MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Combining predictive and analytical methods to elucidate pharmaceutical biotransformation in activated sludge

While man-made chemicals in the environment are ubiquitous and a potential threat to human health and ecosystem integrity, the environmental fate of chemical contaminants such as pharmaceuticals is often poorly understood. Biodegradation processes driven by microbial communities convert chemicals into transformation products (TPs) that may themselves have adverse ecological effects. The detection of TPs formed during biodegradation has been continuously improved thanks to the development of TP prediction algorithms and analytical workflows. Here, we contribute to this advance by (i) reviewing past applications of TP identification workflows, (ii) applying an updated workflow for TP prediction to 42 pharmaceuticals in biodegradation experiments with activated sludge, and (iii) benchmarking 5 different pathway prediction models, comprising 4 prediction models trained on different datasets provided by enviPath, and the state-of-the-art EAWAG pathway prediction system. Using the updated workflow, we could tentatively identify 79 transformation products for 31 pharmaceutical compounds. Compared to previous works, we have further automatized several steps that were previously performed by hand. By benchmarking the enviPath prediction system on experimental data, we demonstrate the usefulness of the pathway prediction tool to generate suspect lists for screening, and we propose new avenues to improve their accuracy. Moreover, we provide a well-documented workflow that can be (i) readily applied to detect transformation products in activated sludge and (ii) potentially extended to other environmental studies.

UPLC-MS based plasma metabolomics and lipidomics reveal alterations associated with IgG4-related disease

OBJECTIVE

The pathogenesis of IgG4-related disease (IgG4-RD) remains unclear. Metabolomic profiling of IgG4-RD patients offers an opportunity to identify novel pathophysiological targets and biomarkers. This study aims to identify potential plasma biomarkers associated with IgG4-RD.

METHODS

Thirty newly diagnosed IgG4-RD patients, age-matched healthy controls and post-treated IgG4-RD patients were enrolled. Patients' clinical data, laboratory parameters and plasma were collected. Plasma was measured for ultraperformance liquid chromatography-tandem mass spectrometry based metabolomics and lipidomics profiling. Multivariate and univariate statistical analyses were conducted to identify potential biomarkers. The receiver operating characteristic and the correlations between biomarkers and clinical parameters were investigated.

RESULTS

The plasma metabolites are altered among healthy controls, newly diagnosed IgG4-RD and post-treated IgG4-RD groups. Of the identified features, eight metabolites were significantly perturbed in the IgG4-RD group, including glyceric acid 1,3-biphosphate (1,3-BPG), uridine triphosphate (UTP), uridine diphosphate glucose (UDP-Glc) or uridine diphosphate galactose (UDP-Gal), lysophospholipids, linoleic acid derivatives and ceramides. Receiver operating characteristic analysis indicated that UTP, UDP-Glc/UDP-Gal and LysoPC (18:1) had high sensitivity and specificity in diagnosis of IgG4-RD. A Pearson correlation analysis showed that 1,3-BPG and UTP were strongly correlated with clinical parameters.

CONCLUSION

IgG4-RD patients have a unique plasma metabolomic profile compared with healthy controls. Our study suggested that metabolomic profiling may provide important insights into pathophysiology and testable biomarkers for diagnosis of IgG4-RD.

CoMNRank: An integrated approach to extract and prioritize human microbial metabolites from MEDLINE records

MOTIVATION

Trillions of bacteria in human body (human microbiota) affect human health and diseases by controlling host functions through small molecule metabolites.An accurate and comprehensive catalog of the metabolic output from human microbiota is critical for our deep understanding of how microbial metabolism contributes to human health.The large number of published biomedical research articles is a rich resource of microbiome studies.However, automatically extracting microbial metabolites from free-text documents and differentiating them from other human metabolites is a challenging task.Here we developed an integrated approach called Co-occurrence Metabolite Network Ranking (CoMNRank) by combining named entity extraction, network construction and topic sensitive network-based prioritization to extract and prioritize microbial metabolites from biomedical articles.

METHODS

The text data included 28,851,232 MEDLINE records.CoMNRank consists of three steps: (1) extraction of human metabolites from MEDLINE records; (2) construction of a weighted co-occurrence metabolite network (CoMN); (3) prioritization and differentiation of microbial metabolites from other human metabolites.

RESULTS

For the first step of CoMNRank, we extracted 11,846 human metabolites from MEDLINE articles, with a baseline performance of precision of 0.014, recall of 0.959 and F1 of 0.028.We then constructed a weighted CoMN of 6,996 nodes and 986,186 edges.CoMNRank effectively prioritized microbial metabolites: the precision of top ranked metabolites is 0.45, a 31-fold enrichment as compared to the overall precision of 0.014.Manual curation of top 100 metabolites showed a true precision of 0.67, among which 48% true positives are not captured by existing databases.

CONCLUSION

Our study sets the foundation for future tasks of microbial entity and relationship extractions as well as data-driven studies of how microbial metabolism contributes to human health and diseases.

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

Many diseases are driven by gene-environment interactions. One important environmental factor is the metabolic output of human gut microbiota. A comprehensive catalog of human metabolites originated in microbes is critical for data-driven approaches to understand how microbial metabolism contributes to human health and diseases. Here we present a novel integrated approach to automatically extract and analyze microbial metabolites from 28 million published biomedical records. First, we classified 28,851,232 MEDLINE records into microbial metabolism-related or not. Second, candidate microbial metabolites were extracted from the classified texts. Third, we developed signal prioritization algorithms to further differentiate microbial metabolites from metabolites originated from other resources. Finally, we systematically analyzed the interactions between extracted microbial metabolites and human genes. A total of 11,846 metabolites were extracted from 28 million MEDLINE articles. The combined text classification and signal prioritization significantly enriched true positives among top: manual curation of top 100 metabolites showed a true precision of 0.55, representing a significant 38.3-fold enrichment as compared to the precision of 0.014 for baseline extraction. More importantly, 29% extracted microbial metabolites have not been captured by existing databases. We performed data-driven analysis of the interactions between the extracted microbial metabolite and human genetics. This study represents the first effort towards automatically extracting and prioritizing microbial metabolites from published biomedical literature, which can set a foundation for future tasks of microbial metabolite relationship extraction from literature and facilitate data-driven studies of how microbial metabolism contributes to human diseases.

Challenges in the construction of knowledge bases for human microbiome-disease associations

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels.

Results

We used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes.

Conclusions

Our study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels.

GC/MS-Based Metabolomics Reveals Biomarkers in Asthma Murine Model Modulated by Opuntia humifusa

GC/MS coupled with multivariate statistical analysis was performed to identify marker metabolites in serum of mice after healing ovalbumin- (OVA-) induced asthma using Opuntia humifusa. Principal component analysis (PCA) score plot showed separation among groups, with metabolite profiles of serum showing differences according to various treatments for the asthma murine model. Levels of stearic acid and arachidic acid were significantly lower in the serum from OVA-induced group than those from the control group. Dexamethasone treatment group was characterized by higher serum levels of urea, myristic acid, and palmitic acid along with lower levels of aspartic acid compared to OVA-induced group. O. humifusa treatment mice groups showed dose-proportional higher levels of urea and glycerol than OVA-induced group. These results highlight that GC/MS-based metabolomics is a powerful technique for identifying molecular markers of asthma.

Metabolomics research on potential role for 9-cis-retinoic acid in breast cancer progression

Deciphering the molecular networks that discriminate organ-confined breast cancer from metastatic breast cancer may lead to the identification of critical biomarkers for breast cancer invasion and aggressiveness. Here metabolomics, a global study of metabolites, has been applied to explore the metabolic alterations that characterize breast cancer progression. We profiled a total of 693 metabolites across 87 serum samples related to breast cancer (46 clinically localized and 41 metastatic breast cancer) and 49 normal samples. These unbiased metabolomic profiles were able to distinguish normal individuals, clinically localized and metastatic breast cancer patients. 9-cis-Retinoic acid, an isomer of all-trans retinoic acid, was identified as a differential metabolite that significantly decreased during breast cancer progression to metastasis, and its levels were also reduced in urine samples from biopsy-positive breast cancer patients relative to biopsy-negative individuals and in invasive breast cancer cells relative to benign MCF-10A cells. The addition of exogenous 9-cis-retinoic acid to MDA-MB-231 cells and knockdown of aldehyde dehydrogenase 1 family member A1, a regulatory enzyme for 9-cis-retinoic acid, remarkably impaired cell invasion and migration, presumably through preventing the key regulator cofilin from activation and inhibiting MMP2 and MMP9 expression. Taken together, our study showed the potential inhibitory role for 9-cis-retinoic acid in breast cancer progression by attenuating cell invasion and migration.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.