accessible biofluid, urine.

Selective paired ion contrast analysis: a novel algorithm for analyzing postprocessed LC-MS metabolomics data possessing high experimental noise.

One of the consequences in analyzing biological data from noisy sources, such as human subjects, is the sheer variability of experimentally irrelevant factors that cannot be controlled for. This holds true especially in metabolomics, the global study of small molecules in a particular system. While metabolomics can offer deep quantitative insight into the metabolome via easy-to-acquire biofluid samples such as urine and blood, the aforementioned confounding factors can easily overwhelm attempts to extract relevant information. This can mar potentially crucial applications such as biomarker discovery. As such, a new algorithm, called Selective Paired Ion Contrast (SPICA), has been developed with the intent of extracting potentially biologically relevant information from the noisiest of metabolomic data sets. The basic idea of SPICA is built upon redefining the fundamental unit of statistical analysis. Whereas the vast majority of algorithms analyze metabolomics data on a single-ion basis, SPICA relies on analyzing ion-pairs. A standard metabolomic data set is reinterpreted by exhaustively considering all possible ion-pair combinations. Statistical comparisons between sample groups are made only by analyzing the differences in these pairs, which may be crucial in situations where no single metabolite can be used for normalization. With SPICA, human urine data sets from patients undergoing total body irradiation (TBI) and from a colorectal cancer (CRC) relapse study were analyzed in a statistically rigorous manner not possible with conventional methods. In the TBI study, 3530 statistically significant ion-pairs were identified, from which numerous putative radiation specific metabolite-pair biomarkers that mapped to potentially perturbed metabolic pathways were elucidated. In the CRC study, SPICA identified 6461 statistically significant ion-pairs, several of which putatively mapped to folic acid biosynthesis, a key pathway in colorectal cancer. Utilizing support vector machines (SVMs), SPICA was also able to unequivocally outperform binary classifiers built from classical single-ion feature based SVMs.