AMDORAP: non-targeted metabolic profiling based on high-resolution LC-MS

Effect of acute high-intensity exercise on myocardium metabolic profiles in rat and human study via metabolomics approach

Acute high-intensity exercise can affect cardiac health by altering substance metabolism. However, few metabolomics-based studies provide data on the effect of exercise along with myocardial metabolism. Our study aimed to identify metabolic signatures in rat myocardium during acute high-intensity exercise and evaluate their diagnostic potential for sports injuries. We collected rat myocardium samples and subjects’ serum samples before and after acute high-intensity exercise for metabolite profiling to explore metabolic alterations of exercise response in the myocardium. Multivariate analysis revealed myocardium metabolism differed before and after acute high-intensity exercise. Furthermore, 6 target metabolic pathways and 12 potential metabolic markers for acute high-intensity exercise were identified. Our findings provided an insight that myocardium metabolism during acute high-intensity exercise had distinct disorders in complex lipids and fatty acids. Moreover, an increase of purine degradation products, as well as signs of impaired glucose metabolism, were observed. Besides, amino acids were enhanced with a certain protective effect on the myocardium. In this study, we discovered how acute high-intensity exercise affected myocardial metabolism and exercise-related heart injury risks, which can provide references for pre-competition screening, risk prevention, and disease prognosis in competitive sports and effective formulation of exercise prescriptions for different people.

Mining plant metabolomes: Methods, applications, and perspectives

Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.

Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing

With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Comparative metabolic profiling of Vitis amurensis and Vitis vinifera during cold acclimation

Vitis amurensis is a wild Vitis plant that can withstand extreme cold temperatures. However, the accumulation of metabolites during cold acclimation (CA) in V. amurensis remains largely unknown. In this study, plantlets of V. amurensis and V. vinifera cv. Muscat of Hamburg were treated at 4 °C for 24 and 72 h, and changes of metabolites in leaves were detected by gas chromatography coupled with time-of-flight mass spectrometry. Most of the identified metabolites, including carbohydrates, amino acids, and organic acids, accumulated in the two types of grape after CA. Galactinol, raffinose, fructose, mannose, glycine, and ascorbate were continuously induced by cold in V. amurensis, but not in Muscat of Hamburg. Twelve metabolites, including isoleucine, valine, proline, 2-oxoglutarate, and putrescine, increased in V. amurensis during CA. More galactinol, ascorbate, 2-oxoglutarate, and putrescine, accumulated in V. amurensis, but not in Muscat of Hamburg, during CA, which may be responsible for the excellent cold tolerance in V. amurensis. The expression levels of the genes encoding β-amylase (BAMY), galactinol synthase (GolS), and raffinose synthase (RafS) were evaluated by quantitative reverse transcription-PCR. The expression BAMY (VIT_02s0012 g00170) and RafS (VIT_05s0077 g00840) were primarily responsible for the accumulation of maltose and raffinose, respectively. The accumulation of galactinol was attributed to different members of GolS in the two grapes. In conclusion, these results show the inherent differences in metabolites between V. amurensis and V. vinifera under CA.

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

An analysis of green discoloration of urine caused by propofol infusion

BACKGROUND

Propofol is a short-acting, intravenous sedative-hypnotic agent that is widely used for the induction and maintenance of general anesthesia and sedation. An uncommon adverse effect of propofol is green discoloration of the urine, which has been reported not only under general anesthesia but also with sedation. Although it is assumed that the phenolic derivatives of propofol can cause green discoloration of the urine, the actual origin remains unknown. The aim of this report was to identify the origin of the green discoloration of the urine using liquid chromatography-mass spectrometry (LC-MS).

CLINICAL FEATURES

The patient, a 51-year-old man, was scheduled for his oral surgery under general anesthesia using propofol. Postoperatively, the color of his urine was observed to be green. We compared and analyzed both the green urine and the normal urine using LC-MS.

CONCLUSION

We experienced a case of a patient with green discoloration of the urine after general anesthesia using propofol. Although LC-MS analysis showed 2 unique peaks in the green urine at 490 and 590 nm, obvious causes were not revealed.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach

MOTIVATION

Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.

RESULTS

Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties.

AVAILABILITY AND IMPLEMENTATION

The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Sum of the parts: mass spectrometry-based metabolomics

Metabolomics is a rapidly growing field of research used in the identification and quantification of the small molecule metabolites within an organism, thereby providing insights into cell metabolism and bioenergetics as well as processes important in clinical medicine, such as disposition of pharmaceutical compounds. It offers comprehensive information about thousands of low-molecular mass compounds (<1500 Da) that represent a wide range of pathways and intermediary metabolism. Because of its vast expansion in the past two decades, mass spectrometry has become an indispensable tool in "omic" analyses. The use of different ionization techniques such as the more traditional electrospray and matrix-assisted laser desorption, as well as recently popular desorption electrospray ionization, has allowed the analysis of a wide range of biomolecules (e.g., peptides, proteins, lipids, and sugars), and their imaging and analysis in the original sample environment in a workup free fashion. An overview of the current state of the methodology is given, as well as examples of application.

Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data

Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .

Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.