Tytus D. Mak

MetaboLyzer: a novel statistical workflow for analyzing Postprocessed LC-MS metabolomics data.

Metabolomics, the global study of small molecules in a particular system, has in the past few years risen to become a primary -omics platform for the study of metabolic processes. With the ever-increasing pool of quantitative data yielded from metabolomic research, specialized methods and tools with which to analyze and extract meaningful conclusions from these data are becoming more and more crucial. Furthermore, the depth of knowledge and expertise required to undertake a metabolomics oriented study is a daunting obstacle to investigators new to the field. As such, we have created a new statistical analysis workflow, MetaboLyzer, which aims to both simplify analysis for investigators new to metabolomics, as well as provide experienced investigators the flexibility to conduct sophisticated analysis. MetaboLyzers workflow is specifically tailored to the unique characteristics and idiosyncrasies of postprocessed liquid chromatography-mass spectrometry (LC-MS)-based metabolomic data sets. It utilizes a wide gamut of statistical tests, procedures, and methodologies that belong to classical biostatistics, as well as several novel statistical techniques that we have developed specifically for metabolomics data. Furthermore, MetaboLyzer conducts rapid putative ion identification and putative biologically relevant analysis via incorporation of four major small molecule databases: KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these aspects into a comprehensive workflow that outputs easy to understand statistically significant and potentially biologically relevant information in the form of heatmaps, volcano plots, 3D visualization plots, correlation maps, and metabolic pathway hit histograms. For demonstration purposes, a urine metabolomics data set from a previously reported radiobiology study in which samples were collected from mice exposed to γ radiation was analyzed. MetaboLyzer was able to identify 243 statistically significant ions out of a total of 1942. Numerous putative metabolites and pathways were found to be biologically significant from the putative ion identification workflow.

Development of a Metabolomic Radiation Signature in Urine from Patients Undergoing Total Body Irradiation

The emergence of the threat of radiological terrorism and other radiological incidents has led to the need for development of fast, accurate and noninvasive methods for detection of radiation exposure. The purpose of this study was to extend radiation metabolomic biomarker discovery to humans, as previous studies have focused on mice. Urine was collected from patients undergoing total body irradiation at Memorial Sloan-Kettering Cancer Center prior to hematopoietic stem cell transplantation at 4–6 h postirradiation (a single dose of 1.25 Gy) and 24 h (three fractions of 1.25 Gy each). Global metabolomic profiling was obtained through analysis with ultra performance liquid chromatography coupled to time-of-flight mass spectrometry (TOFMS). Prior to further analyses, each sample was normalized to its respective creatinine level. Statistical analysis was conducted by the nonparametric Kolmogorov-Smirnov test and the Fishers exact test and markers were validated against pure standards. Seven markers showed distinct differences between pre- and post-exposure samples. Of those, trimethyl-l-lysine and the carnitine conjugates acetylcarnitine, decanoylcarnitine and octanoylcarnitine play an important role in the transportation of fatty acids across mitochondria for subsequent fatty acid β-oxidation. The remaining metabolites, hypoxanthine, xanthine and uric acid are the final products of the purine catabolism pathway, and high levels of excretion have been associated with increased oxidative stress and radiation induced DNA damage. Further analysis revealed sex differences in the patterns of excretion of the markers, demonstrating that generation of a sex-specific metabolomic signature will be informative and can provide a quick and reliable assessment of individuals in a radiological scenario. This is the first radiation metabolomics study in human urine laying the foundation for the use of metabolomics in biodosimetry and providing confidence in biomarker identification based on the overlap between animal models and humans.

Metabolomic and lipidomic analysis of serum from mice exposed to an internal emitter, cesium-137, using a shotgun LC-MS(E) approach.

In this study ultra performance liquid chromatography (UPLC) coupled to time-of-flight mass spectrometry in the MSE mode was used for rapid and comprehensive analysis of metabolites in the serum of mice exposed to internal exposure by Cesium-137 (137Cs). The effects of exposure to 137Cs were studied at several time points after injection of 137CsCl in mice. Over 1800 spectral features were detected in the serum of mice in positive and negative electrospray ionization modes combined. Detailed statistical analysis revealed that several metabolites associated with amino acid metabolism, fatty acid metabolism, and the TCA cycle were significantly perturbed in the serum of 137Cs-exposed mice compared with that of control mice. While metabolites associated with the TCA cycle and glycolysis increased in their serum abundances, fatty acids such as linoleic acid and palmitic acid were detected at lower levels in serum after 137Cs exposure. Furthermore, phosphatidylcholines (PCs) were among the most perturbed ions in the serum of 137Cs-exposed mice. This is the first study on the effects of exposure by an internal emitter in serum using a UPLC–MSE approach. The results have put forth a panel of metabolites, which may serve as potential serum markers to 137Cs exposure.

Development of Urinary Biomarkers for Internal Exposure by Cesium-137 Using a Metabolomics Approach in Mice

Cesium-137 is a fission product of uranium and plutonium in nuclear reactors and is released in large quantities during nuclear explosions or detonation of an improvised device containing this isotope. This environmentally persistent radionuclide undergoes radioactive decay with the emission of beta particles as well as gamma radiation. Exposure to 137Cs at high doses can cause acute radiation sickness and increase risk for cancer and death. The serious health risks associated with 137Cs exposure makes it critical to understand how it affects human metabolism and whether minimally invasive and easily accessible samples such as urine and serum can be used to triage patients in case of a nuclear disaster or a radiologic event. In this study, we have focused on establishing a time-dependent metabolomic profile for urine collected from mice injected with 137CsCl. The samples were collected from control and exposed mice on days 2, 5, 20 and 30 after injection. The samples were then analyzed by ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC/TOFMS) and processed by an array of informatics and statistical tools. A total of 1,412 features were identified in ESI+ and ESI– modes from which 200 were determined to contribute significantly to the separation of metabolomic profiles of controls from those of the different treatment time points. The results of this study highlight the ease of use of the UPLC/TOFMS platform in finding urinary biomarkers for 137Cs exposure. Pathway analysis of the statistically significant metabolites suggests perturbations in several amino acid and fatty acid metabolism pathways. The results also indicate that 137Cs exposure causes: similar changes in the urinary excretion levels of taurine and citrate as seen with external-beam gamma radiation; causes no attenuation in the levels of hexanoylglycine and N-acetylspermidine; and has unique effects on the levels of isovalerylglycine and tiglylglycine.

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.

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

Internal emitters such as Strontium-90 (90Sr) pose a substantial health risk during and immediately after a nuclear disaster or detonation of an improvised device. The environmental persistency and potency of 90Sr 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 90Sr 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 90Sr. 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 90Sr using the easily accessible biofluid, urine.

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.

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).

PPARβ/δ modulates ethanol-induced hepatic effects by decreasing pyridoxal kinase activity.

Because of the significant morbidity and lethality caused by alcoholic liver disease (ALD), there remains a need to elucidate the regulatory mechanisms that can be targeted to prevent and treat ALD. Toward this goal, minimally invasive biomarker discovery represents an outstanding approach for these purposes. The mechanisms underlying ALD include hepatic lipid accumulation. As the peroxisome proliferator-activated receptor-β/δ (PPARβ/δ) has been shown to inhibit steatosis, the present study examined the role of PPARβ/δ in ALD coupling metabolomic, biochemical and molecular biological analyses. Wild-type and Pparβ/δ-null mice were fed either a control or 4% ethanol diet and examined after 4-7 months of treatment. Ethanol fed Pparβ/δ-null mice exhibited steatosis after short-term treatment compared to controls, the latter effect appeared to be due to increased activity of sterol regulatory element binding protein 1c (SREBP1c). The wild-type and Pparβ/δ-null mice fed the control diet showed clear differences in their urinary metabolomic profiles. In particular, metabolites associated with arginine and proline metabolism, and glycerolipid metabolism, were markedly different between genotypes suggesting a constitutive role for PPARβ/δ in the metabolism of these amino acids. Interestingly, urinary excretion of taurine was present in ethanol-fed wild-type mice but markedly lower in similarly treated Pparβ/δ-null mice. Evidence suggests that PPARβ/δ modulates pyridoxal kinase activity by altering Km, consistent with the observed decreased in urinary taurine excretion. These data collectively suggest that PPARβ/δ prevents ethanol-induced hepatic effects by inhibiting hepatic lipogenesis, modulation of amino acid metabolism, and altering pyridoxal kinase activity.