Keywords:Breast cancer Neoadjuvant chemotherapy Therapy response Metabolomics NMR LCeMS A B S T R A C TBreast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n ¼ 8), partial (n ¼ 14) and no response (n ¼ 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatographyemass spectrometry (LCeMS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LCeMS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.ª 2012 Federation of European Biochemical Societies.Published by Elsevier B.V. All rights reserved.Abbreviations: NMR, nuclear magnetic resonance; LCeMS, liquid chromatographyemass spectrometry; pCR, pathologic complete response; SD, stable disease; PR, partial response; PLS-DA, partial least squares discriminant analysis; ROC, receiver operating characteristic; CV, cross validation.

Sepsis often results in damage to multiple organ systems, possibly due to severe mitochondrial dysfunction. Two members of the sirtuin family, SIRT1 and SIRT3, have been implicated in the reversal of mitochondrial damage. The aim of this study was to determine the role of SIRT1/3 in acute kidney injury (AKI) following sepsis in a septic rat model. After drug pretreatment and cecal ligation and puncture (CLP) model reproduction in the rats, we performed survival time evaluation and kidney tissue extraction and renal tubular epithelial cell (RTEC) isolation. We observed reduced SIRT1/3 activity, elevated acetylated SOD2 (ac-SOD2) levels and oxidative stress, and damaged mitochondria in RTECs following sepsis. Treatment with resveratrol (RSV), a chemical SIRT1 activator, effectively restored SIRT1/3 activity, reduced acetylated SOD2 levels, ameliorated oxidative stress and mitochondrial function of RTECs, and prolonged survival time. However, the beneficial effects of RSV were greatly abrogated by Ex527, a selective inhibitor of SIRT1. These results suggest a therapeutic role for SIRT1 in the reversal of AKI in septic rat, which may rely on SIRT3-mediated deacetylation of SOD2. SIRT1/3 activation could therefore be a promising therapeutic strategy to treat sepsis-associated AKI.

BackgroundEsophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret's esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication.Methodology/Principal FindingsSerum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites.Conclusions/SignificanceMetabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.

A convenient and fast method for quantifying urea in biofluids is demonstrated using NMR analysis and the solvent water signal as a concentration reference. The urea concentration can be accurately determined with errors less than 3% between 1 mM and 50 mM, and less than 2% above 50 mM in urine and serum. The method is promising for various applications with advantages of simplicity, high accuracy, and fast non-destructive detection. With an ability to measure other metabolites simultaneously, this NMR method is also likely to find applications in metabolic profiling and system biology.

NMR spectroscopy is a powerful analytical tool for both qualitative and quantitative analysis. However, accurate quantitative analysis in complex fluids such as human blood plasma is challenging, and analysis using one-dimensional NMR is limited by signal overlap. It is impractical to use heteronuclear experiments involving natural abundance 13C on a routine basis due to low sensitivity, despite their improved resolution. Focusing on circumventing such bottlenecks, this study demonstrates the utility of a combination of isotope tagged NMR experiments to analyze metabolites in human blood plasma. 1H-15N HSQC and 1H-13C HSQC experiments on the isotope tagged samples combined with the conventional 1H one-dimensional and 1H-1H TOCSY experiments provide quantitative information on a large number of metabolites in plasma. The methods were first tested on a mixture of 28 synthetic analogues of metabolites commonly present in human blood; twenty-seven metabolites in a standard NIST (National Institute of Standards and Technology) human blood plasma were then identified and quantified with an average coefficient of variation of 2.4 % for 17 metabolites and 5.6% when all the metabolites were considered. Carboxylic acids and amines represent a majority of the metabolites in body fluids and their analysis by isotope tagging enables a significant enhancement of the metabolic pool for biomarker discovery applications. Improved sensitivity and resolution of NMR experiments imparted by 15N and 13C isotope tagging is attractive for both the enhancement of the detectable metabolic pool and accurate analysis of plasma metabolites. The approach can be easily extended to many additional metabolites in almost any biological mixture.

Bladder cancer is one of the leading lethal cancers worldwide. With the high risk of recurrence for bladder cancer following the initial diagnoses, lifelong monitoring of patients is necessary. The lack of adequate sensitivity and specificity of current noninvasive monitoring approaches including urine cytology, other urine tests, and imaging, underlines the importance of studies that focus on the detection of more reliable biomarkers for this cancer. The emerging area of metabolomics, which deals with the analysis of a large number of small molecules in a single step, promises immense potential for discovering metabolite markers for screening and monitoring treatment response and recurrence in patients with bladder cancer. Since naturally-occurring canine transitional cell carcinoma of the urinary bladder is very similar to human invasive bladder cancer, spontaneous canine transitional cell carcinoma has been applied as a relevant animal model of human invasive transitional cell carcinoma. In this study, we have focused on profiling the metabolites in urine from dogs with transitional cell carcinoma and healthy control dogs combining nuclear magnetic resonance spectroscopy and statistical analysis methods. (1)H NMR-based metabolite profiling analysis was shown to be an effective approach for differentiating samples from dogs with transitional cell carcinoma and healthy controls based on a partial least square-discriminant analysis of the NMR spectra. In addition, there were significant differences in the levels of six individual metabolites between samples from dogs with transitional cell carcinoma and the control group based on the Student's t-test. These metabolites were selected to build a separate partial least square-discriminant analysis model that was then used to test the classification accuracy. The result showed good classification between transitional cell carcinoma and control groups with the area under the receiver operating characteristic curve of 0.85. The sensitivity and specificity of the model were 86% and 78%, respectively. These results suggest that urine metabolic profiling may have potential for early detection of bladder cancer and of bladder cancer recurrence following treatment, and may enhance our understanding of the mechanisms involved.

Sepsis is the leading cause of death in the intensive care unit and continues to lack effective treatment. It is widely accepted that high-mobility group box 1 (HMGB1) is a key inflammatory mediator in the pathogenesis of sepsis. Moreover, some studies indicate that the functions of HMGB1 depend on its molecular localization and posttranslational modifications. Our previous study confirms that sirtuin 1, silent information regulator 2-related enzyme 1 (SIRT1), a type III deacetylase, can ameliorate sepsis-associated acute kidney injury (SA-AKI). We explored the effect and mechanism of SIRT1 on HMGB1 using a mouse model of cecal ligation and puncture-induced sepsis and LPS-treated human kidney (HK-2) cell line. We found that HMGB1 is elevated in the serum but is gradually reduced in kidney cells in the later stages of septic mice. The acetylation modification of HMGB1 is a key process before its nucleus-to-cytoplasm translocation and extracellular secretion in kidney cells, accelerating the development of SA-AKI. Moreover, SIRT1 can physically interact with HMGB1 at the deacetylated lysine sites K28, K29, and K30, subsequently suppressing downstream inflammatory signaling. Thus the SIRT1-HMGB1 signaling pathway is a crucial mechanism in the development of SA-AKI and presents a novel experimental perspective for future SA-AKI research.

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal to noise. In this study we present a new approach, RANSY (Ratio Analysis NMR Spectroscopy), which identifies all the peaks of a specific metabolite based on the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficient of variations derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to 1H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.