Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Trackable and scalable LC-MS metabolomics data processing using asari.

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination.

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data.

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.

Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data.

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.

High-Throughput Production of Diverse Xenobiotic Metabolites with Cytochrome P450-Transduced Huh7 Hepatoma Cell Lines.

Precision medicine and exposomics require methods to assess xenobiotic metabolism in human metabolomic analyses, including the identification of known and undocumented drug and chemical exposures as well as their metabolites. Recent work demonstrated the use of high-throughput generation of xenobiotic metabolites with human liver S-9 fractions for their detection in human plasma and urine. Here, we tested whether a panel of lentivirally transduced human hepatoma cell lines (Huh7) that express individual cytochrome P450 (P450) enzymes could be used to generate P450-specific metabolites in a high-throughput manner, while simultaneously identifying the enzymes responsible. Cell-line activities were verified using P450-specific probe substrates. To increase analytical throughput, we used a pooling strategy where 36 chemicals were grouped into 12 unique mixtures, each mixture containing 6 randomly selected compounds, and each compound being present in two separate mixtures. Each mixture was incubated with 8 different P450 cell lines for 0 and 2 hours and extracts were analyzed using liquid chromatography-high-resolution mass spectrometry. Cell lines selectively metabolized test substrates, e.g., pazopanib, bupropion, and ß-naphthoflavone with expected substrate-enzyme specificities. Predicted metabolites from the remaining 33 compounds as well as many unidentified m/z features were detected. We also showed that a specific bupropion metabolite generated by CYP2B6 cells, but not detected in the S9 system, was identified in human samples. Our data show that the chemical mixtures approach accelerated characterization of xenobiotic chemical space, while simultaneously identifying enzyme sources that can be used for scalable generation of metabolites for their identification in human metabolomic analyses. SIGNIFICANCE STATEMENT: High-resolution mass spectrometry (HRMS) enables the detection of exposures to drugs and other xenobiotics in human samples, but chemical identification can be difficult for several reasons. This paper demonstrates the utility of a panel of engineered cytochrome P450-expressing hepatoma cells in a scalable workflow for production of xenobiotic metabolites, which will facilitate their use as surrogate standards to validate xenobiotic detection by HRMS in human metabolomic studies.

Large scale enzyme based xenobiotic identification for exposomics.

Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites and could complement existing criteria for chemical identification.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

A Practical Guide to Metabolomics Software Development.

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

Network Topology of Biological Aging and Geroscience-Guided Approaches to COVID-19.

Aging has emerged as the greatest and most prevalent risk factor for the development of severe COVID-19 infection and death following exposure to the SARS-CoV-2 virus. The presence of multiple co-existing chronic diseases and conditions of aging further enhances this risk. Biological aging not only enhances the risk of chronic diseases, but the presence of such conditions further accelerates varied biological processes or "hallmarks" implicated in aging. Given growing evidence that it is possible to slow the rate of many biological aging processes using pharmacological compounds has led to the proposal that such geroscience-guided interventions may help enhance immune resilience and improve outcomes in the face of SARS-CoV-2 infection. Our review of the literature indicates that most, if not all, hallmarks of aging may contribute to the enhanced COVID-19 vulnerability seen in frail older adults. Moreover, varied biological mechanisms implicated in aging do not function in isolation from each other, and exhibit intricate effects on each other. With all of these considerations in mind, we highlight limitations of current strategies mostly focused on individual single mechanisms, and we propose an approach which is far more multidisciplinary and systems-based emphasizing network topology of biological aging and geroscience-guided approaches to COVID-19.

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/ .

Transcriptomic and Metabolic Responses to a Live-Attenuated Francisella tularensis Vaccine.

The immune response to live-attenuated Francisella tularensis vaccine and its host evasion mechanisms are incompletely understood. Using RNA-Seq and LC-MS on samples collected pre-vaccination and at days 1, 2, 7, and 14 post-vaccination, we identified differentially expressed genes in PBMCs, metabolites in serum, enriched pathways, and metabolites that correlated with T cell and B cell responses, or gene expression modules. While an early activation of interferon α/ß signaling was observed, several innate immune signaling pathways including TLR, TNF, NF-κB, and NOD-like receptor signaling and key inflammatory cytokines such as Il-1α, Il-1ß, and TNF typically activated following infection were suppressed. The NF-κB pathway was the most impacted and the likely route of attack. Plasma cells, immunoglobulin, and B cell signatures were evident by day 7. MHC I antigen presentation was more actively up-regulated first followed by MHC II which coincided with the emergence of humoral immune signatures. Metabolomics analysis showed that glycolysis and TCA cycle-related metabolites were perturbed including a decline in pyruvate. Correlation networks that provide hypotheses on the interplay between changes in innate immune, T cell, and B cell gene expression signatures and metabolites are provided. Results demonstrate the utility of transcriptomics and metabolomics for better understanding molecular mechanisms of vaccine response and potential host-pathogen interactions.

Tryptophan catabolism reflects disease activity in human tuberculosis.

There is limited understanding of the role of host metabolism in the pathophysiology of human tuberculosis (TB). Using high-resolution metabolomics with an unbiased approach to metabolic pathway analysis, we discovered that the tryptophan pathway is highly regulated throughout the spectrum of TB infection and disease. This regulation is characterized by increased catabolism of tryptophan to kynurenine, which was evident not only in active TB disease but also in latent TB infection (LTBI). Further, we found that tryptophan catabolism is reversed with effective treatment of both active TB disease and LTBI in a manner commensurate with bacterial clearance. Persons with active TB and LTBI also exhibited increased expression of indoleamine 2,3-dioxygenase-1 (IDO-1), suggesting IDO-1 mediates observed increases in tryptophan catabolism. Together, these data indicate IDO-1-mediated tryptophan catabolism is highly preserved in the human response to Mycobacterium tuberculosis and could be a target for biomarker development as well as host-directed therapies.

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Early Pregnancy Serum Metabolite Profiles Associated with Hypertensive Disorders of Pregnancy in African American Women: A Pilot Study.

Hypertensive disorders of pregnancy (HDP) are the most common cardiometabolic complications of pregnancy, affecting nearly 10% of US pregnancies and contributing substantially to maternal and infant morbidity and mortality. In the US, women of African American race are at increased risk for HDP. Early biomarkers that reliably identify women at risk for HDP remain elusive, yet are essential for the early identification and targeting of interventions to improve maternal and infant outcomes. We employed high-resolution metabolomics (HRM) to identify metabolites and metabolic pathways that were altered in early (8-14 weeks) gestation serum samples of pregnant African American women who developed HDP after 20 weeks' gestation (n = 20)-either preeclampsia (PE; n = 11) or gestational hypertension (gHTN; n = 9)-compared to those who delivered full term without complications (n = 80). We found four metabolic pathways that were significantly (p < 0.05) altered in women who developed PE and five pathways that were significantly (p < 0.05) altered in women who developed gHTN compared to women who delivered full term without complications. We also found that four specific metabolites (p < 0.05) were distinctly upregulated (retinoate, kynurenine) or downregulated (SN-glycero-3-phosphocholine, 2'4'-dihydroxyacetophenone) in women who developed PE compared to gHTN. These findings support that there are systemic metabolic disruptions that are detectable in early pregnancy (8-14 weeks of gestation) among pregnant African American women who develop PE and gHTN. Furthermore, the early pregnancy metabolic disruptions associated with PE and gHTN are distinct, implying they are unique entities rather than conditions along a spectrum of the same disease process despite the common clinical feature of high blood pressure.

A Bioinformatics Primer to Data Science, with Examples for Metabolomics.

With the increasing importance of big data in biomedicine, skills in data science are a foundation for the individual career development and for the progress of science. This chapter is a practical guide to working with high-throughput biomedical data. It covers how to understand and set up the computing environment, to start a research project with proper and effective data management, and to perform common bioinformatics tasks such as data wrangling, quality control, statistical analysis, and visualization, with examples on metabolomics data. Concepts and tools related to coding and scripting are discussed. Version control, knitr and Jupyter notebooks are important to project management, collaboration, and research reproducibility. Overall, this chapter describes a core set of skills to work in bioinformatics, and can serve as a reference text at the level of a graduate course and interfacing with data science.

The Essential Toolbox of Data Science: Python, R, Git, and Docker.

The daily work in data science involves a set of essential tools: the programming languages Python and R, the version control tool Git and the virtualization tool Docker. Proficiency in at least one programming language is required for data science. R is tied to a computing environment that focuses on statistics, in which many new algorithms in genomics and biomedicine are first published. Python has a root in system administration, and is a superb language for general programming. Version control is critical to managing complex projects, even if software development is not involved. Docker container is becoming a key tool for deployment, portability, and reproducibility. This chapter provides a self-contained practical guide of these topics so that readers can use it as a reference and to plan their training.

Pathway Analysis for Targeted and Untargeted Metabolomics.

Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.