nucleic acids research article types

Type II restriction endonucleases--a historical perspective and more

Affiliations.

• 1 Institute of Biochemistry, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 58, D-35392 Giessen, Germany [email protected].
• 2 New England Biolabs Inc., 240 County Road, Ipswich, MA 01938-2723, USA.
• 3 Institute of Biochemistry, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 58, D-35392 Giessen, Germany.
• PMID: 24878924
• PMCID: PMC4081073
• DOI: 10.1093/nar/gku447

This article continues the series of Surveys and Summaries on restriction endonucleases (REases) begun this year in Nucleic Acids Research. Here we discuss 'Type II' REases, the kind used for DNA analysis and cloning. We focus on their biochemistry: what they are, what they do, and how they do it. Type II REases are produced by prokaryotes to combat bacteriophages. With extreme accuracy, each recognizes a particular sequence in double-stranded DNA and cleaves at a fixed position within or nearby. The discoveries of these enzymes in the 1970s, and of the uses to which they could be put, have since impacted every corner of the life sciences. They became the enabling tools of molecular biology, genetics and biotechnology, and made analysis at the most fundamental levels routine. Hundreds of different REases have been discovered and are available commercially. Their genes have been cloned, sequenced and overexpressed. Most have been characterized to some extent, but few have been studied in depth. Here, we describe the original discoveries in this field, and the properties of the first Type II REases investigated. We discuss the mechanisms of sequence recognition and catalysis, and the varied oligomeric modes in which Type II REases act. We describe the surprising heterogeneity revealed by comparisons of their sequences and structures.

© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Publication types

• Historical Article
• Research Support, Non-U.S. Gov't
• DNA / chemistry
• DNA / metabolism
• Deoxyribonucleases, Type II Site-Specific / chemistry*
• Deoxyribonucleases, Type II Site-Specific / genetics
• Deoxyribonucleases, Type II Site-Specific / history
• Deoxyribonucleases, Type II Site-Specific / metabolism*
• Evolution, Molecular
• History, 20th Century
• History, 21st Century
• Protein Engineering
• Restriction Mapping
• Deoxyribonucleases, Type II Site-Specific

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• v.46(Database issue); 2018 Jan 4

The 2018 Nucleic Acids Research database issue and the online molecular biology database collection

Daniel j rigden.

Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK

Xosé M Fernández

Institut Curie, 25 rue d’Ulm, 75005 Paris, France

The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website ( https://academic.oup.com/nar ). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/ .

NEW AND UPDATED DATABASES

This 2018 Nucleic Acids Research Database Issue is the 25th annual collection of bioinformatic databases. The quarter century arrives with 181 papers which, as ever, span all areas of molecular biology research. The total includes 82 new databases (Table ​ (Table1) 1 ) and 84 updates of resources that have previously appeared in the Database Issue. There are also 15 updates on databases previously described elsewhere (Table ​ (Table2 2 ).

a For full references to the databases featured in this issue, please see the Table of Contents.

As in previous years, databases are grouped into eight broad subject categories. These cover (i) nucleic acid sequence and structure, transcriptional regulation; (ii) protein sequence and structure; (iii) metabolic and signalling pathways, enzymes and networks; (iv) genomics of viruses, bacteria, protozoa and fungi; (v) genomics of human and model organisms plus comparative genomics; (vi) human genomic variation, diseases and drugs; (vii) plants and (viii) other topics, such as proteomics databases. In an era of increasingly interdisciplinary research, it is no surprise that the content of many databases spans multiple categories so that resources often do not sit comfortably in a single category. Readers are again urged to browse the whole issue, rather than confining themselves to the most obviously relevant sections. The Nucleic Acids Research online Molecular Biology Database Collection, which is available at http://www.oxfordjournals.org/nar/database/c/ , retains its more finely grained organisation, encompassing 15 categories and 41 subcategories.

The issue begins with broad surveys of resources at major global centres, including the U.S. National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EBI) and the BIG Data Center at the Beijing Institute of Genomics, Chinese Academy of Sciences. The NCBI Resources paper ( 1 ) presents an interesting analysis illustrating the extent of the cross-talk between different databases within the site, exemplifying the value to the user of the extensive data integration implemented at these centres. The EBI paper ( 2 ) describes new data types including image data, biobanks and biosamples, as well as charting the continued exponential growth in the volume of many kinds of data. The newest of the three, the BIG Data Center ( 3 ), focuses on genomic information, but also hosts facilities for samples, program code, and wikis. Many of the wikis are very active and have previously featured in NAR eg lncRNAwiki ( 4 ).

The ‘Nucleic acid databases’ section begins with updates from the International Nucleotide Sequence Database Collaboration ( 5 ) and its three contributors, GenBank, ENA and DDBJ ( 6 – 8 ) which together face the challenge of continued exponential growth in nucleic acid sequence data. Transcription factors (TF) and transcriptional regulation are represented by a number of databases. The popular returning database of TF binding profiles, JASPAR ( 9 ), is published back to back with the ReMAP database ( 10 ) of TF ChIP-seq data: data from ReMAP contributed directly to JASPAR’s improved coverage. With recent intense interest in the role of 3D chromatin structure in gene regulation, the 3DIV resource ( 11 ) for 3D genome interaction visualisation is timely. The key RNA database Rfam ( 12 ) contributes an update describing a move to content based on a set of reference genomes. Mirroring changes made in Pfam ( 13 ), this eliminates much unhelpful redundancy and allows for clearer taxonomic comparisons.

miRNA biology is strongly represented by updates from established databases such as DIANA-TarBase ( 14 ) and mirDIP ( 15 ), as well as new databases such as miRCarta ( 16 ). The new MSDD ( 17 ) links miRNA SNPs to diseases while EVLncRNAs ( 18 ) and MNDR ( 19 ) also major on disease links for non-coding RNAs. The well-established MODOMICS database of RNA modifications ( 20 ) is the subject of an update paper which, among other developments, reports on the availability of liquid chromatography/mass spectrometry data for modified nucleosides, facilitating profiling of such modifications by these methods. RNA structure is covered by the returning RMDB database ( 21 ), containing chemical mapping information that can be used to predict RNA secondary and tertiary structure, and the new RNArchitecture ( 22 ) which introduces a hierarchical organisation of RNA families with a focus on 3D structures, in the manner popularised by protein databases like SCOP.

In the section on protein sequence and structure databases, the venerable SMART database celebrates 20 years with an update paper ( 23 ). It describes a particularly valuable new visualisation option, whereby domain architecture information can be added to phylogenetic trees with the Interactive Tree of Life (iTOL) tool ( 24 ). Another update from PDBe ( 25 ) includes mention of a newly developed library of freely available web components for interactive data visualizations. One of these, the LiteMol 3D viewer, notably allows convenient display of electron density in the browser window. An update on the popular ELM database of protein sequence motifs ( 26 ) reports, among other developments, on how fascinating examples of bacterial pathogen mimicry of eukaryotic motifs are now included in the database. A new arrival, ChannelsDB ( 27 ) contributes our cover image and describes the channels, tunnels and pores in protein structures that allow substrate access to buried catalytic sites, for example, or molecular passage through a transmembrane protein. Certain protein classes or families justify their own bespoke databases through medical or biological importance. T-cell receptors are served in this issue by both VDJdb ( 28 ), focussing on receptor sequences of known specificity, and STCRDab ( 29 ) which collects and curates structural information, linking to and allowing searches against a wide variety of structural, sequence and functional data. The returning database GPCRdb ( 30 ), for G protein-coupled receptors, majors on carefully made homology models and mapping receptors to ligands.

Important updates in the metabolic and signalling section include the human metabolomics database HMDB ( 31 ). Release 4.0 brings huge increases in content, an improved interface and new kinds of information—predicted mass spectra and pharmacometabolomics. This issue also reports on a new metabolomics database, PAMDB ( 32 ), devoted to the bacterial pathogen Pseudomonas aeruginosa , justified not only by the biomedical importance of the organism but also by the novel metabolites that it contains. Metabolic pathways are covered by the well-known returning databases Reactome ( 33 ) and WikiPathways ( 34 ). The former update is notable for its Enhanced High Level Diagrams which superbly contextualise low-level pathways using images of cells, tissues and organs. Among enzyme-oriented databases MEROPS ( 35 ), devoted to proteases and their inhibitors, makes a welcome return with a near-doubling of sequences and cross-references to the PANTHER database ( 36 ). PANTHER full-length sequence based clustering is shown to be complementary to MEROP’s domain-based structure. Carbohydrate-active enzymes are covered by the arrival in NAR of PULDB ( 37 ), covering polysaccharide utilization loci in the prominent gut bacteria of the phylum Bacteriodetes, and dbCAN-seq ( 38 ), which usefully extrapolates information from the well-known CAZy database ( 39 ) to a genome scale. At the enzyme mechanism level, this issue sees the merger of two databases, MACiE and CSA, each veterans of multiple Database Issues, into a single new resource M-CSA (Mechanism and Catalytic Site Atlas) ( 40 ).

In the microbial genomics section, there is an update paper from the yeast-focused SGD ( 41 ) which now includes curated lists of yeast genes that can replace the functions of human counterparts or vice versa. The popular TADB, covering toxins and antitoxins, also presents an update ( 42 ), as does Subti Wiki ( 43 ), devoted to the biology of Bacillus subtilis . Two new databases address viruses. The Virus Taxonomy ( 44 ) appears in NAR for the first time, despite the International Committee behind it dating back to the 1960s. The second, MVP ( 45 ) describes the complex interactions between microbes and the phage clusters that can infect one or more of them.

Human and model organism genomics are strongly represented. The core resources Ensembl ( 46 ) and the UCSC Genome Browser ( 47 ) present their usual updates. The former is supplemented by an Ensembl Genomes paper ( 48 ) covering non-vertebrates which reports ∼20 000 new genomes covered. Other well-known returning databases include ENCODE ( 49 ), RefSeq ( 50 ) and Genomicus ( 51 ), the last showcasing new karyotype evolutionary trees. Among new databases, current trends in cell and molecular biology are reflected in StemMapper ( 52 ) that focusses specifically on stem cell gene expression, and SCPortalen ( 53 ) which stores transcriptomics data, metadata and cell images at the single cell level. Another notable new arrival is PICKLES ( 54 ) which collects information on human gene essentiality from the results of genome scale CRISPR knockout and shRNA knockdown experiments in cancer and other cell lines.

As ever, databases devoted to human genomic variation and biomedical research are very well represented. Important returning databases include the IUPHAR/BPS Guide to Pharmacology ( 55 ) which covers properties of existing and potential drug targets. The authors of the update also describe a major new sister resource, the Guide to Immunopharmacology. An interesting evolutionary perspective on drug targets is provided by ECOdrug ( 56 ) which maps the presence or absence of drug target orthologues across species. This will help in efforts to address ecotoxicology concerns over binding of drugs to non-target wild species and assist with appropriate species choices for ecological risk assessments. The popular DrugBank ( 57 ) also returns, now in release 5.0 and bringing huge increases in data volume, new data types such as pharmacotranscriptomics and content reporting on the status of clinical trials. A major new resource is the Genome Variation Map ( 58 ) from the BIG Data Center covering 19 species. Its arrival is particularly timely with the announcement that comparable NCBI resources dbSNP and dbVar are to stop accepting non-human submissions ( https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/09/phasing-out-support-for-non-human-genome-organism-data-in-dbsnp-and-dbvar/ ).The well-used ClinVar resource ( 59 ) also contributes an update and is joined in interpreting human genome variation and its implications for disease by the newcomer VarCards ( 60 ). Two interesting new databases, PGG .Population ( 61 ) and PopHuman ( 62 ) present a population genomics perspective of human genome variation, each containing thousands of human genomes from across the world and allowing interactive exploration of and comparison between populations.

Plant databases represented here include the comparative genomics resources PLAZA ( 63 ) and Gramene ( 64 ). A major new Arabidopsis resource arrives in the form of the AraGWAS catalog ( 65 ) which contains hundreds of thousands of links between SNPs and curated phenotypes. In the last section proteomics databases are well-represented. An update is presented on the major quantitative proteomics resource proteomicsDB ( 66 ). Its protein-centric view links to an impressive variety of visualisations and to different kinds of omics data. Future plans include an extension from its current human focus to model organisms. An intuitive user interface is also a strong point of the new EPD database ( 67 ), while PIT-DB ( 68 ) explicitly works at the intersection of RNA-seq transcriptomics and proteomics mass spectrometry. After covering such a variety of biological areas, it seems appropriate to finish with mention of the BioStudies database ( 69 ) that collates data of any and all kinds relating to a single study.

NAR ONLINE MOLECULAR BIOLOGY DATABASE COLLECTION

We reach this year the 25th update of the NAR online Molecular Biology Database Collection (which is freely available at http://www.oxfordjournals.org/nar/database/c/ ), featuring 88 new databases (Table ​ (Table1) 1 ) and 15 databases not described previously in the NAR Database Issue (Table ​ (Table2). 2 ). Within our ongoing verification processes to make sure information is still relevant, we have removed 47 obsolete or discontinued databases. After contacting their authors, 138 database entries have been updated with respect to new URLs, new descriptions, and/or other metadata.

We welcome suggestions for inclusion in the Collection of additional databases that have been published in other journals. Such suggestions should be addressed to XMF at [email protected] and should include database summaries in plain text, organized in accordance with the http://www.oxfordjournals.org/nar/database/summary/1 template.

ACKNOWLEDGEMENTS

We thank Dr Martine Bernardes-Silva, especially, and the rest of the Oxford University Press team led by Joanna Ventikos and Elisabeth Waelkens for their help in compiling this issue.

Funding for open access charge: Oxford University Press.

Conflict of interest statement . The authors' opinions do not necessarily reflect the views of their respective institutions.

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• Review Article
• Published: 08 April 2024

Nucleic acid-based drugs for patients with solid tumours

• Sebastian G. Huayamares   ORCID: orcid.org/0000-0003-2859-3074 1 , 2 ,
• David Loughrey 1 , 2 ,
• Hyejin Kim   ORCID: orcid.org/0000-0002-7937-2733 1 , 2 ,
• James E. Dahlman   ORCID: orcid.org/0000-0001-7580-436X 1 , 2 &
• Eric J. Sorscher   ORCID: orcid.org/0000-0001-9341-3354 2 , 3 , 4  

Nature Reviews Clinical Oncology ( 2024 ) Cite this article

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• Drug development
• Gene delivery
• Nanoparticles
• Targeted gene repair
• Tumour vaccines

The treatment of patients with advanced-stage solid tumours typically involves a multimodality approach (including surgery, chemotherapy, radiotherapy, targeted therapy and/or immunotherapy), which is often ultimately ineffective. Nucleic acid-based drugs, either as monotherapies or in combination with standard-of-care therapies, are rapidly emerging as novel treatments capable of generating responses in otherwise refractory tumours. These therapies include those using viral vectors (also referred to as gene therapies), several of which have now been approved by regulatory agencies, and nanoparticles containing mRNAs and a range of other nucleotides. In this Review, we describe the development and clinical activity of viral and non-viral nucleic acid-based treatments, including their mechanisms of action, tolerability and available efficacy data from patients with solid tumours. We also describe the effects of the tumour microenvironment on drug delivery for both systemically administered and locally administered agents. Finally, we discuss important trends resulting from ongoing clinical trials and preclinical testing, and manufacturing and/or stability considerations that are expected to underpin the next generation of nucleic acid agents for patients with solid tumours.

Nucleic acid drugs being developed for the treatment of patients with solid tumours can be subdivided into either viral vector-mediated or non-viral nanocarrier-type approaches, with distinct safety and efficacy profiles.

New technologies designed to advance drug development, improve tissue tropism and optimize immune responses are rapidly emerging.

The tumour microenvironment poses several barriers to both intratumoural and systemically administered nucleic acid-based therapies.

When developing nucleic acid treatments, various translational aspects including route of administration, optimal preclinical testing, manufacturing-related aspects and scalability are all important considerations.

Clinical trials investigating anticancer nucleic acid-based agents typically involve repeat dosing and are often tested in combination with standard-of-care therapies.

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Acknowledgements

The authors thank J. Tindall (Emory University, Atlanta, GA) for copyediting the manuscript. The authors also thank B. Kinkead (University of Utah, Salt Lake City, UT) for critical review. The authors gratefully acknowledge funding support from the FDA Office of Orphan Products Division and R01 DE026941.

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Article Contents

Introduction, overall design and workflow of metaboanalyst 6.0, supporting asari and ms2 spectra in lc–ms spectra processing workflow, ms2 peak annotation, causal analysis via two-sample mendelian randomization, dose–response analysis, updated compound database and knowledge libraries, other features, comparison with other tools, data availability, acknowledgements, metaboanalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

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Zhiqiang Pang, Yao Lu, Guangyan Zhou, Fiona Hui, Lei Xu, Charles Viau, Aliya F Spigelman, Patrick E MacDonald, David S Wishart, Shuzhao Li, Jianguo Xia, MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation, Nucleic Acids Research , 2024;, gkae253, https://doi.org/10.1093/nar/gkae253

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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 .

Metabolomics involves the comprehensive study of all small molecules in a biological system. It has diverse applications ranging from basic biochemical research to clinical investigation of diseases, food safety assessment, environmental monitoring, etc. ( 1–5 ). User-friendly and easily accessible bioinformatics tools are essential to deal with the complex data produced from metabolomics studies. MetaboAnalyst is a user-friendly, web-based platform developed to provide comprehensive support for metabolomics data analysis ( 6–10 ). The early versions (1.0–3.0) focused primarily on supporting statistical and functional analysis of targeted metabolomics data. Increasing support for untargeted metabolomics data from liquid chromatography–mass spectrometry (LC–MS) experiments have been gradually introduced in more recent versions of MetaboAnalyst. For instance, version 4.0 implemented a new module to support functional analysis directly from LC–MS peaks, while version 5.0 added an auto-optimized LC–MS spectral processing module that works seamlessly with the functional analysis module. A detailed protocol on how to use different modules for comprehensive analysis of untargeted metabolomics data was published in 2022 ( 11 ). According to Google Analytics, the MetaboAnalyst web server has processed over 2 million jobs, including 33 000 spectral processing jobs over the past 12 months. Many of these jobs are associated with untargeted metabolomics and exposomics studies.

Untargeted metabolomics data generated from high-resolution LC–MS instruments are typically characterized by thousands of peaks with unknown chemical identities. To assist with compound identification, tandem MS (called MS/MS or MS2) spectra are often collected from pooled QC samples during the experiments ( 12 ). The two commonly used MS2 methods are data-dependent acquisition (DDA) and data-independent acquisition (DIA), with sequential window acquisition of all theoretical mass spectra (SWATH) being a promising special case of the latter. DDA data usually have clear associations between the precursor ions and the corresponding MS2 spectra, while DIA data generally require deconvolution of the MS2 data to reconstruct associations with their precursor ions ( 13 ). Incorporating MS2 processing and annotation into untargeted metabolomics workflows can greatly improve compound annotations and functional interpretation.

Exposomics is an emerging field centered on profiling the complete set of exposures individuals encounter across their lifespan, which often involves MS analysis of chemical mixtures traditionally rooted in toxicology and public health ( 4 ). Untargeted LC–MS based metabolomics is increasingly applied to exposomics and toxicology studies. Exposomics data from human cohorts is often associated with complex phenotypic data due to their observational nature. This requires more sophisticated data analysis and visualization methods that can take into consideration of multiple factors or covariates. Exposomics studies typically produce long lists of potential biomarkers that are significantly associated with phenotypes of interest. Identification of causal links from this large number of metabolite-phenotype relations is a natural next step. It has become possible recently with the availability of many metabolomic genome-wide association studies (mGWAS) that link metabolites and genotypes ( 14–16 ). By integrating mGWAS data with comparable GWAS data that associate genotypes with various phenotypes ( 17 ), we can now estimate causal relationships between a metabolite and a phenotype of interest through Mendelian randomization (MR) ( 18 ). Dose-response experiments are often performed to further quantify cause-and-effect relationships. The experiments are often conducted at multiple dose levels using in vitro assays or animal models to calculate dose-response curves for risk assessment of chemical exposures ( 19–21 ).

To address these emerging needs from both the metabolomics and exposomics communities, we have developed MetaboAnalyst version 6.0. This version includes many key features:

A significantly enhanced spectra processing workflow with the addition of asari algorithm for LC–MS spectra processing ( 22 ), as well as support for MS2 (DDA or SWATH-DIA) data processing.

A new module for MS2 spectral database searching for compound identification and results visualization.

A new module for causal analysis between metabolites and phenotypes of interest based on two-sample MR (2SMR).

A new module for dose-response analysis including dose-response curve fitting and benchmark dose (BMD) calculation.

A new module for statistical analysis with complex metadata;

A number of other important updates including: improved functional analysis of untargeted metabolomics data by integrating MS2-based compound identification; updated compound database, pathways and metabolite sets; as well as improved data visualization support across multiple modules.

MetaboAnalyst 6.0 is feely accessible at https://www.metaboanalyst.ca , with comprehensive documentations and updated tutorials. To better engage with our users, a dedicated user forum ( https://omicsforum.ca ) has been operational since May 2022. To dates, this forum contains >4000 posts on ∼700 topics related to different aspects of using MetaboAnalyst.

MetaboAnalyst 6.0 accepts a total of five different data types across various modules encompassing spectra processing, statistical analysis, functional analysis, meta-analysis, and integration with other omics data. Once the data are uploaded, all analysis steps are conducted within a consistent framework including data integrity checks, parameter customization, and results visualization (Figure 1 ). Some of the key features in MetaboAnalyst 6.0 are described below.

MetaboAnalyst 6.0 workflow for targeted and untargeted metabolomics data. Multiple data input types are accepted. Untargeted metabolomics inputs require extra steps for spectra processing and peak annotation. The result table can be used for statistical and functional analysis within a consistent workflow in the same manner as for targeted metabolomics data.

LC–MS spectra processing remains an active research topic in the field of untargeted metabolomics. Many powerful tools have been developed over time, including XCMS ( 23 ), MZmine ( 24 ), MS-DIAL ( 13 ) and asari ( 22 ). In addition to using different peak detection algorithms, most tools require manual parameter tuning to ensure good results. Such practice often leads to results that vary significantly ( 25 ). To mitigate this issue, MetaboAnalyst 5.0 introduced an auto-optimized LC–MS processing pipeline to minimize the parameter-related effects ( 10 , 26 ). The asari software has introduced a set of quality metrics, concepts of mass tracks and composite mass tracks and new algorithmic design to minimize errors in feature correspondence. It requires minimal parameter tuning while achieving much faster computational performance ( 22 ). The asari algorithm is now available in the LC–MS spectra processing options, alongside the traditional approaches.

MS2 spectra processing and metabolite identification are important components of untargeted metabolomics. It is now recognized that MS2 spectral deconvolution is necessary to achieve high-quality compound identification results for both DDA and SWATH-DIA data ( 27–29 ). MetaboAnalyst 6.0 offers an efficient, auto-optimized pipeline for MS2 spectral deconvolution. The DDA data deconvolution method is derived from the DecoID algorithm ( 28 ), which employs a database-dependent regression model to deconvolve contaminated spectra. The SWATH-DIA data deconvolution algorithm is based on the DecoMetDIA method ( 29 ), with the core algorithm re-implemented using a Rcpp/C++ framework to achieve high performance. When MS2 spectra replicates are provided, an extra step will be performed to generate consensus spectra across replicates. The consensus spectra are searched against MetaboAnalyst's curated MS2 reference databases for compound identification based on dot product ( 28 ) or spectral entropy ( 30 ) similarity scores. The complete pipelines for DDA and SWATH-DIA are available from the Spectra Processing [LC–MS w/wo MS2] module.

Raw spectra must be saved in common open formats and uploaded individually as separate zip files. LC–MS spectra data is mandatory, while MS2 is optional. Upon data uploading, MetaboAnalyst 6.0 first validates the status of the MS files. For SWATH-DIA data, the SWATH window design is automatically extracted from the spectra. If the related information is missing, users will be prompted to manually enter the window design. On the parameters setting page, users can choose the auto-optimized centWave algorithm ( 26 ) or the asari algorithm for LC–MS data processing. If MS2 data is included, spectra deconvolution, consensus, and database searching will be performed using the identified MS features as target list. Once the spectra processing is complete, users can explore both MS and MS2 data processing results (Figure 2A - B ) and download the files or directly go to the Functional Analysis module.

Example outputs from MetaboAnalyst 6.0. ( A ) Integrated 3D PCA score and loading plots summarizing the raw spectra processing results. ( B ) An interactive mirror plot showing the MS2 matching result. Matched fragments are marked with a red diamond. ( C ) Functional analysis results with the top four significant pathways labelled. ( D ) A forest plot comparing the effect sizes calculated based on individual SNPs (black) or using all SNPs by different MR methods (red). ( E ) Bar plots of the dose response curve fitting results showing how many times each model type was identified as the best fit. ( F ) A dose-response curve fitting result showing each of the concentration values (black points), the fitted curve (solid blue line), and the estimated benchmark dose (solid red line) with its lower and upper 95% confidence intervals (dashed red lines), respectively.

MS2 data could be acquired independently from MS data acquisition. To accommodate this scenario and offer compatibility with MS2 spectra results from other popular tools such as MS-DIAL, we have added a Peak Annotation [MS2-DDA/DIA] module to allow users to directly upload MS2 spectra for database searching. Users can enter a single MS2 spectrum or upload an MSP or MGF file containing multiple MS2 spectra. For single spectrum searching, users must specify the m/z value of the precursor ion. However, for batch searching based on an MSP file, users do not need to specify the precursors’ m/z values. To ensure timely completion of database searching, the public server processes only 20 spectra for each submission (the first 20 spectra by default). Users can manually specify spectra for searching. After conducting this pilot analysis with 20 spectra, users can download the R command history and use our MetaboAnalystR package to annotate all MS2 spectra ( 26 ).

Multiple databases are available for compound identification. Database searching can be performed based on regular reference MS2 spectra and/or their corresponding neutral loss spectra. The results are visually summarized as mirror plots based on the matching scores (Figure 2B). Users can interactively explore the MS2 database matching results. The molecular formulas for the MS2 peaks in the reference database spectra are predicted using the BUDDY program ( 31 ). Users can download the complete compound identification table together with the mirror plots.

Understanding the causal relationships between metabolites and phenotypes is of great interest in both metabolomics and exposomics. GWAS have established links between genetic variants (e.g. single nucleotide polymorphism, or SNPs) and various phenotypes ( 32 ), while recent mGWAS provide connections between genotypes with metabolites or metabolite concentration changes. It becomes possible to estimate causal relationships between metabolites and a phenotype of interest. If a metabolite is causal for a given disease, genetic variants which influence the levels of that metabolite, either directly through affecting related enzymes or indirectly through influencing lifestyle choices (such as dietary habits), should result in a higher risk of the disease. These causal effects can be estimated through Mendelian randomization (MR) analysis ( 18 ). MR relies on the principle that genetic variants are randomly distributed across populations, similar to how treatments are randomly assigned in clinical trials. By leveraging this random allocation, MR can evaluate whether a relationship between a metabolite and a phenotype is causal, while reducing the impact of confounding factors and reverse causality that often plague observational studies.

MR analysis in MetaboAnalyst is based on the 2SMR approach (using the TwoSampleMR and MRInstruments R packages) which enables application of MR methods using summary statistics from non-overlapping individuals ( 17 , 33 ). Users should first select an exposure (i.e. a metabolite) and an outcome (i.e. a disease) of interest. Based on the selections, the program searches for potential instrumental variables (i.e. SNPs) that are associated with both the metabolite from our large collections of the recent mGWAS studies ( 14 ) and the disease from the OpenGWAS database ( 17 ). The next step is to perform SNP filtering and harmonization to identify independent SNPs through linkage disequilibrium (LD) clumping ( 34 ). When SNPs are absent in the GWAS database, proxy SNPs are identified using LD. In addition, it is critical to harmonize SNPs to make sure effect sizes for the SNPs on both exposures and the outcomes are for the same reference alleles. The last step before conducting MR analysis is to exclude SNPs affecting multiple metabolites to reduce horizontal pleiotropy which occurs when a genetic variant influences the outcome through pathways other than the exposure of interest ( 35 ). MetaboAnalyst's MR analysis page provides diverse statistical methods (currently 12), each of which has its own strengths and limitations. For instance, the weighted median method is robust to the violation of MR assumptions by some of the genetic variants, while Egger regression method is more robust to horizontal pleiotropy. Users can point their mouse over the corresponding question marks beside each method to learn more details.

Dose–response analysis is commonly used in toxicology and pharmacology for understanding how varying concentrations of a chemical can impact a biological system. It plays a pivotal role in risk assessment of chemical exposures ( 36 ). A key output of dose-response analysis is the benchmark dose (BMD), the minimum dose of a substance that produces a clear, low level health risk relative to the control group ( 37 ). Chemicals identified from exposomics are often followed up by dose–response studies to understand their mechanism of action or adverse outcome pathways ( 21 , 38 , 39 ).

Dose–response experiment design includes a control group (dose = 0) and at least three different dose groups, typically with the same number of replicates in each group. The data should be formatted as a csv file with their dose information included as the second row or column. The analysis workflow consists of four main steps: (i) data upload, integrity checking, processing and normalization; (ii) differential analysis to select features that vary with dose levels; (iii) curve fitting on the intensity or concentration values of those selected features against a suite of linear and non-linear models, and (iv) computing BMD values for each feature. The algorithm for dose–response analysis was adapted from the algorithm we developed for transcriptomics BMD analysis ( 40 , 41 ).

Compound database

The compound database has been updated based on HMDB 5.0 ( 42 ), with particular efforts made to synchronize with the IDs of other databases such as KEGG ( 43 ) and PubChem ( 44 ) to improve cross-references during compound mapping and pathway analysis. The compound database was expanded by ∼4000 compounds (after removing ∼10 000 deprecated HMDB entries and adding ∼14 000 new entries).

MS2 reference spectra database.

A total of 12 MS2 reference databases were collected and curated from public resources, including the HMDB experimental MS2 database ( 42 ), the HMDB predicted MS2 database ( 42 ), Global Natural Product Social Molecular Networking (GNPS) database ( 45 ), MoNA ( 46 ), MassBank ( 46 ), MINEs ( 47 ), LipidBlast ( 48 ), RIKEN ( 49 ), ReSpect ( 50 ), BMDMS ( 51 ), VaniyaNP ( 46 ) and the MS-DIAL database (v4.90) ( 52 ). The complete MS2 reference database currently comprises 10 420 215 MS2 records from 1 551 012 unique compounds. We also created a neutral loss spectra database calculated based on the algorithm implemented by the METLIN neutral loss database ( 53 ). The molecular formula of all MS2 fragments were pre-calculated using BUDDY ( 31 ).

Pathway and metabolite set libraries

The KEGG pathway libraries have been updated to their recent version (12/20/2023) via KEGG API. Based on user feedback, the pathway analysis for both targeted and untargeted metabolomics data now supports ∼130 species (up from 28 species in version 5.0), including many new mammals, plants, insects, fungi, and bacteria, etc. We also updated the metabolite set libraries based on HMDB 5.0, MarkerDB ( 54 ), as well as manual curation. For instance, a total of 62 metabolite sets associated with dietary and chemical exposures were added during this process. The metabolite set library also incorporated ∼3700 pathways downloaded from the RaMP-DB ( 55 ).

Statistical analysis with complex metadata

The Statistical Analysis [metadata table] module in MetaboAnalyst 6.0 now provides a comprehensive suite of methods for analyzing and visualizing metabolomics data in relation to various metadata, be it discrete or continuous. Users can quickly assess the correlation patterns among different experimental factors using the metadata overview heatmaps or interactive PCA visualization. The interactive heatmap visualization coupled with hierarchical clustering allows users to easily explore feature abundance variations across different samples and metadata variables. The statistical methods in this module include both univariate linear models with covariate adjustment as well as multivariate methods such as ANOVA Simultaneous Component Analysis ( 56 , 57 ). Random forest is offered for classification with consideration of different metadata variables of interest. More details about this module can be found in our recently published protocol ( 11 ).

Enhanced functional analysis for untargeted metabolomics

Functional analysis of untargeted metabolomics was initially established based on mummichog and Gene Set Enrichment Analysis (GSEA) since MetaboAnalyst 4.0 ( 58 ). It was further enhanced in MetaboAnalyst 5.0 by incorporating retention time into calculating empirical compounds. MetaboAnalyst 6.0 now allows users to upload an LC–MS peak list along with a corresponding MS2-based compound list to filter out unrealistic empirical compounds to further improve the accuracy in functional analysis ( 59 ).

Enhanced data visualization support

We have enhanced the quality of the interactive and synchronized 3D plots across the dimensionality reduction methods (PCA, PLS-DA, sPLS-DA) used in MetaboAnalyst based on the powerful three.js library ( https://threejs.org/ ). New features include customizable backgrounds, data point annotations and confidence ellipsoids (Figure 2A ). We have also implemented interactive plots for clustering heatmaps in the Statistical Analysis modules to better support visual exploration of large data matrices typical in untargeted metabolomics. Both mouse-over and zoom-in functionalities are supported to allow users to examine specific features or patterns of interest. In addition to these enhancements, we also updated the visualization for KEGG’s global metabolic network ( 43 ).

To illustrate the utility of the new features of MetaboAnalyst 6.0, we used a metabolomics dataset collected in-house that aimed at studying glucose-induced insulin secretion in isolated human islets. The dataset contains five samples of high-glucose (16.7 mM) exposures, five samples of low-glucose (2.8 mM) exposures, both for 30 min, and five quality control (QC) samples. The LC-MS spectra were collected using our Q-Exactive Orbitrap platform (Thermo Scientific, Waltham, MA USA), together with three SWATH-DIA acquisitions from the pooled QC. The spectra were first centroided and converted into mzML format using ProteoWizard ( 60 , 61 ) and uploaded to MetaboAnalyst 6.0. LC–MS spectra processing was performed using the asari algorithm. All detected MS1 features were used as a target list for MS2 deconvolution and database searching. A total of 27 209 MS1 features were detected, with 4959 of them identified with at least one potential named chemical identity. Functional analysis using the mummichog algorithm indicated compounds showing significant changes between the high-glucose and low-glucose groups were involved in the C arnitine shuttle , C affeine, Tryptophan , and C oenzyme A metabolism pathways (Figure 2C ). These pathways have been consistently identified in previous studies ( 62–65 ). Finally, we performed a causal analysis on the associations between one of the significant metabolites identified, L-Cystathionine and type 2 diabetes (GWAS ID: finn-b-E4_DM2). The default parameters were used for both SNP filtering and harmonization, as well as MR analysis. Based on these results, a significantly altered cystathionine level was found to have a causal effect on type 2 diabetes (Figure 2D ), which aligns well with a study published recently ( 66 ). This case study highlights how MetaboAnalyst 6.0 allows users to investigate the chemical identities of MS peaks, elucidate associations between metabolites and phenotypes to unveil previously unknown functional insights. To showcase the dose-response analysis module, we utilized a published data collected from BT549 breast cancer cells treated with four different doses of etomoxir ( 21 ). Figure 2E summarizes the results from dose-response modeling. Figure 2F shows an example feature-level BMD calculated based on the fitted curve. The workflow is included as a series of tutorials on our website.

Several web-based tools have been developed to address various aspects of metabolomics data processing, statistical analysis, functional interpretation, and results visualization. Table 1 compares the main features of MetaboAnalyst 6.0 with other popular tools including the previous version, XCMS online ( 23 ), GNPS ( 45 ), Workflow4Metabolomics (W4M) ( 67 ) and MetExplore ( 68 ). For raw data processing, MetaboAnalyst primarily focuses on supporting LC–MS data, whereas W4M also supports GC–MS and NMR raw data processing, and GNPS emphasizes MS2-based compound identification via molecular networks. In comparison, MetaboAnalyst provides an auto-optimized workflow along with an additional algorithm (asari) for efficient LC–MS spectra processing, together with more extensive MS2 spectra libraries for compound identification. In terms of statistical analysis, MetaboAnalyst 6.0 has introduced new modules for dealing with complex metadata, causal analysis and dose–response analysis, while maintaining all other functionalities. MetaboAnalyst contains unique features for enrichment and pathway analysis, and these strengths were further improved in version 6.0, with the addition of unique functions and supports for more species. For network analysis and integration, MetExplore specializes in metabolic network visualization and integration with other omics. These features are addressed by our companion tool, OmicsNet ( 69 ). Overall, MetaboAnalyst 6.0 continues to be the most comprehensive tool for metabolomics data processing, analysis and interpretation.

Comparison of MetaboAnalyst 6.0 with its previous version and other common web-based metabolomics tools. Symbols used for feature evaluations with ‘√’ for present, ‘-’ for absent, and ‘+’ for a more quantitative assessment (more ‘+’ indicate better support)

• XCMS online: https://xcmsonline.scripps.edu/ .

• GNPS: https://gnps.ucsd.edu/ .

• Workflow4Metabolomics (W4M): https://workflow4metabolomics.org/ .

• MetExplore: https://metexplore.toulouse.inra.fr/metexplore2/

By incorporating a new MS2 data processing workflow, MetaboAnalyst 6.0 now offers a web-based, end-to-end platform for metabolomics data analysis. The workflow spans from raw MS spectra processing to compound identification to functional analysis. A key motivation in developing version 6.0 was to support the data analysis needs emerging from exposomics and follow-up validation studies. The new statistical analysis module specifically takes into account of complex metadata to better identify robust associations. From these associations, users can perform causal analysis based on 2SMR to narrow down candidate compounds. The remaining compounds can be validated through dose-response studies based on in vitro or animal models. Our case study highlights the streamlined analysis workflow from raw spectra processing to compound annotation, to functional interpretation, and finally to causal insights. In conclusion, MetaboAnalyst 6.0 is a user-friendly platform for comprehensive analysis of metabolomics data and help address emerging needs from recent exposomics research. For future directions, we will continue to improve metabolome annotations, better integrate with other omics data, and explore new ways to interact with users via generative artificial intelligence technologies ( 70–73 ).

MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca . No log in required.

Human islets for research were provided by the Alberta Diabetes Institute IsletCore at the University of Alberta in Edmonton with the assistance of the Human Organ Procurement and Exchange (HOPE) program, Trillium Gift of Life Network (TGLN), and other Canadian organ procurement organizations. Islet isolation was approved by the Human Research Ethics Board at the University of Alberta (Pro00013094). All donors’ families gave informed consent for the use of pancreatic tissue in research.

This research was funded by Genome Canada, Canadian Foundation for Innovation (CFI), US National Institutes of Health (U01 CA235493), Canadian Institutes of Health Research (CIHR), Juvenile Diabetes Research Foundation (JDRF), Natural Sciences and Engineering Research Council of Canada (NSERC), and Diabetes Canada. Funding for open access charge: NSERC.

Conflict of interest statement . J. Xia is the founder of XiaLab Analytics.

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