Urinary metabolomics in Fxr-null mice reveals activated adaptive metabolic pathways upon bile acid challenge

Farnesoid X receptor (FXR) is a nuclear receptor that regulates genes involved in synthesis, metabolism, and transport of bile acids and thus plays a major role in maintaining bile acid homeostasis. In this study, metabolomic responses were investigated in urine of wild-type and Fxr-null mice fed cholic acid, an FXR ligand, using ultra-performance liquid chromatography (UPLC) coupled with electrospray time-of-flight mass spectrometry (TOFMS). Multivariate data analysis between wild-type and Fxr-null mice on a cholic acid diet revealed that the most increased ions were metabolites of p-cresol (4-methylphenol), corticosterone, and cholic acid in Fxr-null mice. The structural identities of the above metabolites were confirmed by chemical synthesis and by comparing retention time (RT) and/or tandem mass fragmentation patterns of the urinary metabolites with the authentic standards. Tauro-3alpha,6,7alpha,12alpha-tetrol (3alpha,6,7alpha,12alpha-tetrahydroxy-5beta-cholestan-26-oyltaurine), one of the most increased metabolites in Fxr-null mice on a CA diet, is a marker for efficient hydroxylation of toxic bile acids possibly through induction of Cyp3a11. A cholestatic model induced by lithocholic acid revealed that enhanced expression of Cyp3a11 is the major defense mechanism to detoxify cholestatic bile acids in Fxr-null mice. These results will be useful for identification of biomarkers for cholestasis and for determination of adaptive molecular mechanisms in cholestasis.

Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Comparative metabolism of cyclophosphamide and ifosfamide in the mouse using UPLC-ESI-QTOFMS-based metabolomics

Ifosfamide (IF) and cyclophosphamide (CP) are common chemotherapeutic agents. Interestingly, while the two drugs are isomers, only IF treatment is known to cause nephrotoxicity and neurotoxicity. Therefore, it was anticipated that a comparison of IF and CP drug metabolites in the mouse would reveal reasons for this selective toxicity. Drug metabolites were profiled by ultra-performance liquid chromatography-linked electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS), and the results analyzed by multivariate data analysis. Of the total 23 drug metabolites identified by UPLC-ESI-QTOFMS for both IF and CP, five were found to be novel. Ifosfamide preferentially underwent N-dechloroethylation, the pathway yielding 2-chloroacetaldehyde, while cyclophosphamide preferentially underwent ring-opening, the pathway yielding acrolein (AC). Additionally, S-carboxymethylcysteine and thiodiglycolic acid, two downstream IF and CP metabolites, were produced similarly in both IF- and CP-treated mice. This may suggest that other metabolites, perhaps precursors of thiodiglycolic acid, may be responsible for IF encephalopathy and nephropathy.

Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling

There has been limited analysis of the effects of hepatocellular carcinoma (HCC) on liver metabolism and circulating endogenous metabolites. Here, we report the findings of a plasma metabolomic investigation of HCC patients by ultraperformance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS), random forests machine learning algorithm, and multivariate data analysis. Control subjects included healthy individuals as well as patients with liver cirrhosis or acute myeloid leukemia. We found that HCC was associated with increased plasma levels of glycodeoxycholate, deoxycholate 3-sulfate, and bilirubin. Accurate mass measurement also indicated upregulation of biliverdin and the fetal bile acids 7α-hydroxy-3-oxochol-4-en-24-oic acid and 3-oxochol-4,6-dien-24-oic acid in HCC patients. A quantitative lipid profiling of patient plasma was also conducted by ultraperformance liquid chromatography-electrospray ionization-triple quadrupole mass spectrometry (UPLC-ESI-TQMS). By this method, we found that HCC was also associated with reduced levels of lysophosphocholines and in 4 of 20 patients with increased levels of lysophosphatidic acid [LPA(16:0)], where it correlated with plasma α-fetoprotein levels. Interestingly, when fatty acids were quantitatively profiled by gas chromatography-mass spectrometry (GC-MS), we found that lignoceric acid (24:0) and nervonic acid (24:1) were virtually absent from HCC plasma. Overall, this investigation illustrates the power of the new discovery technologies represented in the UPLC-ESI-QTOFMS platform combined with the targeted, quantitative platforms of UPLC-ESI-TQMS and GC-MS for conducting metabolomic investigations that can engender new insights into cancer pathobiology.

Radiation metabolomics. 4. UPLC-ESI-QTOFMS-Based metabolomics for urinary biomarker discovery in gamma-irradiated rats

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or γ-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of γ radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2'-deoxyuridine, 2'deoxyxanthosine, N(1)-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2'-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2'Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure.

Radiation metabolomics. 5. Identification of urinary biomarkers of ionizing radiation exposure in nonhuman primates by mass spectrometry-based metabolomics

Mass spectrometry-based metabolomics has previously demonstrated utility for identifying biomarkers of ionizing radiation exposure in cellular, mouse and rat in vivo radiation models. To provide a valuable link from small laboratory rodents to humans, γ-radiation-induced urinary biomarkers were investigated using a nonhuman primate total-body-irradiation model. Mass spectrometry-based metabolomics approaches were applied to determine whether biomarkers could be identified, as well as the previously discovered rodent biomarkers of γ radiation. Ultra-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry analysis was carried out on a time course of clean-catch urine samples collected from nonhuman primates (n = 6 per cohort) exposed to sham, 1.0, 3.5, 6.5 or 8.5 Gy doses of (60)Co γ ray (∼0.55 Gy/min) ionizing radiation. By multivariate data analysis, 13 biomarkers of radiation were discovered: N-acetyltaurine, isethionic acid, taurine, xanthine, hypoxanthine, uric acid, creatine, creatinine, tyrosol sulfate, 3-hydroxytyrosol sulfate, tyramine sulfate, N-acetylserotonin sulfate, and adipic acid. N-Acetyltaurine, isethionic acid, and taurine had previously been identified in rats, and taurine and xanthine in mice after ionizing radiation exposure. Mass spectrometry-based metabolomics has thus successfully revealed and verified urinary biomarkers of ionizing radiation exposure in the nonhuman primate for the first time, which indicates possible mechanisms for ionizing radiation injury.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Radiation metabolomics. 1. Identification of minimally invasive urine biomarkers for gamma-radiation exposure in mice

Gamma-radiation exposure has both short- and long-term adverse health effects. The threat of modern terrorism places human populations at risk for radiological exposures, yet current medical countermeasures to radiation exposure are limited. Here we describe metabolomics for gamma-radiation biodosimetry in a mouse model. Mice were gamma-irradiated at doses of 0, 3 and 8 Gy (2.57 Gy/min), and urine samples collected over the first 24 h after exposure were analyzed by ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). Multivariate data were analyzed by orthogonal partial least squares (OPLS). Both 3- and 8-Gy exposures yielded distinct urine metabolomic phenotypes. The top 22 ions for 3 and 8 Gy were analyzed further, including tandem mass spectrometric comparison with authentic standards, revealing that N-hexanoylglycine and beta-thymidine are urinary biomarkers of exposure to 3 and 8 Gy, 3-hydroxy-2-methylbenzoic acid 3-O-sulfate is elevated in urine of mice exposed to 3 but not 8 Gy, and taurine is elevated after 8 but not 3 Gy. Gene Expression Dynamics Inspector (GEDI) self-organizing maps showed clear dose-response relationships for subsets of the urine metabolome. This approach is useful for identifying mice exposed to gamma radiation and for developing metabolomic strategies for noninvasive radiation biodosimetry in humans.

UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Serum metabolomics reveals irreversible inhibition of fatty acid beta-oxidation through the suppression of PPARalpha activation as a contributing mechanism of acetaminophen-induced hepatotoxicity

Metabolic bioactivation, glutathione depletion, and covalent binding are the early hallmark events after acetaminophen (APAP) overdose. However, the subsequent metabolic consequences contributing to APAP-induced hepatic necrosis and apoptosis have not been fully elucidated. In this study, serum metabolomes of control and APAP-treated wild-type and Cyp2e1-null mice were examined by liquid chromatography-mass spectrometry (LC-MS) and multivariate data analysis. A dose-response study showed that the accumulation of long-chain acylcarnitines in serum contributes to the separation of wild-type mice undergoing APAP-induced hepatotoxicity from other mouse groups in a multivariate model. This observation, in conjunction with the increase of triglycerides and free fatty acids in the serum of APAP-treated wild-type mice, suggested that APAP treatment can disrupt fatty acid beta-oxidation. A time-course study further indicated that both wild-type and Cyp2e1-null mice had their serum acylcarnitine levels markedly elevated within the early hours of APAP treatment. While remaining high in wild-type mice, serum acylcarnitine levels gradually returned to normal in Cyp2e1-null mice at the end of the 24 h treatment. Distinct from serum aminotransferase activity and hepatic glutathione levels, the pattern of serum acylcarnitine accumulation suggested that acylcarnitines can function as complementary biomarkers for monitoring the APAP-induced hepatotoxicity. An essential role for peroxisome proliferator-activated receptor alpha (PPARalpha) in the regulation of serum acylcarnitine levels was established by comparing the metabolomic responses of wild-type and Ppara-null mice to a fasting challenge. The upregulation of PPARalpha activity following APAP treatment was transient in wild-type mice but was much more prolonged in Cyp2e1-null mice. Overall, serum metabolomics of APAP-induced hepatotoxicity revealed that the CYP2E1-mediated metabolic activation and oxidative stress following APAP treatment can cause irreversible inhibition of fatty acid oxidation, potentially through suppression of PPARalpha-regulated pathways.

Fenofibrate metabolism in the cynomolgus monkey using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics

Fenofibrate, widely used for the treatment of dyslipidemia, activates the nuclear receptor, peroxisome proliferator-activated receptor alpha. However, liver toxicity, including liver cancer, occurs in rodents treated with fibrate drugs. Marked species differences occur in response to fibrate drugs, especially between rodents and humans, the latter of which are resistant to fibrate-induced cancer. Fenofibrate metabolism, which also shows species differences, has not been fully determined in humans and surrogate primates. In the present study, the metabolism of fenofibrate was investigated in cynomolgus monkeys by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS)-based metabolomics. Urine samples were collected before and after oral doses of fenofibrate. The samples were analyzed in both positive-ion and negative-ion modes by UPLC-QTOFMS, and after data deconvolution, the resulting data matrices were subjected to multivariate data analysis. Pattern recognition was performed on the retention time, mass/charge ratio, and other metabolite-related variables. Synthesized or purchased authentic compounds were used for metabolite identification and structure elucidation by liquid chromatographytandem mass spectrometry. Several metabolites were identified, including fenofibric acid, reduced fenofibric acid, fenofibric acid ester glucuronide, reduced fenofibric acid ester glucuronide, and compound X. Another two metabolites (compound B and compound AR), not previously reported in other species, were characterized in cynomolgus monkeys. More importantly, previously unknown metabolites, fenofibric acid taurine conjugate and reduced fenofibric acid taurine conjugate were identified, revealing a previously unrecognized conjugation pathway for fenofibrate.

Metabolomics reveals a novel vitamin E metabolite and attenuated vitamin E metabolism upon PXR activation

Pregnane X receptor (PXR) is an important nuclear receptor xenosensor that regulates the expression of metabolic enzymes and transporters involved in the metabolism of xenobiotics and endobiotics. In this study, ultra-performance liquid chromatography (UPLC) coupled with electrospray time-of-flight mass spectrometry (TOFMS), revealed altered urinary metabolomes in both Pxr-null and wild-type mice treated with the mouse PXR activator pregnenolone 16alpha-carbonitrile (PCN). Multivariate data analysis revealed that PCN significantly attenuated the urinary vitamin E metabolite alpha-carboxyethyl hydroxychroman (CEHC) glucuronide together with a novel metabolite in wild-type but not Pxr-null mice. Deconjugation experiments with beta-glucuronidase and beta-glucosidase suggested that the novel urinary metabolite was gamma-CEHC beta-D-glucoside (Glc). The identity of gamma-CEHC Glc was confirmed by chemical synthesis and by comparing tandem mass fragmentation of the urinary metabolite with the authentic standard. The lower urinary CEHC was likely due to PXR-mediated repression of hepatic sterol carrier protein 2 involved in peroxisomal beta-oxidation of branched-chain fatty acids (BCFA). Using a combination of metabolomic analysis and a genetically modified mouse model, this study revealed that activation of PXR results in attenuated levels of the two vitamin E conjugates, and identification of a novel vitamin E metabolite, gamma-CEHC Glc. Activation of PXR results in attenuated levels of the two vitamin E conjugates that may be useful as biomarkers of PXR activation.

Data-driven estimates of the number of clusters in multivariate time series

An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

Benchmarking in the SWISSspine registry: results of 52 Dynardi lumbar total disc replacements compared with the data pool of 431 other lumbar disc prostheses

The SWISSspine registry is the first mandatory registry of its kind in the history of Swiss orthopaedics and it follows the principle of "coverage with evidence development". Its goal is the generation of evidence for a decision by the Swiss federal office of health about reimbursement of the concerned technologies and treatments by the basic health insurance of Switzerland. Recently, developed and clinically implemented, the Dynardi total disc arthroplasty (TDA) accounted for 10% of the implanted lumbar TDAs in the registry. We compared the outcomes of patients treated with Dynardi to those of the recipients of the other TDAs in the registry. Between March 2005 and October 2009, 483 patients with single-level TDA were documented in the registry. The 52 patients with a single Dynardi lumbar disc prosthesis implanted by two surgeons (CE and OS) were compared to the 431 patients who received one of the other prostheses. Data were collected in a prospective, observational multicenter mode. Surgery, implant, 3-month, 1-year, and 2-year follow-up forms as well as comorbidity, NASS and EQ-5D questionnaires were collected. For statistical analyses, the Wilcoxon signed-rank test and chi-square test were used. Multivariate regression analyses were also performed. Significant and clinically relevant reduction of low back pain and leg pain as well as improvement in quality of life was seen in both groups (P < 0.001 postop vs. preop). There were no inter-group differences regarding postoperative pain levels, intraoperative and follow-up complications or revision procedures with a new hospitalization. However, significantly more Dynardi patients achieved a minimum clinically relevant low back pain alleviation of 18 VAS points and a quality of life improvement of 0.25 EQ-5D points. The patients with Dynardi prosthesis showed a similar outcome to patients receiving the other TDAs in terms of postoperative low back and leg pain, complications, and revision procedures. A higher likelihood for achieving a minimum clinically relevant improvement of low back pain and quality of life in Dynardi patients was observed. This difference might be due to the large number of surgeons using other TDAs compared to only two surgeons using the Dynardi TDA, with corresponding variations in patient selection, patient-physician interaction and other factors, which cannot be assessed in a registry study.