Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans.

Emerging evidence indicates a central role for the microbiome in immunity. However, causal evidence in humans is sparse. Here, we administered broad-spectrum antibiotics to healthy adults prior and subsequent to seasonal influenza vaccination. Despite a 10,000-fold reduction in gut bacterial load and long-lasting diminution in bacterial diversity, antibody responses were not significantly affected. However, in a second trial of subjects with low pre-existing antibody titers, there was significant impairment in H1N1-specific neutralization and binding IgG1 and IgA responses. In addition, in both studies antibiotics treatment resulted in (1) enhanced inflammatory signatures (including AP-1/NR4A expression), observed previously in the elderly, and increased dendritic cell activation; (2) divergent metabolic trajectories, with a 1,000-fold reduction in serum secondary bile acids, which was highly correlated with AP-1/NR4A signaling and inflammasome activation. Multi-omics integration revealed significant associations between bacterial species and metabolic phenotypes, highlighting a key role for the microbiome in modulating human immunity.

Metabolic Phenotypes of Response to Vaccination in Humans.

Herpes zoster (shingles) causes significant morbidity in immune compromised hosts and older adults. Whereas a vaccine is available for prevention of shingles, its efficacy declines with age. To help to understand the mechanisms driving vaccinal responses, we constructed a multiscale, multifactorial response network (MMRN) of immunity in healthy young and older adults immunized with the live attenuated shingles vaccine Zostavax. Vaccination induces robust antigen-specific antibody, plasmablasts, and CD4+ T cells yet limited CD8+ T cell and antiviral responses. The MMRN reveals striking associations between orthogonal datasets, such as transcriptomic and metabolomics signatures, cell populations, and cytokine levels, and identifies immune and metabolic correlates of vaccine immunity. Networks associated with inositol phosphate, glycerophospholipids, and sterol metabolism are tightly coupled with immunity. Critically, the sterol regulatory binding protein 1 and its targets are key integrators of antibody and T follicular cell responses. Our approach is broadly applicable to study human immunity and can help to identify predictors of efficacy as well as mechanisms controlling immunity to vaccination.

Autophagy is essential for effector CD8(+) T cell survival and memory formation.

The importance of autophagy in the generation of memory CD8(+) T cells in vivo is not well defined. We report here that autophagy was dynamically regulated in virus-specific CD8(+) T cells during acute infection of mice with lymphocytic choriomeningitis virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8(+) T cells and was then upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of the relationship between T cell activation and autophagy.

Molecular signatures of antibody responses derived from a systems biology study of five human vaccines.

Many vaccines induce protective immunity via antibodies. Systems biology approaches have been used to determine signatures that can be used to predict vaccine-induced immunity in humans, but whether there is a 'universal signature' that can be used to predict antibody responses to any vaccine is unknown. Here we did systems analyses of immune responses to the polysaccharide and conjugate vaccines against meningococcus in healthy adults, in the broader context of published studies of vaccines against yellow fever virus and influenza virus. To achieve this, we did a large-scale network integration of publicly available human blood transcriptomes and systems-scale databases in specific biological contexts and deduced a set of transcription modules in blood. Those modules revealed distinct transcriptional signatures of antibody responses to different classes of vaccines, which provided key insights into primary viral, protein recall and anti-polysaccharide responses. Our results elucidate the early transcriptional programs that orchestrate vaccine immunity in humans and demonstrate the power of integrative network modeling.

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

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

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

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

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

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

Systems biology of vaccination for seasonal influenza in humans.

Here we have used a systems biology approach to study innate and adaptive responses to vaccination against influenza in humans during three consecutive influenza seasons. We studied healthy adults vaccinated with trivalent inactivated influenza vaccine (TIV) or live attenuated influenza vaccine (LAIV). TIV induced higher antibody titers and more plasmablasts than LAIV did. In subjects vaccinated with TIV, early molecular signatures correlated with and could be used to accurately predict later antibody titers in two independent trials. Notably, expression of the kinase CaMKIV at day 3 was inversely correlated with later antibody titers. Vaccination of CaMKIV-deficient mice with TIV induced enhanced antigen-specific antibody titers, which demonstrated an unappreciated role for CaMKIV in the regulation of antibody responses. Thus, systems approaches can be used to predict immunogenicity and provide new mechanistic insights about vaccines.

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

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

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

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

The amino acid sensor GCN2 controls gut inflammation by inhibiting inflammasome activation.

The integrated stress response (ISR) is a homeostatic mechanism by which eukaryotic cells sense and respond to stress-inducing signals, such as amino acid starvation. General controlled non-repressed (GCN2) kinase is a key orchestrator of the ISR, and modulates protein synthesis in response to amino acid starvation. Here we demonstrate in mice that GCN2 controls intestinal inflammation by suppressing inflammasome activation. Enhanced activation of ISR was observed in intestinal antigen presenting cells (APCs) and epithelial cells during amino acid starvation, or intestinal inflammation. Genetic deletion of Gcn2 (also known as Eif2ka4) in CD11c(+) APCs or intestinal epithelial cells resulted in enhanced intestinal inflammation and T helper 17 cell (TH17) responses, owing to enhanced inflammasome activation and interleukin (IL)-1ß production. This was caused by reduced autophagy in Gcn2(-/-) intestinal APCs and epithelial cells, leading to increased reactive oxygen species (ROS), a potent activator of inflammasomes. Thus, conditional ablation of Atg5 or Atg7 in intestinal APCs resulted in enhanced ROS and TH17 responses. Furthermore, in vivo blockade of ROS and IL-1ß resulted in inhibition of TH17 responses and reduced inflammation in Gcn2(-/-) mice. Importantly, acute amino acid starvation suppressed intestinal inflammation via a mechanism dependent on GCN2. These results reveal a mechanism that couples amino acid sensing with control of intestinal inflammation via GCN2.

A Practical Guide to Metabolomics Software Development.

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

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

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.

Systems vaccinology.

Vaccination is one of the greatest triumphs of modern medicine, yet we remain largely ignorant of the mechanisms by which successful vaccines stimulate protective immunity. Two recent advances are beginning to illuminate such mechanisms: realization of the pivotal role of the innate immune system in sensing microbes and stimulating adaptive immunity, and advances in systems biology. Recent studies have used systems biology approaches to obtain a global picture of the immune responses to vaccination in humans. This has enabled the identification of early innate signatures that predict the immunogenicity of vaccines, and identification of potentially novel mechanisms of immune regulation. Here, we review these advances and critically examine the potential opportunities and challenges posed by systems biology in vaccine development.

Metabolic perturbations in classic galactosemia beyond the Leloir pathway: Insights from an untargeted metabolomic study.

Classic galactosemia (CG) is an autosomal recessive disorder that impacts close to 1/50000 live births in the United States, with varying prevalence in other countries. Following exposure to milk, which contains high levels of galactose, affected infants may experience rapid onset and progression of potentially lethal symptoms. With the benefit of early diagnosis, generally by newborn screening, and immediate and lifelong dietary restriction of galactose, the acute sequelae of disease can be prevented or resolved. However, long-term complications are common, and despite many decades of research, the bases of these complications remain unexplained. As a step toward defining the underlying pathophysiology of long-term outcomes in CG, we applied an untargeted metabolomic approach with mass spectrometry and dual liquid chromatography, comparing thousands of small molecules in plasma samples from 183 patients and 31 controls. All patients were on galactose-restricted diets. Using both univariate and multivariate statistical methods, we identified 252 differentially abundant features from anion exchange chromatography and 167 differentially abundant features from C18 chromatography. Mapping these discriminatory features to putative metabolites and biochemical pathways revealed 14 significantly perturbed pathways; these included multiple redox, amino acid, and mitochondrial pathways, among others. Finally, we tested whether any discriminatory features also distinguished cases with mild vs more severe long-term outcomes and found multiple candidates, of which one achieved false discovery rate-adjusted q < 0.1. These results extend substantially from prior targeted studies of metabolic perturbation in CG and offer a new approach to identifying candidate modifiers and targets for intervention.

Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information.

Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data-driven and knowledge-based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of cardiovascular disease. Our simulations demonstrate that the structured sparse CCA methods outperform several existing sparse CCA methods in selecting relevant genes and metabolites when structural information is informative and are robust to mis-specified structural information. Our analysis of the PHI study reveals that a number of gene and metabolic pathways including some known to be associated with cardiovascular diseases are enriched in the set of genes and metabolites selected by our proposed approach.

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks.

False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria.

BACKGROUND: Plasmodium vivax is one of the leading causes of malaria worldwide. Infections with this parasite cause diverse clinical manifestations, and recent studies revealed that infections with P. vivax can result in severe and fatal disease. Despite these facts, biological traits of the host response and parasite metabolism during P. vivax malaria are still largely underexplored. Parasitemia is clearly related to progression and severity of malaria caused by P. falciparum, however the effects of parasitemia during infections with P. vivax are not well understood. RESULTS: We conducted an exploratory study using a high-resolution metabolomics platform that uncovered significant associations between parasitemia levels and plasma metabolites from 150 patients with P. vivax malaria. Most plasma metabolites were inversely associated with higher levels of parasitemia. Top predicted metabolites are implicated into pathways of heme and lipid metabolism, which include biliverdin, bilirubin, palmitoylcarnitine, stearoylcarnitine, phosphocholine, glycerophosphocholine, oleic acid and omega-carboxy-trinor-leukotriene B4. CONCLUSIONS: The abundance of several plasma metabolites varies according to the levels of parasitemia in patients with P. vivax malaria. Moreover, our data suggest that the host response and/or parasite survival might be affected by metabolites involved in the degradation of heme and metabolism of several lipids. Importantly, these data highlight metabolic pathways that may serve as targets for the development of new antimalarial compounds.

Systems biological approaches to measure and understand vaccine immunity in humans.

Recent studies have demonstrated the utility of using systems approaches to identify molecular signatures that can be used to predict vaccine immunity in humans. Such approaches are now being used extensively in vaccinology, and are beginning to yield novel insights about the molecular networks driving vaccine immunity. In this review, we present a broad review of the methodologies involved in these studies, and discuss the promise and challenges involved in this emerging field of "systems vaccinology."