We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

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

We used a systems biological approach to study innate and adaptive responses to influenza vaccination in humans, during 3 consecutive influenza seasons. Healthy adults were vaccinated with inactivated (TIV) or live attenuated (LAIV) influenza vaccines. TIV induced greater antibody titers and enhanced numbers of plasmablasts than LAIV. In TIV vaccinees, early molecular signatures correlated with, and accurately predicted, later antibody titers in two independent trials. Interestingly, the expression of Calcium/calmodulin-dependent kinase IV (CamkIV) at day 3 was inversely correlated with later antibody titers. Vaccination of CamkIV −/− mice with TIV induced enhanced antigen-specific antibody titers, demonstrating an unappreciated role for CaMKIV in the regulation of antibody responses. Thus systems approaches can predict immunogenicity, and reveal new mechanistic insights about vaccines.

Many vaccines induce protective immunity via antibodies. Recent studies have used systems biological approaches to determine signatures that predict vaccine immunity in humans, but whether there is a ‘universal signature’ that can predict antibody responses to any vaccine, is unknown. Here we performed systems analyses of immune responses to the meningococcal polysaccharide and conjugate vaccines in healthy adults, in the broader context of our previous studies with the yellow fever and two influenza vaccines. To achieve this, we performed a large-scale network integration of public human blood transcriptomes, and systems-scale databases in specific biological contexts, and deduced a set of blood transcription modules. These modules revealed distinct transcriptional signatures of antibody responses to different classes of vaccines providing key insights into primary viral, protein recall and anti-polysaccharide responses. These results illuminate the early transcriptional programs orchestrating vaccine immunity in humans, and demonstrate the power of integrative network modeling.

The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.