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 is 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.

Trackable and scalable LC-MS metabolomics data processing using asari.

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination.

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

Ketogenic diets slow melanoma growth in vivo regardless of tumor genetics and metabolic plasticity.

BACKGROUND: Growing evidence supports the use of low-carbohydrate/high-fat ketogenic diets as an adjunctive cancer therapy. However, it is unclear which genetic, metabolic, or immunological factors contribute to the beneficial effect of ketogenic diets. Therefore, we investigated the effect of ketogenic diets on the progression and metabolism of genetically and metabolically heterogeneous melanoma xenografts, as well as on the development of melanoma metastases in mice with a functional immune system. METHODS: Mice bearing BRAF mutant, NRAS mutant, and wild-type melanoma xenografts as well as mice bearing highly metastatic melanoma allografts were fed with a control diet or ketogenic diets, differing in their triglyceride composition, to evaluate the effect of ketogenic diets on tumor growth and metastasis. We performed an in-depth targeted metabolomics analysis in plasma and xenografts to elucidate potential antitumor mechanisms in vivo. RESULTS: We show that ketogenic diets effectively reduced tumor growth in immunocompromised mice bearing genetically and metabolically heterogeneous human melanoma xenografts. Furthermore, the ketogenic diets exerted a metastasis-reducing effect in the immunocompetent syngeneic melanoma mouse model. Targeted analysis of plasma and tumor metabolomes revealed that ketogenic diets induced distinct changes in amino acid metabolism. Interestingly, ketogenic diets reduced the levels of alpha-amino adipic acid, a biomarker of cancer, in circulation to levels observed in tumor-free mice. Additionally, alpha-amino adipic acid was reduced in xenografts by ketogenic diets. Moreover, the ketogenic diets increased sphingomyelin levels in plasma and the hydroxylation of sphingomyelins and acylcarnitines in tumors. CONCLUSIONS: Ketogenic diets induced antitumor effects toward melanoma regardless of the tumors´ genetic background, its metabolic signature, and the host immune status. Moreover, ketogenic diets simultaneously affected multiple metabolic pathways to create an unfavorable environment for melanoma cell proliferation, supporting their potential as a complementary nutritional approach to melanoma therapy.

Targeted Metabolomics Identifies Plasma Biomarkers in Mice with Metabolically Heterogeneous Melanoma Xenografts.

Melanomas are genetically and metabolically heterogeneous, which influences therapeutic efficacy and contributes to the development of treatment resistance in patients with metastatic disease. Metabolite phenotyping helps to better understand complex metabolic diseases, such as melanoma, and facilitates the development of novel therapies. Our aim was to characterize the tumor and plasma metabolomes of mice bearing genetically different melanoma xenografts. We engrafted the human melanoma cell lines A375 (BRAF mutant), WM47 (BRAF mutant), WM3000 (NRAS mutant), and WM3311 (BRAF, NRAS, NF1 triple-wildtype) and performed a broad-spectrum targeted metabolomics analysis of tumor and plasma samples obtained from melanoma-bearing mice as well as plasma samples from healthy control mice. Differences in ceramide and phosphatidylcholine species were observed between melanoma subtypes irrespective of the genetic driver mutation. Furthermore, beta-alanine metabolism differed between melanoma subtypes and was significantly enriched in plasma from melanoma-bearing mice compared to healthy mice. Moreover, we identified beta-alanine, p-cresol sulfate, sarcosine, tiglylcarnitine, two dihexosylceramides, and one phosphatidylcholine as potential melanoma biomarkers in plasma. The present data reflect the metabolic heterogeneity of melanomas but also suggest a diagnostic biomarker signature for melanoma screening.

Role of coenzymes in cancer metabolism.

Cancer is a heterogeneous set of diseases characterized by the rewiring of cellular signaling and the reprogramming of metabolic pathways to sustain growth and proliferation. In past decades, studies were focused primarily on the genetic complexity of cancer. Recently, increasing number of studies have discovered several mutations among metabolic enzymes in different tumor cells. Most of the enzymes are regulated by coenzymes, organic cofactors, that function as intermediate carrier of electrons or functional groups that are transferred during the reaction. However, the precise role of cofactors is not well elucidated. In this review, we discuss several metabolic enzymes associated to cancer metabolism rewiring, whose inhibition may represent a therapeutic target. Such enzymes, upon expression or inhibition, may impact also the coenzymes levels, but only in few cases, it was possible to direct correlate coenzymes changes with a specific enzyme. In addition, we also summarize an up-to-date information on biological role of some coenzymes, preclinical and clinical studies, that have been carried out in various cancers and their outputs.