A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R

A Concise Guide to Essential R Packages for Analyses of DNA, RNA, and Proteins

R is widely regarded as unrivalled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative PCR and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve.

Navigating common pitfalls in metabolite identification and metabolomics bioinformatics

BACKGROUND

Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools.

AIM OF REVIEW

In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.

PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks

MOTIVATION

Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

RESULTS

To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user's needs based on the reliability of imputed values.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/DILiS-lab/pepermint.

Pesticide residues and polyphenols in urine - A combined LC-HRMS screening to reveal intake patterns

Human exposure to pesticides in the general population occurs mainly through food consumption. However, specific dietary habits or food products that contribute to pesticide exposure are often unknown. In this study, we propose a combined screening for polyphenols and pesticide residues by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to assess the diet and the associated pesticide exposure. We measured 587 urine samples from women around the 34th week of pregnancy of a prospective mother-child cohort. A non-targeted screening for flavonoid-like compounds related to fruit and vegetable consumption was performed, prioritizing 164 features and identifying a total of 46 features by spectral library search. Based on a subset of markers, k-means clustering was performed, leading to four clusters with presumably similar dietary habits. The clusters were compared against food questionnaire data collected within the period of sample collection. Suspect screening of more than 500 pesticide residues including metabolites was performed, with a total of 40 residues being reported for 27 different pesticides. The detection of pesticide residues was compared across the different clusters of dietary habits. Indications were found that pyrimethanil metabolites might be associated with the consumption of citrus fruits or derivate products. We demonstrate that the method used has the potential to reveal patterns of pesticide intake from specific food commodities.

The Fluorinated NAD Precursors Enhance FK866 Cytotoxicity by Activating SARM1 in Glioblastoma Cells

Glioblastoma, a formidable brain tumor characterized by dysregulated NAD metabolism, poses a significant therapeutic challenge. The NAMPT inhibitor FK866, which induces NAD depletion, has shown promise in controlling tumor proliferation and modifying the tumor microenvironment. However, the clinical efficacy of FK866 as a single drug therapy for glioma is limited. In this study, we aim to disrupt NAD metabolism using fluorinated NAD precursors and explore their synergistic effect with FK866 in inducing cytotoxicity in glioblastoma cells. The synthesized analogue of nicotinamide riboside (NR), ara-F nicotinamide riboside (F-NR), inhibits nicotinamide ribose kinase (NRK) activity in vitro, reduces cellular NAD levels, and enhances FK866's cytotoxicity in U251 glioblastoma cells, indicating a collaborative impact on cell death. Metabolic analyses reveal that F-NR undergoes conversion to fluorinated nicotinamide mononucleotide (F-NMN) and other metabolites, highlighting the intact NAD metabolic pathway in glioma cells. The activation of SARM1 by F-NMN, a potent NAD-consuming enzyme, is supported by the synergistic effect of CZ-48, a cell-permeable SARM1 activator. Temporal analysis underscores the sequential nature of events, establishing NAD depletion as a precursor to ATP depletion and eventual massive cell death. This study not only elucidates the molecular intricacies of glioblastoma cell death but also proposes a promising strategy to enhance FK866 efficacy through fluorinated NAD precursors, offering potential avenues for innovative therapeutic interventions in the challenging landscape of glioblastoma treatment.

Phytochemical drivers of insect herbivory: a functional toolbox to support agroecological diversification

Plant metabolism is a key feature of biodiversity that remains underexploited in functional frameworks used in agroecology. Here, we study how phytochemical diversity considered at three organizational levels can promote pest control. In a factorial field experiment, we manipulated plant diversity in three monocultures and three mixed crops of oilseed rape to explore how intra- and interspecific phytochemical diversity affects pest infestation. We combined recent progress in metabolomics with classic metrics used in ecology to test a box of hypotheses grounded in plant defence theory. According to the hypothesis of 'phytochemically mediated coevolution', our study stresses the relationships between herbivore infestation and particular classes of specialized metabolites like glucosinolates. Among 178 significant relationships between metabolites and herbivory rates, only 20% were negative. At the plant level, phytochemical abundance and richness had poor predictive power on pest regulation. This challenges the hypothesis of 'synergistic effects'. At the crop cover level, in line with the hypothesis of 'associational resistance', the phytochemical dissimilarity between neighbouring plants limited pest infestation. We discuss the intricate links between associational resistance and bottom-up pest control. Bridging different levels of organization in agroecosystems helps to dissect the multi-scale relationships between phytochemistry and insect herbivory.

Production of Polyphenolic Natural Products by Bract-Derived Tissue Cultures of Three Medicinal Tilia spp.: A Comparative Untargeted Metabolomics Study

Medicinal plant tissue cultures are potential sources of bioactive compounds. In this study, we report the chemical characterization of the callus cultures of three medicinal Tilia spp. (Tilia cordata, Tilia vulgaris and Tilia tomentosa), along with the comparison to bracts and flowers of the same species. Our aim was to show that calli of Tilia spp. are good alternatives to the calli of T. americana for the production of polyphenols and are better sources of a subset of polyphenolic metabolites, compared to the original organs. Calli were initiated from young bracts and grown on woody plant medium containing 1 mg L-1 2,4-D and 0.1 mg L-1 BAP. For chemical characterization, a quality-controlled untargeted metabolomics approach and the quantification of several bioactive compounds was performed with the use of LC-ESI-MS/MS. While bracts and flowers contained flavonoid glycosides (astragalin, isoquercitrin) as major polyphenols, calli of all species contained catechins, coumarins (fraxin, esculin and scopoletin) and flavane aglyca. T. tomentosa calli contained 5397 µg g DW-1 catechin, 201 µg g DW-1 esculin, 218 µg g DW-1 taxifolin and 273 µg g DW-1 eriodictyol, while calli from other species contained lower amounts. T. cordata and T. tomentosa flowers were rich in isoquercitrin, containing 8134 and 6385 µg g DW-1, respectively. The currently tested species contained many of the bioactive metabolites described from T. americana. The production of catechin was shown to be comparable to the most efficient tissue cultures reported. Flowers and bracts contained flavonoid glycosides, including tiliroside, resembling bioactive fractions of T. americana. In addition, untargeted metabolomics has shown fingerprint-like differences among species, highlighting possible chemotaxonomic and quality control applications, especially for bracts.

Discovery of Glycation Products: Unraveling the Unknown Glycation Space Using a Mass Spectral Library from In Vitro Model Systems

The nonenzymatic reaction between amino acids (AAs) and reducing sugars, also known as the Maillard reaction, is the primary source of free glycation products (GPs) in vivo and in vitro. The limited number of MS/MS records for GPs in public libraries hinders the annotation and investigation of nonenzymatic glycation. To address this issue, we present a mass spectral library containing the experimental MS/MS spectra of diverse GPs from model systems. Based on the conceptional reaction processes and structural characteristics of products, we classified GPs into common GPs (CGPs) and modified AAs (MAAs). A workflow for annotating GPs was established based on the structural and fragmentation patterns of each GP type. The final spectral library contains 157 CGPs, 499 MAAs, and 2426 GP spectra with synthetic model system information, retention time, precursor m/z, MS/MS, and annotations. As a proof-of-concept, we demonstrated the use of the library for screening GPs in unidentified spectra of human plasma and urine. The AAs with the C6H10O5 modification, fructosylation from Amadori rearrangement, were the most found GPs. With the help of the model system, we confirmed the existence of C6H10O5-modified Valine in human plasma by matching both retention time, MS1, and MS/MS without reference standards. In summary, our GP library can serve as an online resource to quickly screen possible GPs in an untargeted metabolomics workflow, furthermore with the model system as a practical synthesis method to confirm their identity.

Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Standardized Workflow for Mass-Spectrometry-Based Single-Cell Proteomics Data Processing and Analysis Using the scp Package

Mass-spectrometry (MS)-based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells-proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover, it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardized framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this chapter, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalization, and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardized framework and highlight some crucial steps.

Integrating HexNAcQuest with Glycoproteomics Data Analysis Software to Distinguish HexNAc Isomers on Peptides

Recently, HexNAcQuest was developed to help distinguish peptides modified by HexNAc isomers, more specifically O-linked β-N-acetylglucosamine (O-GlcNAc) and O-linked α-N-acetylgalactosamine (O-GalNAc, Tn antigen). To facilitate its usage (particularly for datasets from glycoproteomics studies), herein we present a detailed protocol. It describes example cases and procedures for which users might need to use HexNAcQuest to distinguish these two modifications.

High-Resolution Mass Spectrometry-Based Metabolomics for Increased Grape Juice Metabolite Coverage

The composition of the juice from grape berries is at the basis of the definition of technological ripeness before harvest, historically evaluated from global sugar and acid contents. If many studies have contributed to the identification of other primary and secondary metabolites in whole berries, deepening knowledge about the chemical composition of the sole flesh of grape berries (i.e., without considering skins and seeds) at harvest is of primary interest when studying the enological potential of widespread grape varieties producing high-added-value wines. Here, we used non-targeted DI-FT-ICR-MS and RP-UHPLC-Q-ToF-MS analyses to explore the extent of metabolite coverage of up to 290 grape juices from four Vitis vinifera grape varieties, namely Chardonnay, Pinot noir, Meunier, and Aligoté, sampled at harvest from 91 vineyards in Europe and Argentina, over three successive vintages. SPE pretreatment of samples led to the identification of more than 4500 detected C,H,O,N,S-containing elemental compositions, likely associated with tens of thousands of distinct metabolites. We further revealed that a major part of this chemical diversity appears to be common to the different juices, as exemplified by Pinot noir and Chardonnay samples. However, it was possible to build significant models for the discrimination of Chardonnay from Pinot noir grape juices, and of Chardonnay from Aligoté grape juices, regardless of the geographical origin or the vintage. Therefore, this metabolomic approach opens access to a remarkable holistic molecular description of the instantaneous composition of such a biological matrix, which is the result of complex interplays among environmental, biochemical, and vine growing practices.

Untargeted metabolomics of the intestinal tract of DEV-infected ducks

INTRODUCTION

Duck enteritis virus (DEV) mainly causes infectious diseases characterized by intestinal haemorrhage, inflammation and parenchymal organ degeneration in ducks and other poultry. However, the mechanism by which it causes intestinal damage in ducks is not well understood. Metabolomics can provide an in-depth understanding of the full complexity of the disease.

METHODS

In this study, 24 clinically healthy green-shell ducks (weight 1.5 kg ± 20 g) were randomly divided into 2 groups (experimental group, 18; control group, 6). The experimental group was intramuscularly injected with 0.2 mL of DEV virus in solution (TCID50 3.16 × 108 PFU/mL), and the control group was injected with 0.2 mL of sterile normal saline. Duck duodenum and ileum tissue samples were collected at 66 h, 90 h and 114 h post-injection (12 h of fasting before killing), and metabolomics analysis of duck duodenum and ileum tissues at the three time points (66, 90, 114 h) was performed by liquid chromatography-mass spectrometry (LC-MS) to screen for and analyse the potential differentiated metabolites and related signalling pathways.

RESULTS

Screening was performed in the positive/negative mode (Pos: Positive ion mode; the ionization of substances at the ion source with positive ions such as H+, NH4+, Na+ and K+; Neg: Negative ion mode; the ionization of substances at the ion source with negative ions such as Cl-, OAc-), and compound abundance was compared to that in the control group. The total number of differentially abundant compounds in the duodenum at 66 h, 90 h and 114 h of DEV infection gradually increased, and metabolites such as cytidine, 2'-deoxyriboside and 4-guanidinobutyric acid were differentially abundant metabolites common to all three time periods. The metabolic pathways related to inflammatory response and immune response were tryptophan acid metabolism, cysteine-methionine metabolism, histidine metabolism and other amino acid metabolism and fat metabolism. Among them, the metabolic pathways with more differentially abundant metabolites were amino acid biosynthesis, cysteine and methionine metabolism, tryptophan metabolism, unsaturated fatty acid biosynthesis and purine metabolism, and the metabolic pathways with more enrichment factors were the IgA-related intestinal immune network pathway and lysosome pathway. Compared with the control group, there were 16 differentially abundant metabolites in the ileum tissue of DEV-infected ducks at 66 h of infection, 52 at 90 h of infection, and 40 at 14 h of infection with TD114. The metabolic pathways with more enriched differentially abundant metabolites were pyrimidine metabolism, tyrosine metabolism, phenylalanine metabolism and tryptophan biosynthesis. The metabolic pathways with the most enrichment factors were the mTOR signalling pathway, ferroptosis pathway, tryptophan metabolism pathway and caffeine metabolism pathway.

CONCLUSION

Comparative analysis showed that the number of differentially abundant metabolites in the duodenum and ileum differed to some extent after DEV infection, with significantly more differentially abundant metabolites in duodenal tissues and fewer in ileal tissues; after DEV infection, the highest number of differentially abundant metabolites was obtained at 114 h of DEV infection, followed by the second highest at 90 h of infection and the lowest at 66 h of infection. The common differentially abundant metabolites in duodenal and ileal tissues were prostaglandins, arachidonic acid, and arachidonic ethanolamine. The main metabolic pathways in the duodenum were the IgA-associated intestinal immune network pathway and the lysosomal pathway, and the metabolic pathways with more enriched factors in the ileum were the mTOR signalling pathway, the ferroptosis pathway, and the tryptophan metabolism pathway.

The Integration of the Metabolome and Transcriptome for Dendrobium nobile Lindl. in Response to Methyl Jasmonate

Dendrobium nobile Lindl., as an endangered medicinal plant within the genus Dendrobium, is widely distributed in southwestern China and has important ecological and economic value. There are a variety of metabolites with pharmacological activity in D. nobile. The alkaloids and polysaccharides contained within D. nobile are very important active components, which mainly have antiviral, anti-tumor, and immunity improvement effects. However, the changes in the compounds and functional genes of D. nobile induced by methyl jasmonate (MeJA) are not clearly understood. In this study, the metabolome and transcriptome of D. nobile were analyzed after exposure to MeJA. A total of 377 differential metabolites were obtained through data analysis, of which 15 were related to polysaccharide pathways and 35 were related to terpenoids and alkaloids pathways. Additionally, the transcriptome sequencing results identified 3256 differentially expressed genes that were discovered in 11 groups. Compared with the control group, 1346 unigenes were differentially expressed in the samples treated with MeJA for 14 days (TF14). Moreover, the expression levels of differentially expressed genes were also significant at different growth and development stages. According to GO and KEGG annotations, 189 and 99 candidate genes were identified as being involved in terpenoid biosynthesis and polysaccharide biosynthesis, respectively. In addition, the co-expression analysis indicated that 238 and 313 transcription factors (TFs) may contribute to the regulation of terpenoid and polysaccharide biosynthesis, respectively. Through a heat map analysis, fourteen terpenoid synthetase genes, twenty-three cytochrome P450 oxidase genes, eight methyltransferase genes, and six aminotransferase genes were identified that may be related to dendrobine biosynthesis. Among them, one sesquiterpene synthase gene was found to be highly expressed after the treatment with MeJA and was positively correlated with the content of dendrobine. This study provides important and valuable metabolomics and transcriptomic information for the further understanding of D. nobile at the metabolic and molecular levels and provides candidate genes and possible intermediate compounds for the dendrobine biosynthesis pathway, which lays a certain foundation for further research on and application of Dendrobium.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

Identification of Emerging Novel Psychoactive Substances by Retrospective Analysis of Population-Scale Mass Spectrometry Data Sets

Over the last two decades, hundreds of new psychoactive substances (NPSs), also known as "designer drugs", have emerged on the illicit drug market. The toxic and potentially fatal effects of these compounds oblige laboratories around the world to screen for NPS in seized materials and biological samples, commonly using high-resolution mass spectrometry. However, unambiguous identification of a NPS by mass spectrometry requires comparison to data from analytical reference materials, acquired on the same instrument. The sheer number of NPSs that are available on the illicit market, and the pace at which new compounds are introduced, means that forensic laboratories must make difficult decisions about which reference materials to acquire. Here, we asked whether retrospective suspect screening of population-scale mass spectrometry data could provide a data-driven platform to prioritize emerging NPSs for assay development. We curated a suspect database of precursor and diagnostic fragment ion masses for 83 emerging NPSs and used this database to retrospectively screen mass spectrometry data from 12,727 urine drug screens from one Canadian province. We developed integrative computational strategies to prioritize the most reliable identifications and tracked the frequency of these identifications over a 3 year study period between August 2019 and August 2022. The resulting data were used to guide the acquisition of new reference materials, which were in turn used to validate a subset of the retrospective identifications. Last, we took advantage of matching clinical reports for all 12,727 samples to systematically benchmark the accuracy of our retrospective data analysis approach. Our work opens up new avenues to enable the rapid detection of emerging illicit drugs through large-scale reanalysis of mass spectrometry data.

The Pharmacokinetics of Individual Conjugated Xanthohumol Metabolites Show Efficient Glucuronidation and Higher Bioavailability of Micellar than Native Xanthohumol in a Randomized, Double-Blind, Crossover Trial in Healthy Humans

SCOPE

Prenylated chalcones and flavonoids are found in many plants and are believed to have beneficial effects on health when consumed. Xanthohumol is present in beer and likely the most consumed prenylated chalcone, but poorly absorbed and rapidly metabolized and excreted, thus limiting its bioavailability. Micellar formulations of phytochemicals have been shown to improve bioavailability.

METHODS AND RESULTS

In a randomized, double-blind, crossover trial with five healthy (three males and two females) volunteers, a single dose of 43 mg was orally administered as a native or micellar formulation. The major human xanthohumol metabolites are quantified in plasma. Unmetabolized free xanthohumol makes 1% or less of total plasma xanthohumol. The area under the plasma concentration-time curve of xanthohumol-7-O-glucuronide following the ingestion of the micellular formulation is 5-fold higher and its maximum plasma concentration is more than 20-fold higher compared to native xanthohumol.

CONCLUSION

Metabolism of orally ingested xanthohumol is complex and efficiently converts the parent compound to predominantly glucuronic acid and to a lesser extent sulfate conjugates. The oral bioavailability of micellar xanthohumol is superior to native xanthohumol, making it a useful delivery form for future human trials.

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

MsQuality: an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data

MOTIVATION

Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows.

RESULTS

The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets.

AVAILABILITY AND IMPLEMENTATION

The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.

ShinyMetID: An R shiny package for metabolite identification by mass spectral matching

We present metabolite identification software in the form of R Shiny. Metabolite identification by mass spectral matching in gas chromatography (GC-MS)-based untargeted metabolomics can be done by using the easy-to-use software. Various similarity measures are given and toy example using graphical user interface is presented.

A cytochrome P450 CYP87A4 imparts sterol side-chain cleavage in digoxin biosynthesis

Digoxin extracted from the foxglove plant is a widely prescribed natural product for treating heart failure. It is listed as an essential medicine by the World Health Organization. However, how the foxglove plant synthesizes digoxin is mostly unknown, especially the cytochrome P450 sterol side chain cleaving enzyme (P450scc), which catalyzes the first and rate-limiting step. Here we identify the long-speculated foxglove P450scc through differential transcriptomic analysis. This enzyme converts cholesterol and campesterol to pregnenolone, suggesting that digoxin biosynthesis starts from both sterols, unlike previously reported. Phylogenetic analysis indicates that this enzyme arises from a duplicated cytochrome P450 CYP87A gene and is distinct from the well-characterized mammalian P450scc. Protein structural analysis reveals two amino acids in the active site critical for the foxglove P450scc's sterol cleavage ability. Identifying the foxglove P450scc is a crucial step toward completely elucidating digoxin biosynthesis and expanding the therapeutic applications of digoxin analogs in future work.

Untargeted lipidomic profiling of grapes highlights the importance of modified lipid species beyond the traditional compound classes

The aim of this paper is to provide a detailed characterisation of grape lipidome. To achieve this objective, it starts by describing a pipeline implemented in R software to allow the semi-automatic annotation of the detected lipid species. It also provides an extensive description of the different properties of each molecule (such as retention time dependencies, mass accuracy, adduct formation and fragmentation patterns), which allowed the annotations to be made more accurately. Most annotated lipids in the grape samples were (lyso)glycerophospholipids and glycerolipids, although a few free fatty acids, hydroxyceramides and sitosterol esters were also observed. The proposed pipeline also allowed the identification of a series of methylated glycerophosphates never previously observed in grapes. The current results highlight the importance of expanding chemical analyses beyond the classical lipid categories.

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R² > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

An untargeted metabolomics investigation in liver of flaviviruses-infected mice

Dengue virus (DENV), Japanese encephalitis virus (JEV) and Zika virus (ZIKV) are the three most important flaviviruses, which can cause health problems worldwide. All these flaviviruses can cause liver damage, however, the mechanism of liver injury is still unclear. Metabolomics can give insight into the full complexity of a disease. In our study, we used an LC-MS method to analysis the metabolites in liver samples of the three flaviviruses-infected mice and the non-infected mice. Compared with the control mice, the liver of the DENV-infected, JEV-infected, and ZIKV-infected mice had 32, 34, and 55 differential metabolites. We also found that there were obvious differences in some metabolic pathways among the four groups. Metabonomic analysis of liver is very important for understanding the pathogenesis of flaviviruses.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS²) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS² spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub ( https://github.com/zmahnoor14/MAW ). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Lipidomic and Metallomic Alteration of Caenorhabditis elegans after Acute and Chronic Manganese, Iron, and Zinc Exposure with a Link to Neurodegenerative Disorders

Parkinson's disease (PD) progresses with the loss of dopaminergic neurons in the substantia nigra pars compacta region of the brain. The superior mechanisms and the cause of this specific localized neurodegeneration is currently unknown. However, experimental evidence indicates a link between PD progression and reactive oxygen species with imbalanced metal homeostasis. Wild-type Caenorhabditis elegans exposed to redox-active metals was used as the model organism to study cellular response to imbalanced metal homeostasis linked to neurodegenerative diseases. Using modern hyphenated techniques such as capillary electrophoresis coupled to inductively coupled plasma mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry, alterations in the lipidome and metallome were determined in vivo. In contrast to iron, most of the absorbed zinc and manganese were loosely bound. We observed changes in the phospholipid composition for acute iron and manganese exposures, as well as chronic zinc exposure. Furthermore, we focused on the mitochondrial membrane alteration due to its importance in neuronal function. However, significant changes in the inner mitochondrial membrane by determination of cardiolipin species could only be observed for acute iron exposure. These results indicate different intracellular sites of local ROS generation, depending on the redox active metal. Our study combines metallomic and lipidomic alterations as the cause and consequence to enlighten intracellular mechanisms in vivo, associated with PD progression. The mass spectrometry raw data have been deposited to the MassIVE database (https://massive.ucsd.edu) with the identifier MSV000090796 and 10.25345/C51J97C8F.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

VAMS-Based Blood Capillary Sampling for Mass Spectrometry-Based Human Metabolomics Studies

Volumetric absorptive microsampling (VAMS) is a recently developed sample collection method that enables single-drop blood collection in a minimally invasive manner. Blood biomolecules can then be extracted and processed for analysis using several analytical platforms. The integration of VAMS with conventional mass spectrometry (MS)-based metabolomics approaches is an attractive solution for human studies representing a less-invasive procedure compared to phlebotomy with the additional potential for remote sample collection. However, as we recently demonstrated, VAMS samples require long-term storage at -80 °C. This study investigated the stability of VAMS samples during short-term storage and compared the metabolome obtained from capillary blood collected from the fingertip to those of plasma and venous blood from 22 healthy volunteers. Our results suggest that the blood metabolome collected by VAMS samples is stable at room temperature only for up to 6 h requiring subsequent storage at -80 °C to avoid significant changes in the metabolome. We also demonstrated that capillary blood provides better coverage of the metabolome compared to plasma enabling the analysis of several intracellular metabolites presented in red blood cells. Finally, this work demonstrates that with the appropriate pre-analytical protocol capillary blood can be successfully used for untargeted metabolomics studies.

A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences

Within the Human Biomonitoring for Europe initiative (HBM4EU), a study to determine new biomarkers of exposure to pesticides and to assess exposure patterns was conducted. Human urine samples (N = 2,088) were collected from five European regions in two different seasons. The objective of the study was to identify pesticides and their metabolites in collected urine samples with a harmonized suspect screening approach based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) applied in five laboratories. A combined data processing workflow included comprehensive data reduction, correction of mass error and retention time (RT) drifts, isotopic pattern analysis, adduct and elemental composition annotation, finalized by a mining of the elemental compositions for possible annotations of pesticide metabolites. The obtained tentative annotations (n = 498) were used for acquiring representative data-dependent tandem mass spectra (MS2) and verified by spectral comparison to reference spectra generated from commercially available reference standards or produced through human liver S9 in vitro incubation experiments. 14 parent pesticides and 71 metabolites (including 16 glucuronide and 11 sulfate conjugates) were detected. Collectively these related to 46 unique pesticides. For the remaining tentative annotations either (i) no data-dependent MS2 spectra could be acquired, (ii) the spectral purity was too low for sufficient matching, or (iii) RTs indicated a wrong annotation, leaving potential for more pesticides and/or their metabolites being confirmed in further studies. Thus, the reported results are reflecting only a part of the possible pesticide exposure.

SQuAPP – Simple Quantitative Analysis of Proteins & PTMs

Summary

The Comprehensive analysis of the proteome and its modulation by post-translational modification is increasingly used in biological and biomedical studies. As a result, proteomics data analysis is ever more carried out by scientists with limited expertise in this type of data. While excellent software solutions for comprehensive and rigorous analysis of quantitative proteomic data exist, most are complex and not well suited for non-proteomics scientists. Integrative analysis of multi-level proteomics data on protein and diverse post-translational modifications (PTMs), like phosphorylation or proteolytic processing, remains particularly challenging and inaccessible to most biologists. To fill this void, we developed SQuAPP, an R-Shiny web-based analysis pipeline for the quantitative analysis of proteomic data. SQuAPP uses a streamlined workflow model to guide expert and novice users through quality control, data pre-processing, statistical analysis and visualization steps. Processing the protein, peptide, and post-translational modification datasets in parallel and their quantitative integration enable rapid identification of protein-level-independent modulation of protein modifications and intuitive interpretation of dynamic dependencies between different protein modifications.

Availability

SQuAPP is available at http://squapp.langelab.org/. The source code and local setup instructions can be accessed from https://github.com/LangeLab/SQuAPP.

MobilityTransformR: an R package for effective mobility transformation of CE-MS data

SUMMARY

We present MobilityTransformR, an R/Bioconductor package for the effective mobility scaling of capillary zone electrophoresis-mass spectrometry (CE-MS) data. It uses functionality from different R packages that are frequently used for data processing and analysis in MS-based metabolomics workflows, allowing the subsequent use of reproducible transformed CE-MS data in existing workflows.

AVAILABILITY AND IMPLEMENTATION

MobilityTransformR is implemented in R (Version >= 4.2) and can be downloaded directly from the Bioconductor database (https://bioconductor.org/packages/MobilityTransformR) or GitHub (https://github.com/LiesaSalzer/MobilityTransformR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TidyMass an object-oriented reproducible analysis framework for LC-MS data

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.