See Covering the Cover synopsis on page 740.OBJECTIVE: Lipidomic changes were causally linked to metabolic diseases, but the scenario for colorectal cancer (CRC) is less clear. We investigated the CRC lipidome for putative tumor-specific alterations through analysis of 3 independent retrospective patient cohorts from 2 clinical centers, to derive a clinically useful signature. DESIGN: Quantitative comprehensive lipidomic analysis was performed using direct infusion electrospray ionization coupled with tandem mass spectrometry (ESI-MS/MS) and high-resolution mass spectrometry (HR-MS) on matched nondiseased mucosa and tumor tissue in a discovery cohort (n ¼ 106). Results were validated in 2 independent cohorts (n ¼ 28, and n ¼ 20), associated with genomic and clinical data, and lipidomic data from a genetic mouse tumor model (Apc 1638N ). RESULTS: Significant differences were found between tumor and normal tissue for glycero-, glycerophospho-, and sphingolipids in the discovery cohort. Comparison to the validation collectives unveiled that glycerophospholipids showed high interpatient variation and were strongly affected by preanalytical conditions, whereas glycero-and sphingolipids appeared more robust. Signatures of sphingomyelin and triacylglycerol (TG) species significantly differentiated cancerous from nondiseased tissue in both validation studies. Moreover, lipogenic enzymes were significantly up-regulated in CRC, and FASN gene expression was prognostically detrimental. The TG profile was significantly associated with postoperative disease-free survival and lymphovascular invasion, and was essentially conserved in murine digestive cancer, but not associated with microsatellite status, KRAS or BRAF mutations, or T-cell infiltration. CONCLUSION: Analysis of the CRC lipidome revealed a robust TG-species signature with prognostic potential. A better understanding of the cancerassociated glycerolipid and sphingolipid metabolism may lead to novel therapeutic strategies.

Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach. Validation is performed using data from a GWAS and results from three in vitro experiments. We show that one predicted reaction is enzymatically feasible and that one rejected reaction does not occur in vitro. Moreover, in contrast to previous knowledge, enzymes involved in our predictions colocalize in the Golgi of two cell lines, further confirming the in silico predictions.

Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc binding region of IgGs, responsible for the antibody’s effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC–ESI-MS)—measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e., ST6GAL1, B4GALT1, FUT8, and MGAT3), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring RUNX3, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the RUNX family are cross-regulated, and RUNX3 is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within RUNX3, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

This paper presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results, and visualizations. maplet’s key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. Availability The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.