Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers

The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas

The 2021 edition of the World Health Organization (WHO) classification of central nervous system tumors introduces significant revisions across various tumor types. These updates, encompassing changes in diagnostic techniques, genomic integration, terminology, and grading, are crucial for radiologists, who play a critical role in interpreting brain tumor imaging. Such changes impact the diagnosis and management of nearly all central nervous system tumor categories, including the reclassification, addition, and removal of specific tumor entities. Given their pivotal role in patient care, radiologists must remain conversant with these revisions to effectively contribute to multidisciplinary tumor boards and collaborate with peers in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This knowledge is essential not only for accurate diagnosis and staging, but also for understanding the molecular and genetic underpinnings of tumors, which can influence treatment decisions and prognostication. This review, therefore, focuses on the most pertinent updates concerning the classification of adult diffuse gliomas, highlighting the aspects most relevant to radiological practice. Emphasis is placed on the implications of new genetic information on tumor behavior and imaging findings, providing necessary tools to stay abreast of advancements in the field. This comprehensive overview aims to enhance the radiologist's ability to integrate new WHO classification criteria into everyday practice, ultimately improving patient outcomes through informed and precise imaging assessments.

Multiparametric Analysis Combining DSC-MR Perfusion and [18F]FET-PET is Superior to a Single Parameter Approach for Differentiation of Progressive Glioma from Radiation Necrosis

PURPOSE

Perfusion-weighted (PWI) magnetic resonance imaging (MRI) and O‑(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) positron emission tomography (PET) are both useful for discrimination of progressive disease (PD) from radiation necrosis (RN) in patients with gliomas. Previous literature showed that the combined use of FET-PET and MRI-PWI is advantageous; hhowever the increased diagnostic performances were only modest compared to the use of a single modality. Hence, the goal of this study was to further explore the benefit of combining MRI-PWI and [18F]FET-PET for differentiation of PD from RN. Secondarily, we evaluated the usefulness of cerebral blood flow (CBF), mean transit time (MTT) and time to peak (TTP) as previous studies mainly examined cerebral blood volume (CBV).

METHODS

In this single center study, we retrospectively identified patients with WHO grades II-IV gliomas with suspected tumor recurrence, presenting with ambiguous findings on structural MRI. For differentiation of PD from RN we used both MRI-PWI and [18F]FET-PET. Dynamic susceptibility contrast MRI-PWI provided normalized parameters derived from perfusion maps (r(relative)CBV, rCBF, rMTT, rTTP). Static [18F]FET-PET parameters including mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated. Based on histopathology and radioclinical follow-up we diagnosed PD in 27 and RN in 10 cases. Using the receiver operating characteristic (ROC) analysis, area under the curve (AUC) values were calculated for single and multiparametric models. The performances of single and multiparametric approaches were assessed with analysis of variance and cross-validation.

RESULTS

After application of inclusion and exclusion criteria, we included 37 patients in this study. Regarding the in-sample based approach, in single parameter analysis rTBRmean (AUC = 0.91, p < 0.001), rTBRmax (AUC = 0.89, p < 0.001), rTTP (AUC = 0.87, p < 0.001) and rCBVmean (AUC = 0.84, p < 0.001) were efficacious for discrimination of PD from RN. The rCBFmean and rMTT did not reach statistical significance. A classification model consisting of TBRmean, rCBVmean and rTTP achieved an AUC of 0.98 (p < 0.001), outperforming the use of rTBRmean alone, which was the single parametric approach with the highest AUC. Analysis of variance confirmed the superiority of the multiparametric approach over the single parameter one (p = 0.002). While cross-validation attributed the highest AUC value to the model consisting of TBRmean and rCBVmean, it also suggested that the addition of rTTP resulted in the highest accuracy. Overall, multiparametric models performed better than single parameter ones.

CONCLUSION

A multiparametric MRI-PWI and [18F]FET-PET model consisting of TBRmean, rCBVmean and PWI rTTP significantly outperformed the use of rTBRmean alone, which was the best single parameter approach. Secondarily, we firstly report the potential usefulness of PWI rTTP for discrimination of PD from RN in patients with glioma; however, for validation of our findings the prospective studies with larger patient samples are necessary.

The Effect of Blood Contained in the Samples on the Metabolomic Profile of Mouse Brain Tissue: A Study by NMR Spectroscopy

(1) Recently, metabolic profiling of the tissue in the native state or extracts of its metabolites has become increasingly important in the field of metabolomics. An important factor, in this case, is the presence of blood in a tissue sample, which can potentially lead to a change in the concentration of tissue metabolites and, as a result, distortion of experimental data and their interpretation. (2) In this paper, the metabolomic profiling based on NMR spectroscopy was performed to determine the effect of blood contained in the studied samples of brain tissue on their metabolomic profile. We used 13 male laboratory CD-1^® IGS mice for this study. The animals were divided into two groups. The first group of animals (n = 7) was subjected to the perfusion procedure, and the second group of animals (n = 6) was not perfused. The brain tissues of the animals were homogenized, and the metabolite fraction was extracted with a water/methanol/chloroform solution. Samples were studied by high-frequency ¹H-NMR spectroscopy with subsequent statistical data analysis. The group comparison was performed with the use of the Student's test. We identified 36 metabolites in the brain tissue with the use of NMR spectroscopy. (3) For the major set of studied metabolites, no significant differences were found in the brain tissue metabolite concentrations in the native state and after the blood removal procedure. (4) Thus, it was shown that the presence of blood does not have a significant effect on the metabolomic profile of the brain in animals without pathologies.

Robust detection of oncometabolic aberrations by 1H-13C heteronuclear single quantum correlation in intact biological specimens

Magnetic resonance (MR) spectroscopy has potential to non-invasively detect metabolites of diagnostic significance for precision oncology. Yet, many metabolites have similar chemical shifts, yielding highly convoluted ¹H spectra of intact biological material and limiting diagnostic utility. Here, we show that hydrogen–carbon heteronuclear single quantum correlation (¹H–¹³C HSQC) offers dramatic improvements in sensitivity compared to one-dimensional (1D) ¹³C NMR and significant signal deconvolution compared to 1D ¹H spectra in intact biological settings. Using a standard NMR spectroscope with a cryoprobe but without specialized signal enhancing features such as magic angle spinning, metabolite extractions or ¹³C-isotopic enrichment, we obtain well-resolved 2D ¹H–¹³C HSQC spectra in live cancer cells, in ex vivo freshly dissected xenografted tumors and resected primary tumors. This method can identify tumors with specific oncometabolite alterations such as IDH mutations by 2-hydroxyglutarate and PGD-deleted tumors by gluconate. Results suggest potential of ¹H–¹³C HSQC as a non-invasive diagnostic in precision oncology.

Barekatain et al. demonstrate that hydrogen–carbon heteronuclear single quantum correlation (HSQC) spectra, obtained using a standard NMR spectroscope, can detect tumours with specific oncometabolite alterations including IDH1 mutant glioblastoma, suggesting the feasibility of this method as a diagnostic tool.

Advances in Analysis and Detection of Major Mycotoxins in Foods

Mycotoxins are the most widely studied biological toxins, which contaminate foods at very low concentrations. This review describes the emerging extraction techniques and the current and alternatives analytical techniques and methods that have been used to successfully detect and identify important mycotoxins. Some of them have proven to be particularly effective in not only the detection of mycotoxins, but also in detecting mycotoxin-producing fungi. Chromatographic techniques such as high-performance liquid chromatography coupled with various detectors like fluorescence, diode array, UV, liquid chromatography coupled with mass spectrometry, and liquid chromatography-tandem mass spectrometry, have been powerful tools for analyzing and detecting major mycotoxins. Recent progress of the development of rapid immunoaffinity-based detection techniques such as immunoassays and biosensors, as well as emerging technologies like proteomic and genomic methods, molecular techniques, electronic nose, aggregation-induced emission dye, quantitative NMR and hyperspectral imaging for the detection of mycotoxins in foods, have also been presented.

Proton polarization enhancement of up to 150 with dynamic nuclear polarization of plasma-treated glucose powder

Dynamic nuclear polarization (DNP) for the enhancement of the NMR signals of specific metabolites has recently found applications in the context of magnetic resonance imaging (MRI). Currently, DNP signal enhancement is implemented in clinical systems through the use of exogenous stable organic free radicals, known as polarization agents (PAs), mixed in a solution with the metabolite of interest. These PAs are medically undesirable and thus must be filtered out prior to patient injection - a task that involves considerable technical complexity and consumes valuable time during which the polarization decays. Here, we aim to demonstrate DNP enhancements large enough for clinical relevance using a process free of exogenous PAs. This is achieved by processing (soft grinding) the metabolite in its solid form and subsequently exposing it to plasma in a dilute atmosphere to produce chemically-unstable free radicals (herein referred to as electrical-discharge-induced radicals - EDIRs) within the powder. These samples are then subjected to the normal DNP procedure of microwave irradiation while placed under a high static magnetic field, and their NMR signal is measured to quantify the enhancement of the protons' signal in the solid. Proton signal enhancements (measured as the ratio of the NMR signal with microwave irradiation to the NMR signal without microwave irradiation) of up to 150 are demonstrated in glucose. Upon fast dissolution, the free radicals are annihilated, leaving the sample in its original chemical composition (which is safe for clinical use) without any need for filtration and cumbersome quality control procedures. We thus conclude that EDIRs are found to be highly efficient in providing DNP enhancement levels that are on par with those achieved with the exogenous PAs, while being safe for clinical use. This opens up the possibility of applying our method to clinical scenarios with minimal risks and lower costs per procedure.

Evaluation of sample preparation protocols for quantitative NMR-based metabolomics

INTRODUCTION

Quantification of metabolites in biological fluids and tissues by NMR spectroscopy is challenged by the presence of abundant macromolecules and lipoproteins in samples, which give broad signals in the NMR spectra. To improve the quality of NMR spectra the different protocols for protein and lipid removal from the sample are used.

OBJECTIVES

This work is aimed at the evaluation of the effectiveness of various methods of purification of blood serum from proteins and lipids for ¹H NMR metabolomic profiling.

METHODS

The advantages and limitations of different methods of the sample preparation for NMR-based quantitative metabolomics have been compared, including ultrafiltration, methanol and ethanol extractions with and without additional lipid removal, and methanol-chloroform extraction.

RESULTS

The concentrations of 30 abundant metabolites extracted from human blood serum have been measured. It is found that ultrafiltration provides the best lipid removal, but causes significant and inhomogeneous metabolite losses. Ethanol and methanol extractions demonstrate similar performance with the minimal metabolite losses, and are ideal for fluids and tissues with low lipid content. The additional purification of alcohol extracts from lipids allows for the significant improving of NMR spectra, but causes additional metabolite losses.

CONCLUSIONS

The methanol-chloroform extraction seems to be an optimal method for tissues with the high lipid content, providing a satisfactory lipid removal and low metabolite losses. The ultrafiltration leads to large losses of metabolites (up to 60%) and for this reason is not suitable for quantitative analysis.

Quantitative NMR-Based Metabolomics on Tissue Biomarkers and Its Translation into In Vivo Magnetic Resonance Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy is an established analytical platform for analyzing metabolic profiles of cells, tissues, and body fluids. There are several advantages in introducing an NMR-based study design into metabolomics studies, including a fast and comprehensive detection, characterization, and quantification of dozens of endogenous metabolites in a single NMR spectrum. Quantitative proton ¹H-NMR is the most useful NMR-based platform for metabolomics. The frozen tissues can be analyzed noninvasively using a high-resolution magic angle spinning (HR-MAS) ¹H-NMR spectroscopy; or several extraction techniques can be applied to detect additional metabolites using a conventional liquid-based NMR technique. In this chapter, we report on tissue collection, handling, extraction methods, and ¹H-NMR acquisition protocols developed in the past decades for a precise and quantitative NMR-metabolomics approach. The NMR acquisition protocols (both HR-MAS and conventional ¹H-NMR spectroscopy) and spectral analysis steps are also presented. Since NMR can be applied "in vivo" using horizontal bore MRI scanners, several in vivo sequences for localized ¹H-MRS (magnetic resonance spectroscopy) are presented which can be directly applied for noninvasive detection of brain metabolites.

Alpha-oxoglutarate inhibits the proliferation of immortalized normal bladder epithelial cells via an epigenetic switch involving ARID1A

Interstitial cystitis (IC) is a chronic urinary tract disease that is characterized by unpleasant sensations, such as persistent pelvic pain, in the absence of infection or other identifiable causes. We previously performed comprehensive metabolomics profiling of urine samples from IC patients using nuclear magnetic resonance and gas-chromatography/mass spectrometry and found that urinary α-oxoglutarate (α-OG), was significantly elevated. α-OG, a tricarboxylic acid (TCA) cycle intermediate, reportedly functions to suppress the proliferation of immortalized normal human bladder epithelial cells. Here, we identified AT-rich interactive domain 1 A (ARID1A), a key chromatin remodeler, as being hypomethylated and upregulated by α-OG treatment. This was done through EPIC DNA methylation profiling and subsequent biochemical approaches, including quantitative RT-PCR and western blot analyses. Furthermore, we found that α-OG almost completely suppresses ten-eleven translocation (TET) activity, but does not affect DNA methyltransferase (DNMT) activity. Altogether, our studies reveal the potential role of α-OG in epigenetic remodeling through its effects on ARID1A and TET expression in the bladder. This may provide a new possible therapeutic strategy in treating IC.

Magnetic Resonance Spectroscopy Studies of Mouse Models of Cancer

Magnetic resonance spectroscopy (MRS) or spectroscopic imaging (MRSI) enables the detection of metabolites, amino acids, and lipids, among other biomolecules, in tumors of live mouse models of cancer. Tumor-bearing mice are anesthetized by breathing isoflurane in a magnetic resonance (MR) scanner dedicated to small animal MR. Here we describe the overall setup and steps for measuring ¹H and ³¹P MRS and ¹H MRSI of orthotopic breast tumor models in mice with surface coils. This protocol can be adapted to the use of volume coils to measure ¹H and ³¹P MRS(I) of tumor models that grow inside the body. We address issues of animal handling, setting up the measurement, measurement options, and data analysis.

Metabolomics identifies metabolite biomarkers associated with acute rejection after heart transplantation in rats

The aim of this study was to identify metabolite biomarkers associated with acute rejection after heart transplantation in rats using a LC-MS-based metabolomics approach. A model of heterotopic cardiac xenotransplantation was established in rats, with Wistar rats as donors and SD rats as recipients. Blood and cardiac samples were collected from blank control rats (Group A), rats 5 (Group B) and 7 days (Group C) after heart transplantation, and pretreated rats 5 (Group D) and 7 days (Group E) post-transplantation for pathological and metabolomics analyses. We assessed International Society for Heart and Lung Transplantation (ISHLT) grades 0, 3B, 4, 1 and 1 rejection in groups A to E. There were 15 differential metabolites between groups A and B, 14 differential metabolites between groups A and C, and 10 differential metabolites between groups B and C. In addition, four common differential metabolites, including D-tagatose, choline, C16 sphinganine and D-glutamine, were identified between on days 5 and 7 post-transplantation. Our findings demonstrate that the panel of D-tagatose, choline, C16 sphinganine and D-glutamine exhibits a high sensitivity and specificity for the early diagnosis of acute rejection after heart transplantation, and LC-MS-based metabolomics approach has a potential value for screening post-transplantation biomarkers.

Metabolomic characterization of experimental ovarian cancer ascitic fluid

INTRODUCTION

Malignant ascites (MA) is a major cause of morbidity that occurs in 37% of ovarian cancer patients. The accumulation of MA in the peritoneal cavity due to cancer results in debilitating symptoms and extremely poor quality of life. There is an urgent unmet need to expand the understanding of MA to design effective treatment strategies, and to improve MA diagnosis.

OBJECTIVE

Our purpose here is to contribute to a better characterization of MA metabolic composition in ovarian cancer.

METHOD

We determined the metabolic composition of ascitic fluids resulting from orthotopic growth of two ovarian cancer cell lines, the mouse ID8-vascular endothelial growth factor (VEGF)-Defb29 cell line and the human OVCAR3 cell line using high-resolution ¹H MRS. ID8-VEGF-Defb29 tumors induce large volumes of ascites, while OVCAR3 tumors induce ascites less frequently and at smaller volumes. To better understand the factors driving the metabolic composition of the fluid, we characterized the metabolism of these ovarian cancer cells in culture by analyzing cell lysates and conditioned culture media with ¹H NMR.

RESULTS

Distinct metabolite patterns were detected in ascitic fluid collected from OVCAR3 and ID8-VEGF-Defb29 tumor bearing mice that were not reflected in the corresponding cell culture or conditioned medium.

CONCLUSION

High-resolution ¹H NMR metabolic markers of MA can be used to improve characterization and diagnosis of MA. Metabolic characterization of MA can provide new insights into how MA fluid supports cancer cell growth and resistance to treatment, and has the potential to identify metabolic targeting strategies to reduce or eliminate the formation of MA.

Magnetic Resonance Spectroscopy for Detection of 2-Hydroxyglutarate as a Biomarker for IDH Mutation in Gliomas

Mutations in the isocitrate dehydrogenase (IDH)1/2 genes are highly prevalent in gliomas and have been suggested to play an important role in the development and progression of the disease. Tumours harbouring these mutations exhibit a significant alteration in their metabolism resulting in the aberrant accumulation of the oncometabolite 2-hydroxygluarate (2-HG). As well as being suggested to play an important role in tumour progression, 2-HG may serve as a surrogate indicator of IDH status through non-invasive detection using magnetic resonance spectroscopy (MRS). In this review, we describe the recent efforts in developing MRS methods for detection and quantification of 2-HG in vivo and provide an assessment of the role of the 2-HG in gliomagenesis and patient prognosis.

Functional MRI for quantitative treatment response prediction in locally advanced rectal cancer

Despite advances in multimodality treatment strategies for locally advanced rectal cancer and improvements in locoregional control, there is still a considerable variation in response to neoadjuvant chemoradiotherapy (CRT). Accurate prediction of response to neoadjuvant CRT would enable early stratification of management according to good responders and poor responders, in order to adapt treatment to improve therapeutic outcomes in rectal cancer. Clinical studies in diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI have shown promising results for the prediction of therapeutic response in rectal cancer. DWI allows for assessment of tumour cellularity. DCE-MRI enables evaluation of factors of the tumour microvascular environment and changes in perfusion in response to treatment. Studies have demonstrated that predictors of good response to CRT include lower tumour pre-CRT apparent diffusion coefficient (ADC), greater percentage increase in ADC during and post CRT, and higher pre-CRT K^trans. However, the mean ADC and K^trans values do not adequately reflect tumour heterogeneity. Multiparametric MRI using quantitative DWI and DCE-MRI in combination, and a histogram analysis technique can assess tumour heterogeneity and its response to treatment. This strategy has the potential to improve the accuracy of therapeutic response prediction in rectal cancer and warrants further investigation.

Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules

Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world.

Macrophage-Mediated Clofazimine Sequestration Is Accompanied by a Shift in Host Energy Metabolism

Prolonged (8 weeks) oral administration of clofazimine results in a profound pharmacodynamic response-bioaccumulation in macrophages (including Kupffer cells) as intracellular crystal-like drug inclusions (CLDIs) with an associated increase in interleukin-1 receptor antagonist production. Notably, CLDI formation in Kupffer cells concomitantly occurs with the formation of macrophage-centric granulomas. Accordingly, we sought to understand the impact of these events on host metabolism using ¹H-nuclear magnetic resonance metabolomics. Mice received a clofazimine or vehicle-enriched (sham) diet for at least 8 weeks. At 2 weeks, the antimicrobial activity of clofazimine was evident by changes in urine metabolites. From 2 to 8 weeks, there was a striking change in metabolite levels indicative of a reorientation of host energy metabolism paralleling the onset of CLDI and granuloma formation. This was evidenced by a progressive reduction in urine levels of metabolites involved in one-carbon metabolism with corresponding increases in whole blood, and changes in metabolites associated with lipid, nucleotide and amino acid metabolism, and glycolysis. Although clofazimine-fed mice ate more, they gained less weight than control mice. Together, these results indicate that macrophage sequestration of clofazimine as CLDIs and granuloma formation is accompanied by a profound metabolic disruption in energy homeostasis and one-carbon metabolism.

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

CEST-MRI detects metabolite levels altered by breast cancer cell aggressiveness and chemotherapy response

Chemical exchange saturation transfer (CEST) is an MRI contrast mechanism that detects the exchange of protons from distinct hydroxyl, amine, and amide groups to tissue water through the transfer of signal loss, with repeated exchange enhancing their effective signal. We applied CEST to detect systematically 15 common cellular metabolites in a panel of differentially aggressive human breast cancer cell lines. The highest CEST contrast was generated by creatine, myo-inositol, glutamate, and glycerophosphocholine, whose cellular concentrations decreased with increasing breast cancer aggressiveness. These decreased metabolite concentrations resulted in turn in a decreased CEST profile with increasing breast cancer aggressiveness in water-soluble extracts of breast cell lines. Treatment of both breast cancer cell lines with the chemotherapy drug doxorubicin resulted in increased metabolic CEST profiles, which correlated with significant increases in creatine, phosphocreatine, and glycerophosphocholine. CEST can detect breast cancer aggressiveness and response to chemotherapy in water-soluble extracts of breast cell lines. The presented results help shed light on possible contributions from CEST-active metabolites to the CEST contrast produced by breast cancers. The metabolic CEST profile may improve detection sensitivity over conventional MRS, and may have the potential to assess breast cancer aggressiveness and response to chemotherapy non-invasively using MRI if specialized metabolic CEST profile detection can be realized in vivo. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomics and Its Application to Acute Lung Diseases

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

1D-¹H-nuclear magnetic resonance metabolomics reveals age-related changes in metabolites associated with experimental venous thrombosis

OBJECTIVE

Age is a significant risk factor for the development of venous thrombosis (VT), but the mechanism(s) that underlie this risk remain(s) undefined and poorly understood. Aging is known to adversely influence inflammation and affect metabolism. Untargeted metabolomics permits an agnostic assessment of the physiological landscape and lends insight into the mechanistic underpinnings of clinical phenotypes. The objective of this exploratory study was to test the feasibility of a metabolomics approach for identifying potential metabolic mechanisms of age-related VT.

METHODS

We subjected whole blood samples collected from young and old nonthrombosed controls and VT mice 2 days after thrombus induction using the electrolytic inferior vena cava, to a methanol:chloroform extraction and assayed the resulting aqueous fractions using 1D-(1)H- nuclear magnetic resonance. Normalized mouse metabolite data were compared across groups using analysis of variance (ANOVA) with Holm-Sidak post-testing. In addition, associations between metabolite concentrations and parameters of thrombosis such as thrombus and vein wall weights, and markers of inflammation, vein wall P- and E-selectin levels, were assessed using linear regression. The relatedness of the found significant metabolites was visually assessed using a bioinformatics tool, Metscape, which generates compound-reaction-enzyme-gene networks to aid in the interpretation of metabolomics data.

RESULTS

Old mice with VT had a greater mean vein wall weight compared with young mice with VT (P < .05). Clot weight differences between old and young mice followed the same trend as vein wall weight (0.011 ± 0.04 g vs 0.008 ± 0.003 g; P = not significant). Glutamine (ANOVA, P < .01), proline (ANOVA, P < .01), and phenylalanine (ANOVA, P < .05) levels were increased in old VT mice compared with age-matched controls and young VT mice. Betaine and/or trimethylamine N-oxide levels were increased in aged mice compared with young animals. Vein wall weight was strongly associated with glutamine (P < .05), and phenylalanine (P < .01) concentrations and there was a trend toward an association with proline (P = .09) concentration. Vein wall P-selectin, but not E-selectin levels, were increased in old VT mice and were associated with the three found metabolites of age-related VT. Collectively, with the addition of glutamate, these metabolites form a single compound-reaction-enzyme-gene network that was generated by Metscape.

CONCLUSIONS

We used 1D-(1)H-nuclear magnetic resonance-metabolite profiling to identify, for the first time, in an experimental model, three potential metabolites, glutamine, phenylalanine, and proline, associated with age-related VT. These metabolites are metabolically related and their levels are associated with vein wall weight and P-selectin concentrations. In aggregate, these findings provide a "roadmap" of pathways that could be interrogated in future studies, which could include provocation of the glutamine, phenylalanine, and proline pathways in the vein wall. This study introduces metabolomics as a new approach to furthering knowledge about the mechanisms of age-related VT.

Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock

RATIONALE

Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis.

OBJECTIVES

To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders.

METHODS

Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks.

MEASUREMENTS AND MAIN RESULTS

Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 μM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate.

CONCLUSIONS

The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.

Nuclear magnetic resonance: a key metabolomics platform in the drug discovery process

Metabolomics is an innovative tool that is now emerging in the drug discovery process. Indeed, its ability to follow the dynamic perturbations in the metabolome resulting from pathologies but also from drug treatment and or/toxicity is of value for the development of new therapeutic approaches. Nuclear magnetic resonance (NMR) spectroscopy, which is an important analytical technique for several steps of the lead discovery, validation and optimization processes, has been described, together with mass spectrometry (MS) as one of the major platform that could be used for metabolomics studies. This review highlights why NMR could be considered a key tool for the application of metabolomics in drug discovery.

In vivo determination of the volatile metabolites of saprotroph fungi by comprehensive two-dimensional gas chromatography

In this work, we discuss the use of multiway principal component analysis combined with comprehensive two-dimensional gas chromatography to study the volatile metabolites of the saprophytic fungus Memnoniella sp. isolated in vivo by headspace solid-phase microextraction. This fungus has been identified as having the ability to induce plant resistance against pathogens, possibly through its volatile metabolites. Adequate culture media were inoculated, and its headspace was then sampled with a solid-phase microextraction fiber and chromatographed every 24 h over seven days. The raw chromatogram processing using multiway principal component analysis allowed the determination of the inoculation period, during which the concentration of volatile metabolites was maximized, as well as the discrimination of the appropriate peaks from the complex culture media background. Several volatile metabolites not previously described in the literature on biocontrol fungi were observed, as well as sesquiterpenes and aliphatic alcohols. These results stress that, due to the complexity of multidimensional chromatographic data, multivariate tools might be mandatory even for apparently trivial tasks, such as the determination of the temporal profile of metabolite production and extinction. However, when compared with conventional gas chromatography, the complex data processing yields a considerable improvement in the information obtained from the samples.

In situ drug and metabolite analysis [corrected] in biological and clinical research by MALDI MS imaging

In recent years the analysis in mass spectrometry (MS) [corrected] imaging has been expanded to detect a wide variety of low molecular weight compounds (LMWC), including exogenous and endogenous compounds. The high sensitivity and selectivity of MS imaging combined with visualization of molecular spatial distribution in tissues, makes it a valuable [corrected] platform in targeted drug and untargeted metabolomic analysis [corrected] in biological and clinical research. Here, we review the current and potential applications of MALDI MS imaging in these areas. The aim of advancing MALDI MS imaging in the field of LMWC is to support clinical applications by understanding drug and drug-metabolite distribution, investigating toxicity and discovering [corrected] new biomarkers.

Metabolomics insights into pathophysiological mechanisms of interstitial cystitis

Interstitial cystitis (IC), also known as painful bladder syndrome or bladder pain syndrome, is a chronic lower urinary tract syndrome characterized by pelvic pain, urinary urgency, and increased urinary frequency in the absence of bacterial infection or identifiable clinicopathology. IC can lead to long-term adverse effects on the patient's quality of life. Therefore, early diagnosis and better understanding of the mechanisms underlying IC are needed. Metabolomic studies of biofluids have become a powerful method for assessing disease mechanisms and biomarker discovery, which potentially address these important clinical needs. However, limited intensive metabolic profiles have been elucidated in IC. The article is a short review on metabolomic analyses that provide a unique fingerprint of IC with a focus on its use in determining a potential diagnostic biomarker associated with symptoms, a response predictor of therapy, and a prognostic marker.

1H-NMR metabolomic biomarkers of poor outcome after hemorrhagic shock are absent in hibernators

Background

Hemorrhagic shock (HS) following trauma is a leading cause of death among persons under the age of 40. During HS the body undergoes systemic warm ischemia followed by reperfusion during medical intervention. Ischemia/reperfusion (I/R) results in a disruption of cellular metabolic processes that ultimately lead to tissue and organ dysfunction or failure. Resistance to I/R injury is a characteristic of hibernating mammals. The present study sought to identify circulating metabolites in the rat as biomarkers for metabolic alterations associated with poor outcome after HS. Arctic ground squirrels (AGS), a hibernating species that resists I/R injury independent of decreased body temperature (warm I/R), was used as a negative control.

Methodology/principal findings

Male Sprague-Dawley rats and AGS were subject to HS by withdrawing blood to a mean arterial pressure (MAP) of 35 mmHg and maintaining the low MAP for 20 min before reperfusing with Ringers. The animals’ temperature was maintained at 37±0.5°C for the duration of the experiment. Plasma samples were taken immediately before hemorrhage and three hours after reperfusion. Hydrophilic and lipid metabolites from plasma were then analyzed via ¹H–NMR from unprocessed plasma and lipid extracts, respectively. Rats, susceptible to I/R injury, had a qualitative shift in their hydrophilic metabolic fingerprint including differential activation of glucose and anaerobic metabolism and had alterations in several metabolites during I/R indicative of metabolic adjustments and organ damage. In contrast, I/R injury resistant AGS, regardless of season or body temperature, maintained a stable metabolic homeostasis revealed by a qualitative ¹H–NMR metabolic profile with few changes in quantified metabolites during HS-induced global I/R.

Conclusions/significance

An increase in circulating metabolites indicative of anaerobic metabolism and activation of glycolytic pathways is associated with poor prognosis after HS in rats. These same biomarkers are absent in AGS after HS with warm I/R.

Pushing CT and MR imaging to the molecular level for studying the "omics": current challenges and advancements

During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.

Biological markers in noninvasive brain stimulation trials in major depressive disorder: a systematic review

OBJECTIVES

The therapeutic effects of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation in patients with major depression have shown promising results; however, there is a lack of mechanistic studies using biological markers (BMs) as an outcome. Therefore, our aim was to review noninvasive brain stimulation trials in depression using BMs.

METHODS

The following databases were used for our systematic review: MEDLINE, Web of Science, Cochrane, and SCIELO. We examined articles published before November 2012 that used TMS and transcranial direct current stimulation as an intervention for depression and had BM as an outcome measure. The search was limited to human studies written in English.

RESULTS

Of 1234 potential articles, 52 articles were included. Only studies using TMS were found. Biological markers included immune and endocrine serum markers, neuroimaging techniques, and electrophysiological outcomes. In 12 articles (21.4%), end point BM measurements were not significantly associated with clinical outcomes. All studies reached significant results in the main clinical rating scales. Biological marker outcomes were used as predictors of response, to understand mechanisms of TMS, and as a surrogate of safety.

CONCLUSIONS

Functional magnetic resonance imaging, single-photon emission computed tomography, positron emission tomography, magnetic resonance spectroscopy, cortical excitability, and brain-derived neurotrophic factor consistently showed positive results. Brain-derived neurotrophic factor was the best predictor of patients' likeliness to respond. These initial results are promising; however, all studies investigating BMs are small, used heterogeneous samples, and did not take into account confounders such as age, sex, or family history. Based on our findings, we recommend further studies to validate BMs in noninvasive brain stimulation trials in MDD.

Signal intensities derived from different NMR probes and parameters contribute to variations in quantification of metabolites

We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-¹H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data.

Monitoring response to cytostatic cisplatin in a HER2(+) ovary cancer model by MRI and in vitro and in vivo MR spectroscopy

Background:

Limited knowledge is available on alterations induced by cytostatic drugs on magnetic resonance spectroscopy (MRS) and imaging (MRI) parameters of human cancers, in absence of apoptosis or cytotoxicity. We here investigated the effects of a cytostatic cisplatin (CDDP) treatment on ¹H MRS and MRI of HER2-overexpressing epithelial ovarian cancer (EOC) cells and in vivo xenografts.

Methods:

High-resolution MRS analyses were performed on in vivo passaged SKOV3.ip cells and cell/tissue extracts (16.4 or 9.4 T). In vivo MRI/MRS quantitative analyses (4.7 T) were conducted on xenografts obtained by subcutaneous implantation of SKOV3.ip cells in SCID mice. The apparent diffusion coefficient (ADC) and metabolite levels were measured.

Results:

CDDP-induced cytostatic effects were associated with a metabolic shift of cancer cells towards accumulation of MRS-detected neutral lipids, whereas the total choline profile failed to be perturbed in both cultured cells and xenografts. In vivo MRI examinations showed delayed tumour growth in the CDDP-treated group, associated with early reduction of the ADC mean value.

Conclusion:

This study provides an integrated set of information on cancer metabolism and physiology for monitoring the response of an EOC model to a cytostatic chemotherapy, as a basis for improving the interpretation of non-invasive MR examinations of EOC patients.

Volume determination with two standards allows absolute quantification and improved chemometric analysis of metabolites by NMR from submicroliter samples

The accurate measurement of metabolite concentrations in miniscule biological sample volumes is often desirable, yet it remains challenging. In many cases, the starting analyte volumes are imprecisely known, or not directly measurable, and hence absolute metabolite concentrations are difficult to calculate. Here, we introduce volume determination using two standards (VDTS) as a general quantitative method for the analysis of polar metabolites in submicrolitre samples using ¹H NMR spectroscopy. This approach permits the back calculation of absolute metabolite concentrations from small biological samples of unknown volume. Where small sample volumes are also variable, VDTS can improve multivariate chemometric analysis. In this context, principal component analysis (PCA) yielded more logically consistent and biologically insightful outputs when we used volume-corrected spectra, calculated using VDTS, rather than probabilistic quotient normalization (PQN) of raw spectra. As proof-of-principle, the VDTS-based method and PCA were used to analyze polar metabolites in the hemolymph (blood) extracted from larvae of the very small but widely used genetic model organism Drosophila. This analysis showed that the hemolymph metabolomes of males and females are markedly different when larvae are well fed. However, gender-specific metabolomes tend to converge when larval dietary nutrients are restricted. We discuss the biological implications of these surprising results and compare and contrast them to previous analyses of Drosophila hemolymph and mammalian blood plasma. Together, these findings reveal an interesting and hitherto unknown sexual dimorphism in systemic Drosophila metabolites, clearly warranting further biological investigation. Importantly, the VDTS approach should be adaptable to many different analytical platforms, including mass spectrometry.

Two birds with one stone: doing metabolomics with your proteomics kit

Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘-OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC-MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics-based studies. Limitations and requirements are discussed as well as extensions to the LC-MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC-MS, CE-MS or NMR.

Universal quantitative NMR analysis of complex natural samples

Nuclear Magnetic Resonance (NMR) is a universal and quantitative analytical technique. Being a unique structural tool, NMR also competes with metrological techniques for purity determination and reference material analysis. In pharmaceutical research, applications of quantitative NMR (qNMR) cover mostly the identification and quantification of drug and biological metabolites. Offering an unbiased view of the sample composition, and the possibility to simultaneously quantify multiple compounds, qNMR has become the method of choice for metabolomic studies and quality control of complex natural samples such as foods, plants or herbal remedies, and biofluids. In this regard, NMR-based metabolomic studies, dedicated to both the characterization of herbal remedies and clinical diagnosis, have increased considerably.

A comprehensive workflow of mass spectrometry-based untargeted metabolomics in cancer metabolic biomarker discovery using human plasma and urine

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC-LC), reversed-phase liquid chromatography (RP-LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

Bioanalysis of drug in tissue: current status and challenges

Distribution of drugs into tissues is an important determinant of the overall PK and PD profile. Thus, bioanalysis of drugs and their metabolites in tissues can play an important role in understanding the pharmacological and toxicological properties of new drug candidates. Unlike liquid matrices, bioanalysis in tissues offers unique challenges such as proper tissue sampling, appropriate tissue sample preparation, efficient extraction of the analytes from the tissue homogenates, and demonstration of stability and recovery of analytes in intact tissues. This article provides a systematic review of tissue sample analysis for small molecules using LC–MS/MS. The authors provide rationale for tissue sample analysis, and discuss strategies for method development, method qualification or validation, and sample analysis. Unique aspects of method development and qualification/validation are highlighted based on authors’ direct experiences and literature summary. Analysis using intact tissue samples such as MALDI imaging is also briefly discussed.