Normalization strategies for metabonomic analysis of urine samples

Unlike plasma and most biological fluids which have solute concentrations that are tightly controlled, urine volume can vary widely based upon water consumption and other physiological factors. As a result, the concentrations of endogenous metabolites in urine vary widely and normalizing for these effects is necessary. Normalization approaches that utilized urine volume, osmolality, creatinine concentration, and components that are common to all samples (“total useful MS signal”) were compared in order to determine which strategies could be successfully used to differentiate between dose groups based upon the complete endogenous metabolite profile. Variability observed in LC/MS results obtained from targeted and non-targeted metabonomic analyses was highly dependent on the strategy used for normalization. We therefore recommend the use of two different normalization techniques in order to facilitate detection of statistically significant changes in the endogenous metabolite profile when working with urine samples.     

A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography–mass spectrometry (GC–MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC–MS. In total, 235 GC–MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples (1) had the same or lower coefficients of variance among reproducibly detected metabolites, (2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and (3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pre-treatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pre-treatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.     

Mealtime,Temporal, and Daily Variability of the Human Urinary and Plasma Metabolomes in a Tightly Controlled Environment

While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by “fast lane” LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.     

Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry

The analysis of urine by direct infusion mass spectrometry suffers from ion suppression due to its high salt content and inter-sample variability caused by the differences in urine volume between persons. Thus, urine metabolomics requires a careful selection of the sample preparation procedure and a normalization strategy to deal with these problems. Several approaches were tested for metabolomic analysis of urine samples by direct infusion electrospray mass spectrometry (DI–ESI–MS), including solid phase extraction, liquid–liquid extraction, and sample dilution. In addition, normalization of results based on conductivity values and statistical treatment was performed to minimize sample variability. Both urine dilution and solid phase extraction with mixed mode sorbent considerably reduced the salt content in urine, providing comprehensive metabolomic fingerprints. Moreover, statistical data normalization enabled the correction of inter-sample physiological variability, improving the quality of results obtained. Therefore, high-throughput DI–ESI–MS fingerprinting of urine samples can be achieved with simple pretreatment procedures allowing the use of this noninvasive sampling in metabolomics. Finally, the optimized approach was tested in a pilot metabolomic investigation of urine samples from transgenic mice models of Alzheimer’s disease (APP/PS1) in order to illustrate the potential of the methodology.     

Metabolomics unveils urinary changes in subjects with metabolic syndrome following 12-week nut consumption

Through an HPLC-Q-TOF-MS-driven nontargeted metabolomics approach, we aimed to discriminate changes in the urinary metabolome of subjects with metabolic syndrome (MetS), following 12 weeks of mixed nuts consumption (30 g/day), compared to sex- and age-matched individuals given a control diet. The urinary metabolome corresponding to the nut-enriched diet clearly clustered in a distinct group, and the multivariate data analysis discriminated relevant mass features in this separation. Metabolites corresponding to the discriminating ions (MS features) were then subjected to multiple tandem mass spectrometry experiments using LC-ITD-FT-MS, to confirm their putative identification. The metabolomics approach revealed 20 potential markers of nut intake, including fatty acid conjugated metabolites, phase II and microbial-derived phenolic metabolites, and serotonin metabolites. An increased excretion of serotonin metabolites was associated for the first time with nut consumption. Additionally, the detection of urinary markers of gut microbial and phase II metabolism of nut polyphenols confirmed the understanding of their bioavailability and bioactivity as a priority area of research in the determination of the health effects derived from nut consumption. The results confirmed how a nontargeted metabolomics strategy may help to access unexplored metabolic pathways impacted by diet, thereby raising prospects for new intervention targets.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

植物代谢组学的研究方法及其应用

代谢组是指某一生物或细胞在一特定生理时期内所有的低分子量代谢产物。植物代谢组学是指对植物抽提物中代谢组进行高通量、无偏差全面分析的技术。近年来, 植物代谢组学研究取得了很大进展。本文介绍了其含义、历史沿革及研究方法, 并用典型实例阐释了它的应用方向。     

植物代谢组学的研究方法及其应用

代谢组是指某一生物或细胞在一特定生理时期内所有的低分子量代谢产物.植物代谢组学是指对植物抽提物中代谢组进行高通量、无偏差全面分析的技术.近年来,植物代谢组学研究取得了很大进展.本文介绍了其含义、历史沿革及研究方法,并用典型实例阐释了它的应用方向.     

Mass spectrometry strategies in metabolomics

MS has evolved as a critical component in metabolomics, which seeks to answer biological questions through large-scale qualitative and quantitative analyses of the metabolome. MS-based metabolomics techniques offer an excellent combination of sensitivity and selectivity, and they have become an indispensable platform in biology and metabolomics. In this minireview, various MS technologies used in metabolomics are briefly discussed, and future needs are suggested.     

Metabolomic and elemental analysis of camel and bovine urine by GC–MS and ICP–MS

Recent studies from the author’s laboratory indicated that camel urine possesses antiplatelet activity and anti-cancer activity which is not present in bovine urine. The objective of this study is to compare the volatile and elemental components of bovine and camel urine using GC–MS and ICP–MS analysis. We are interested to know the component that performs these biological activities. The freeze dried urine was dissolved in dichloromethane and then derivatization process followed by using BSTFA for GC–MS analysis. Thirty different compounds were analyzed by the derivatization process in full scan mode. For ICP–MS analysis twenty eight important elements were analyzed in both bovine and camel urine. The results of GC–MS and ICP–MS analysis showed marked difference in the urinary metabolites. GC–MS evaluation of camel urine finds a lot of products of metabolism like benzene propanoic acid derivatives, fatty acid derivatives, amino acid derivatives, sugars, prostaglandins and canavanine. Several research reports reveal the metabolomics studies on camel urine but none of them completely reported the pharmacology related metabolomics. The present data of GC–MS suggest and support the previous studies and activities related to camel urine.     

代谢物组学方法及其在植物学研究中的应用

代谢物是生物体受遗传控制和环境影响的最终表达产物,以全体代谢物(代谢物组)为研究对象的代谢物组学是继基因组学和蛋白质组学后必然出现的又一门"组学"技术。该文综述了代谢物组的检测、数据的处理和分析等以及这些技术在植物目标分析、基因功能、代谢途径和代谢工程、整合植物学、信号转导等研究中的应用和前景。     

Metabolomics Data Normalization with EigenMS

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.     

Normalization techniques for PARAFAC modeling of urine metabolomic data

Introduction

One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.

Objectives

Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.

Methods

In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.

Results

We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.

Conclusion

For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

     

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants.     

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

Introduction

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

Objective

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

Methods

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n?=?25) or COPD (n?=?26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

Results

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q²?=?0.919, 0.705 vs R²Q²?=?0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

Conclusion

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

     

Noninvasive human metabolome analysis for differential diagnosis of inborn errors of metabolism

Early diagnosis and treatment are critical for patients with inborn errors of metabolism (IEMs). For most IEMs, the clinical presentations are variable and nonspecific, and routine laboratory tests do not indicate the etiology of the disease. A diagnostic procedure using highly sensitive gas chromatography-mass spectrometric urine metabolome analysis is useful for screening and chemical diagnosis of IEM. Metabolite analysis can comprehensively detect enzyme dysfunction caused by a variety of abnormalities. The mutations may be uncommon or unknown. The lack of coenzymes or activators and the presence of post-translational modification defects and subcellular localization abnormalities are also reflected in the metabolome. This noninvasive and feasible urine metabolome analysis, which uses urease-pretreatment, partial adoption of stable isotope dilution, and GC/MS, can be used to detect more than 130 metabolic disorders. It can also detect an acquired abnormal metabolic profile. The metabolic profiles for two cases of non-inherited phenylketonuria are shown. In this review, chemical diagnoses of hyperphenylalaninemia, phenylketonuria, hyperprolinemia, and lactic acidemia, and the differential diagnosis of beta-ureidopropionase deficiency and primary hyperammonemias including ornithine transcarbamylase deficiency and carbamoylphosphate synthetase deficiency are described.     

Urinary and hematologic indexes of hypohydration

As part of a large-scale field feeding system test we were able to collect and study hundreds of aliquots of overnight urine samples obtained immediately prior to a fasting blood sample on days 1, 20, and 44 of the field test. The large number of experimental samples (greater than 650) and concomitant collection of blood and urine aliquots along with data on body weights gave us the opportunity to assess and quantitate the sensitivity of commonly used criteria of hypohydration. Urine aliquots for all test days were initially categorized by specific gravity (SG) greater than or equal to 1.03 (n = 124) or less than 1.03 (n = 540). Creatinine levels were elevated (P less than 0.001) in the concentrated urine samples, but a decreased trend in sodium-to-potassium ratios in these samples failed to achieve statistical significance (P greater than 0.05). However, when individuals with high SG urine were subclassified by a criterion of weight loss greater than 3% from original body weight, then creatinine concentrations were elevated (P = 0.05), whereas sodium-to-potassium ratios were decreased (P = 0.05) when subjects also with high SG but weight loss less than 3% were compared. Because of the moderate altitude (2,000 m) of the field site and the time of sojourn (44 days), there occurred a slight, but significant (P less than 0.001), erythropoietic response. Hematocrit and serum osmolality were not significantly different when examined by the criteria of high or low SG urine and weight loss greater than or less than 3% original body weight.(ABSTRACT TRUNCATED AT 250 WORDS)     

Mass spectrometry for the identification of the discriminating signals from metabolomics: current status and future trends

The metabolome is characterized by a large number of molecules exhibiting a high diversity of chemical structures and abundances, requiring complementary analytical platforms to reach its extensive coverage. Among them, atmospheric pressure ionization mass spectrometry (API-MS)-based technologies, and especially those using electrospray ionization are now very popular. In this context, this review deals with strengths, limitations and future trends in the identification of signals highlighted by API-MS-based metabolomics. It covers the identification process from the determination of the molecular mass and/or its elemental composition to the confirmation of structural hypotheses. Furthermore, some tools that were developed in order to address the MS signal redundancy and some approaches that could facilitate identification by improving the visualization and organization of complex data sets are also reported and discussed.     

New insights into cardiovascular and lipid metabolomics

Metabolomics is the study of metabolite profiles in biological samples, particularly urine, saliva, blood plasma and fat biopsies. The metabolome is now considered by some to be the most predictive phenotype: consequently, the comprehensive and quantitative study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets and enabling appropriate treatments. A wealth of information about metabolites has been accumulated with global profiling tools and several candidate technologies for metabolomic studies are now available. Many high-throughput metabolomics methodologies are currently under development and have yet to be applied in clinical practice on a routine basis. In the cardiovascular field, few recent metabolomic studies have been reported so far. This minireview provides an updated overview of alternative technical approaches for metabolomics studies and reviews initial applications of metabolomics that relate to both cardiovascular disease and lipid metabolism research.