Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Application of NMR and Chemometric Analyses to Better Understand the Quality Attributes in pH and Thermally Degraded Monoclonal Antibodies

PURPOSE

Nuclear magnetic resonance (NMR) spectroscopy provides the sensitivity and specificity to probe the higher order structure (HOS) of monoclonal antibodies (mAbs) for potential changes. This study demonstrates an application of chemometric tools to measure differences in the NMR spectra of mAbs after forced degradation relative to the respective unstressed starting materials.

METHODS

Samples of adalimumab (Humira, ADL-REF) and trastuzumab (Herceptin, TRA-REF) were incubated in three buffer-pH conditions at 40°C for 4 weeks to compare to a control sample that was left unstressed. Replicate 1D 1H and 2D 1H-13C HMQC NMR spectra were collected on all samples. Chemometric analyses such as Easy Comparability of HOS (ECHOS), PROtein FIngerprinting by Lineshape Enhancement (PROFILE), and Principal Component Analysis (PCA) were applied to capture and quantitate differences between the spectra.

RESULTS

Visual and statistical inspection of the 2D 1H-13C HMQC spectra of adalimumab and trastuzumab after forced degradation conditions shows no changes in the spectra relative to the unstressed material. Chemometric analysis of the 1D 1H NMR spectra shows only minor changes in the spectra of adalimumab after forced degradation, but significant differences in trastuzumab.

CONCLUSION

The chemometric analyses support the lack of statistical differences in the structure of pH-thermal stressed adalimumab, however, it reveals conformational changes or chemical modifications in trastuzumab after forced degradation. Application of chemometrics in comparative NMR studies enables HOS characterization and showcases the sensitivity and specificity in detecting differences in the spectra of mAbs after pH-thermal forced degradation with respect to local and global protein structure.

Performance comparison of three scaling algorithms in NMR-based metabolomics analysis

Unit variance (UV) scaling, mean centering (CTR) scaling, and Pareto (Par) scaling are three commonly used algorithms in the preprocessing of metabolomics data. Based on our NMR-based metabolomics studies, we found that the clustering identification performances of these three scaling methods were dramatically different as tested by the spectra data of 48 young athletes’ urine samples, spleen tissue (from mice), serum (from mice), and cell (from Staphylococcus aureus) samples. Our data suggested that for the extraction of clustering information, UV scaling could serve as a robust approach for NMR metabolomics data for the identification of clustering analysis even with the existence of technical errors. However, for the purpose of discriminative metabolite identification, UV scaling, CTR scaling, and Par scaling could equally extract discriminative metabolites efficiently based on the coefficient values. Based on the data presented in this study, we propose an optimal working pipeline for the selection of scaling algorithms in NMR-based metabolomics analysis, which has the potential to serve as guidance for junior researchers working in the NMR-based metabolomics research field.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Cost function for low-dimensional manifold topology assessment

In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Comparative analysis of erythrocyte hemolysis, plasma parameters and metabolic features in red crucian carp (Carassius auratus red var) and triploid hybrid fish following Aeromonas hydrophila challenge

Aeromonas hydrophila can pose a great threat to survival of freshwater fish. In this study, A. hydrophila challenge could promote the erythrocyte hemolysis, increase free hemoglobin (FHB) level and generate malondialdehyde (MDA) production in plasma but decrease the levels of total antioxidant capacity (T-AOC), total superoxide dismutase (SOD), catalase (CAT), alkaline phosphatase (ALP) and lysozyme (LZM) of red crucian carp (RCC, 2 N = 100) and triploid hybrid fish (3 N fish, 3 N = 150) following A. hydrophila challenge. Elevated expression levels of heat shock protein 90 alpha (HSP90α), matrix metalloproteinase 9 (MMP-9), free fatty acid receptor 3 (FFAR3), paraoxonase 2 (PON2) and cytosolic phospholipase A2 (cPLA2) were observed in A. hydrophila-infected fish. In addition, A. hydrophila challenge could significantly increase expressions of cortisol, leucine, isoleucine, glutamate and polyunsaturated fatty acids (PUFAs) in RCC and 3 N, while glycolysis and tricarboxylic acid cycle appeared to be inactive. We identified differential fatty acid derivatives and their metabolic networks as crucial biomarkers from metabolic profiles of different ploidy cyprinid fish subjected to A. hydrophila infection. These results highlighted the comparative metabolic strategy of different ploidy cyprinid fish against bacterial infection.

Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics

Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

INTRODUCTION

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

OBJECTIVE

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

METHODS

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.

RESULTS

The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.

CONCLUSION

The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.

A data-driven approach to generate pseudo-reaction sequences for the thermal conversion of Athabasca bitumen

We use self-modeling multivariate curve resolution to identify pseudo-components and chemical transformations in thermal conversion of Athabasca bitumen.

Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Metabolomic and lipidomic plasma profile changes in human participants ascending to Everest Base Camp

At high altitude oxygen delivery to the tissues is impaired leading to oxygen insufficiency (hypoxia). Acclimatisation requires adjustment to tissue metabolism, the details of which remain incompletely understood. Here, metabolic responses to progressive environmental hypoxia were assessed through metabolomic and lipidomic profiling of human plasma taken from 198 human participants before and during an ascent to Everest Base Camp (5,300 m). Aqueous and lipid fractions of plasma were separated and analysed using proton (1H)-nuclear magnetic resonance spectroscopy and direct infusion mass spectrometry, respectively. Bayesian robust hierarchical regression revealed decreasing isoleucine with ascent alongside increasing lactate and decreasing glucose, which may point towards increased glycolytic rate. Changes in the lipid profile with ascent included a decrease in triglycerides (48-50 carbons) associated with de novo lipogenesis, alongside increases in circulating levels of the most abundant free fatty acids (palmitic, linoleic and oleic acids). Together, this may be indicative of fat store mobilisation. This study provides the first broad metabolomic account of progressive exposure to environmental hypobaric hypoxia in healthy humans. Decreased isoleucine is of particular interest as a potential contributor to muscle catabolism observed with exposure to hypoxia at altitude. Substantial changes in lipid metabolism may represent important metabolic responses to sub-acute exposure to environmental hypoxia.

Neurochemistry of Anesthetic States

Anesthetic mechanisms that eliminate consciousness and perception of pain are products of the nervous system. Chemical approaches to the study of anesthetic mechanisms have the potential to serve as an ideal interface between basic and clinical neuroscience. There are disproportionately more basic neurochemical studies than clinical studies of anesthetic mechanisms. Even within neuroscience, the study of anesthetic mechanisms is sparse. The Society for Neuroscience hosts one of the world's largest and most vibrant scientific meetings, yet the content themes of that meeting do not include anesthesia. One goal of this chapter is to facilitate neurochemical studies of anesthetic mechanisms by outlining user-friendly descriptions of existing and emerging techniques. The introduction provides a context for chapter goals. The second portion of this chapter focuses on microdialysis methods that enable the humane acquisition of neurochemical samples from intact, behaving animals during anesthetic induction, maintenance, and emergence. No single neurotransmitter and no single brain region regulate the physiological and behavioral traits characteristic of any anesthetic state. This limitation is being addressed via application of new instrumentation and techniques in analytic chemistry. The final third of this chapter highlights selected omics approaches that are now being applied to the neurochemical study of anesthetic mechanisms. We hope that this brief chapter can stimulate basic and clinical metabolomic approaches aiming to elucidate the mechanisms of anesthetic action.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine

1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.

NMR-based metabolomic study on the toxicological effects of pesticide, diazinon on adaptation to sea water by endangered Persian sturgeon, Acipenser persicus fingerlings

NMR-based metabolomics was applied to explore metabolic impacts of diazinon on sea water adaptation of Persian sturgeon fingerlings, Acipenser persicus. Fingerlings were exposed to sub-lethal concentrations of diazinon in freshwater (FW) for 96 h (short-term trial) and 12 days (long-term trial) and then exposed in brackish water (BW) (12 mg L^-1 salinity) for 24 h. After 96 h and 12 days exposure in FW, identified metabolites (amino acids, osmolytes, energy metabolites) showed different change-patterns compared to control group (P < 0.05) as follow: (A) short-term trial: higher plasma levels of glucose, lactate (in all diazinon-exposed fish), acetate and acetoacetate (in 0.9 mg L^-1diazinon treatment); lower levels of creatine (in all diazinon-exposed fish), trimethylamine-N-oxide, choline, taurine, betaine, N,N-dimethylglycine and almost all amino acids in fish exposed to high concentrations of diazinon (0.54 and 0.9 mg L^-1 diazinon). (B) Long-term trial: higher plasma levels of lipid oxidation metabolites and almost all amino acids in fish exposed to 0.54 and 0.9 mg L^-1 diazinon; lower levels of creatine, trimethylamine-N-oxide, N,N-dimethylglycine, betaine, choline (in all diazinon-exposed fish), glucose (in 0.54 and 0.9 mg L^-1diazinon treatments) and taurine (in 0.9 mg L^-1 diazinon treatment). When fish were exposed in BW for 24 h, the plasma levels of osmolytes decreased significantly in almost all experimental groups of short-term and long-term trial (P < 0.05). In short-term trial, the plasma levels of glucose in all groups and lactate in 0.18 and 0.54 mg L^-1 diazinon treatments increased after salinity challenge (P < 0.05). However, a significant decrease was observed in lactate levels in 0.9 mg L^-1 diazinon treatment (P < 0.05). Also, the plasma levels of amino acids decreased mostly in fish of control group than exposed fish (P < 0.05). The plasma glycerol concentration showed a significant decrease only in fish of 0.54 mg L^-1 diazinon treatment (P < 0.05). In long term trial, the energetic metabolites (acetate, acetoacetate, glycerol) showed significant increases mostly in fish exposed to high concentrations of diazinon (P < 0.05). Phosphocreatine was detected only in groups exposed to 0.54 and 0.9 mg L^-1 diazinon. Some amino acids decreased in control and diazinon-exposed groups while glycine (in control and 0.18 mg L^-1 diazinon treatment), glutamine and alanine (in 0.9 mg L^-1 diazinon treatment) elevated significantly after 24 h acclimation in BW (P < 0.05). Our results may help to understand the effects of pesticides on fish osmoregulation from a metabolic approach.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren's Syndrome and Systemic Sclerosis

Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diagnose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

INTRODUCTION

The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.

OBJECTIVES

Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.

METHODS

We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.

RESULTS

Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.

CONCLUSION

The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis

OBJECTIVE

While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API.

MATERIALS AND METHODS

Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data.

RESULTS

Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength.

CONCLUSIONS

Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.

Multiplexed Component Analysis to Identify Genes Contributing to the Immune Response during Acute SIV Infection

Immune response genes play an important role during acute HIV and SIV infection. Using an SIV macaque model of AIDS and CNS disease, our overall goal was to assess how the expression of genes associated with immune and inflammatory responses are longitudinally changed in different organs or cells during SIV infection. To compare RNA expression of a panel of 88 immune-related genes across time points and among three tissues – spleen, mesenteric lymph nodes (MLN) and peripheral blood mononuclear cells (PBMC) – we designed a set of Nanostring probes. To identify significant genes during acute SIV infection and to investigate whether these genes are tissue-specific or have global roles, we introduce a novel multiplexed component analysis (MCA) method. This combines multivariate analysis methods with multiple preprocessing methods to create a set of 12 “judges”; each judge emphasizes particular types of change in gene expression to which cells could respond, for example, the absolute or relative size of expression change from baseline. Compared to bivariate analysis methods, our MCA method improved classification rates. This analysis allows us to identify three categories of genes: (a) consensus genes likely to contribute highly to the immune response; (b) genes that would contribute highly to the immune response only if certain assumptions are met – e.g. that the cell responds to relative expression change rather than absolute expression change; and (c) genes whose contribution to immune response appears to be modest. We then compared the results across the three tissues of interest; some genes are consistently highly-contributing in all tissues, while others are specific for certain tissues. Our analysis identified CCL8, CXCL10, CXCL11, MxA, OAS2, and OAS1 as top contributing genes, all of which are stimulated by type I interferon. This suggests that the cytokine storm during acute SIV infection is a systemic innate immune response against viral replication. Furthermore, these genes have approximately equal contributions to all tissues, making them possible candidates to be used as non-invasive biomarkers in studying PBMCs instead of MLN and spleen during acute SIV infection experiments. We identified clusters of genes that co-vary together and studied their correlation with regard to other gene clusters. We also developed novel methods to faithfully visualize multi-gene correlations on two-dimensional polar plots, and to visualize tissue specificity of gene expression responses.

A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis

Highlights

Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.

A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement.

Applying the above strategy, several low abundant masked differential metabolites were rescued.

Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

Major evolutionary trends in hydrogen isotope fractionation of vascular plant leaf waxes

Hydrogen isotopic ratios of terrestrial plant leaf waxes (δD) have been widely used for paleoclimate reconstructions. However, underlying controls for the observed large variations in leaf wax δD values in different terrestrial vascular plants are still poorly understood, hampering quantitative paleoclimate interpretation. Here we report plant leaf wax and source water δD values from 102 plant species grown in a common environment (New York Botanic Garden), chosen to represent all the major lineages of terrestrial vascular plants and multiple origins of common plant growth forms. We found that leaf wax hydrogen isotope fractionation relative to plant source water is best explained by membership in particular lineages, rather than by growth forms as previously suggested. Monocots, and in particular one clade of grasses, display consistently greater hydrogen isotopic fractionation than all other vascular plants, whereas lycopods, representing the earlier-diverging vascular plant lineage, display the smallest fractionation. Data from greenhouse experiments and field samples suggest that the changing leaf wax hydrogen isotopic fractionation in different terrestrial vascular plants may be related to different strategies in allocating photosynthetic substrates for metabolic and biosynthetic functions, and potential leaf water isotopic differences.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis

BACKGROUND

Asthma is a chronic inflammatory disease caused by complex interactions of genetic, epigenetic, and environmental factors. For this reason, new approaches are required to clarify the pathogenesis of asthma by systemic review.

OBJECTIVE

We applied a (1)H-NMR metabolomics approach to investigate the altered metabolic pattern in sera from patients with asthma and sought to identify the mechanism underlying asthma and potential biomarkers.

METHOD

A global profile of sera from patients with asthma (n = 39) and controls (n = 26) was generated using (1)H-NMR spectroscopy coupled with multivariate statistical analysis. Endogenous metabolites in serum were rapidly measured using the target-profiling procedure.

RESULTS

Multivariate statistical analysis showed a clear distinction between patients with asthma and healthy subjects. Sera of asthma patients were characterized by increased levels of methionine, glutamine, and histidine and by decreased levels of formate, methanol, acetate, choline, O-phosphocholine, arginine, and glucose. The metabolites detected in the sera of patients with asthma are involved in hypermethylation, response to hypoxia, and immune reaction. Furthermore, the levels of serum metabolites from patients with asthma correlated with asthma severity; in particular, lipid metabolism was altered in patients with lower forced expiratory volume in 1 s percentage (FEV(1)%) predicted values. In addition, potential biomarkers showed strong predictive power in ROC analysis, and the presence of asthma in external validation models was predicted with high accuracy (90.9% for asthma and 100% for control subjects).

CONCLUSION & CLINICAL RELEVANCE

These data showed that (1)H-NMR-based metabolite profiling of serum may be useful for the effective diagnosis of asthma and a further understanding of its pathogenesis.

Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Lactate and glycine-potential MR biomarkers of prognosis in estrogen receptor-positive breast cancers

Breast cancer is a heterogeneous disease with a variable prognosis. Clinical factors provide some information about the prognosis of patients with breast cancer; however, there is a need for additional information to stratify patients for improved and more individualized treatment. The aim of this study was to examine the relationship between the metabolite profiles of breast cancer tissue and 5-year survival. Biopsies from breast cancer patients (n=98) were excised during surgery and analyzed by high-resolution magic angle spinning MRS. The data were analyzed by multivariate principal component analysis and partial least-squares discriminant analysis, and the findings of important metabolites were confirmed by spectral integration of the metabolite peaks. Predictions of 5-year survival using metabolite profiles were compared with predictions using clinical parameters. Based on the metabolite profiles, patients with estrogen receptor (ER)-positive breast cancer (n=71) were separated into two groups with significantly different survival rates (p=0.024). Higher levels of glycine and lactate were found to be associated with lower survival rates by both multivariate analyses and spectral integration, and are suggested as biomarkers for breast cancer prognosis. Similar metabolic differences were not observed for ER-negative patients, where survivors could not be separated from nonsurvivors. Predictions of 5-year survival of ER-positive patients using metabolite profiles gave better and more robust results than those using traditional clinical parameters. The results imply that the metabolic state of a tumor may provide additional information concerning breast cancer prognosis. Further studies should be conducted in order to evaluate the role of MR metabolomics as an additional clinical tool for determining the prognosis of patients with breast cancer.

Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection

Background

Acupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use.

Results

In this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints.

Conclusions

Our result demonstrates that metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture. Comparing with other existing methods, LPFS shows better performance to select a small set of key molecules. In addition, LPFS is a general methodology and can be applied to other high-dimensional data analysis, for example cancer genomics.

A mitochondria-dependent pathway mediates the apoptosis of GSE-induced yeast

Grapefruit seed extract (GSE), which has powerful anti-fungal activity, can induce apoptosis in S. cerevisiae. The yeast cells underwent apoptosis as determined by testing for apoptotic markers of DNA cleavage and typical chromatin condensation by Terminal Deoxynucleotidyl Transferase-mediated dUTP Nick End Labeling (TUNEL) and 4,6'-diaminidino-2-phenylindole (DAPI) staining and electron microscopy. The changes of ΔΨmt (mitochondrial transmembrane potential) and ROS (reactive oxygen species) indicated that the mitochondria took part in the apoptotic process. Changes in this process detected by metabonomics and proteomics revealed that the yeast cells tenaciously resisted adversity. Proteins related to redox, cellular structure, membrane, energy and DNA repair were significantly increased. In this study, the relative changes in the levels of proteins and metabolites showed the tenacious resistance of yeast cells. However, GSE induced apoptosis in the yeast cells by destruction of the mitochondrial 60 S ribosomal protein, L14-A, and prevented the conversion of pantothenic acid to coenzyme A (CoA). The relationship between the proteins and metabolites was analyzed by orthogonal projections to latent structures (OPLS). We found that the changes of the metabolites and the protein changes had relevant consistency.

Cold tolerance and freeze-induced glucose accumulation in three terrestrial slugs

Cold tolerance and metabolic responses to freezing of three slug species common in Scandinavia (Arion ater, Arion rufus and Arion lusitanicus) are reported. Autumn collected slugs were cold acclimated in the laboratory and subjected to freezing conditions simulating likely winter temperatures in their habitat. Slugs spontaneously froze at about -4 °C when cooled under dry conditions, but freezing of body fluids was readily induced at -1 °C when in contact with external ice crystals. All three species survived freezing for 2 days at -1 °C, and some A. rufus and A. lusitanicus also survived freezing at -2 °C. (1)H NMR spectroscopy revealed that freezing of body fluids resulted in accumulation of lactate, succinate and glucose. Accumulation of lactate and succinate indicates that ATP production occurred via fermentative pathways, which is likely a result of oxygen depletion in frozen tissues. Glucose increased from about 6 to 22 μg/mg dry tissue upon freezing in A. rufus, but less so in A. ater and A. lusitanicus. Glucose may thus act as a cryoprotectant in these slugs, although the concentrations are not as high as reported for other freeze tolerant invertebrates.