Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.

Glycosylation affects structure, folding, and function of numerous proteins. Aberrant glycosylation has been shown to be associated with different diseases. A wide range of analytical methods is available for glycan analysis of antibodies (mainly IgG), but analysis of plasma glycans is less established due to additional challenges encountered with higher complexity of the sample. Here we describe development and optimization of a high-throughput sample preparation method for hydrophilic interaction liquid chromatography and ultra-performance liquid chromatography analysis of plasma N-glycans. Clean-up of labeled glycans was found to be the largest source of variation, and we tested cellulose, silica gel, Bio-Gel, and hydrophilic GHP filter as stationary phases for solid-phase extraction. All stationary phases were shown to be suitable for purification of labeled glycans, but GHP filter plate in combination with cold 96% acetonitrile had the highest reproducibility and was easiest to work with. The method was further optimized with Plackett—Burman screening design and validated in terms of analysis of major step variation and between-day and between-person variation. The developed method is fast, cost-effective, and easy to perform, and it has very good reproducibility during long period of time, enabling the detection of biological variability of the plasma N-glycome.

Structural studies of integral membrane proteins have been limited by the intrinsic conformational flexibility and the need to stabilize the proteins in solution. Stabilization by mutagenesis was very successful for structural biology of G protein-coupled receptors (GPCRs). However, it requires heavy protein engineering and may introduce structural deviations. Here we describe the use of specific calixarenes-based detergents for native GPCR stabilization. Wild type, full length human adenosine A_2A receptor was used to exemplify the approach. We could stabilize native, glycosylated, non-aggregated and homogenous A_2AR that maintained its ligand binding capacity. The benefit of the preparation for fragment screening, using the Saturation-Transfer Difference nuclear magnetic resonance (STD-NMR) experiment is reported. The binding of the agonist adenosine and the antagonist caffeine were observed and competition experiments with CGS-21680 and ZM241385 were performed, demonstrating the feasibility of the STD-based fragment screening on the native A_2A receptor. Interestingly, adenosine was shown to bind a second binding site in the presence of the agonist CGS-21680 which corroborates published results obtained with molecular dynamics simulation. Fragment-like compounds identified using STD-NMR showed antagonistic effects on A_2AR in the cAMP cellular assay. Taken together, our study shows that stabilization of native GPCRs represents an attractive approach for STD-based fragment screening and drug design.

Glycans expand the structural complexity of proteins by several orders of magnitude, resulting in a tremendous analytical challenge when including them in biomedical research. Recent glycobiological research is painting a picture in which glycans represent a crucial structural and functional component of the majority of proteins, with alternative glycosylation of proteins and lipids being an important regulatory mechanism in many biological and pathological processes. Since interindividual differences in glycosylation are extensive, large studies are needed to map the structures and to understand the role of glycosylation in human (patho)physiology. Driven by these challenges, methods have emerged, which can tackle the complexity of glycosylation in thousands of samples, also known as high-throughput (HT) glycomics. For facile dissemination and implementation of HT glycomics technology, the sample preparation, analysis, as well as data mining, need to be stable over a long period of time (months/years), amenable to automation, and available to non-specialized laboratories. Current HT glycomics methods mainly focus on protein N-glycosylation and allow to extensively characterize this subset of the human glycome in large numbers of various biological samples. The ultimate goal in HT glycomics is to gain better knowledge and understanding of the complete human glycome using methods that are easy to adapt and implement in (basic) biomedical research. Aiming to promote wider use and development of HT glycomics, here, we present currently available, emerging, and prospective methods and some of their applications, revealing a largely unexplored molecular layer of the complexity of life.

While N-linked glycosylation has been extensively studied in the context of inflammatory and metabolic disorders, its relationship with major depressive disorder (MDD) and antidepressant treatment response has not been investigated. In our exploratory study, we analysed N-glycan profiles in blood plasma samples collected from MDD patients (n = 18) and found gender-dependent correlations with severity of depressive symptoms prior to initiating antidepressant treatment. In addition, several N-glycosylation traits showed gender-dependent associations with clinical antidepressant response. Follow up proteomics analysis in peripheral blood mononuclear cells (PBMCs) collected from MDD patients (n = 20) identified baseline and post-antidepressant treatment pathway differences between responder and non-responder patients. Reactome data analysis further delineated potential biological reaction differences between responder and non-responder patients. Our preliminary results suggest that specific glycosylation traits are associated with depressive symptom severity and antidepressant response and may be of use as biomarkers.

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays “The Molecular Human”. We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to “The Molecular Human” (http://comics.metabolomix.com), providing interactive data exploration and hypotheses generation possibilities.