Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow.

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

Ten quick tips for building FAIR workflows.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

M-protein diagnostics in multiple myeloma patients using ultra-sensitive targeted mass spectrometry and an off-the-shelf calibrator.

OBJECTIVES: Minimal residual disease status in multiple myeloma is an important prognostic biomarker. Recently, personalized blood-based targeted mass spectrometry (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to measure minimal residual disease. However, quantification of MS-MRD requires a unique calibrator for each patient. The use of patient-specific stable isotope labelled (SIL) peptides is relatively costly and time-consuming, thus hindering clinical implementation. Here, we introduce a simplification of MS-MRD by using an off-the-shelf calibrator. METHODS: SILuMAB-based MS-MRD was performed by spiking a monoclonal stable isotope labeled IgG, SILuMAB-K1, in the patient serum. The abundance of both M-protein-specific peptides and SILuMAB-specific peptides were monitored by mass spectrometry. The relative ratio between M-protein peptides and SILuMAB peptides allowed for M-protein quantification. We assessed linearity, sensitivity and reproducibility of SILuMAB-based MS-MRD in longitudinally collected sera from the IFM-2009 clinical trial. RESULTS: A linear dynamic range was achieved of over 5 log scales, allowing for M-protein quantification down to 0.001 g/L. The inter-assay CV of SILuMAB-based MS-MRD was on average 11 %. Excellent concordance between SIL- and SILuMAB-based MS-MRD was shown (R2>0.985). Additionally, signal intensity of spiked SILuMAB can be used for quality control purpose to assess system performance and incomplete SILuMAB digestion can be used as quality control for sample preparation. CONCLUSIONS: Compared to SIL peptides, SILuMAB-based MS-MRD improves the reproducibility, turn-around-times and cost-efficacy of MS-MRD without diminishing its sensitivity and specificity. Furthermore, SILuMAB can be used as a MS-MRD quality control tool to monitor sample preparation efficacy and assay performance.

N-linked glycosylation of the M-protein variable region: glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma.

OBJECTIVES: Multiple myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged suggesting that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity to provide a site-specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. METHODS: Glycoproteogenomics was used for the detailed analysis of de novo N-glycosylation sites of M-proteins. First, Genomic analysis of the M-protein variable region was used to identify de novo N-glycosylation sites. Subsequently glycopeptide analysis with LC-MS/MS was used for detailed analysis of the M-protein glycan sites. RESULTS: Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to Fab Light Chain N-glycosylation of polyclonal antibodies from healthy individuals. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the Fab N-glycosylation sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. CONCLUSIONS: Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias.

An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients.

OBJECTIVES: Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. METHODS: Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. RESULTS: No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. CONCLUSIONS: Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.

The GlycoPaSER Prototype as a Real-Time N-Glycopeptide Identification Tool Based on the PaSER Parallel Computing Platform.

Real-time database searching allows for simpler and automated proteomics workflows as it eliminates technical bottlenecks in high-throughput experiments. Most importantly, it enables results-dependent acquisition (RDA), where search results can be used to guide data acquisition during acquisition. This is especially beneficial for glycoproteomics since the wide range of physicochemical properties of glycopeptides lead to a wide range of optimal acquisition parameters. We established here the GlycoPaSER prototype by extending the Parallel Search Engine in Real-time (PaSER) functionality for real-time glycopeptide identification from fragmentation spectra. Glycopeptide fragmentation spectra were decomposed into peptide and glycan moiety spectra using common N-glycan fragments. Each moiety was subsequently identified by a specialized algorithm running in real-time. GlycoPaSER can keep up with the rate of data acquisition for real-time analysis with similar performance to other glycoproteomics software and produces results that are in line with the literature reference data. The GlycoPaSER prototype presented here provides the first proof-of-concept for real-time glycopeptide identification that unlocks the future development of RDA technology to transcend data acquisition.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

Site-Specific, Platform-Based Conjugation Strategy for the Synthesis of Dual-Labeled Immunoconjugates for Bimodal PET/NIRF Imaging of HER2-Positive Tumors.

Because positron emission tomography (PET) and optical imaging are very complementary, the combination of these two imaging modalities is very enticing in the oncology field. Such bimodal imaging generally relies on imaging agents bearing two different imaging reporters. In the bioconjugation field, this is mainly performed by successive random conjugations of the two reporters on the protein vector, but these random conjugations can alter the vector properties. In this study, we aimed at abrogating the heterogeneity of the bimodal imaging immunoconjugate and mitigating the impact of multiple random conjugations. A trivalent platform bearing a DFO chelator for 89Zr labeling, a NIR fluorophore, IRDye800CW, and a bioconjugation handle was synthesized. This bimodal probe was site-specifically grafted to trastuzumab via glycan engineering. This new bimodal immunoconjugate was then investigated in terms of radiochemistry, in vitro and in vivo, and compared to the clinically relevant random equivalent. In vitro and in vivo, our strategy provides several improvements over the current clinical standard. The combination of site-specific conjugation with the monomolecular platform reduced the heterogeneity of the final immunoconjugate, improved the resistance of the fluorophore toward radiobleaching, and reduced the nonspecific uptake in the spleen and liver compared to the standard random immunoconjugate. To conclude, the strategy developed is very promising for the synthesis of better defined dual-labeled immunoconjugates, although there is still room for improvement. Importantly, this conjugation strategy is highly modular and could be used for the synthesis of a wide range of dual-labeled immunoconjugates.

Primary Focal Segmental Glomerulosclerosis Plasmas Increase Lipid Droplet Formation and Perilipin-2 Expression in Human Podocytes.

Many patients with primary focal segmental glomerulosclerosis (FSGS) develop recurrence of proteinuria after kidney transplantation. Several circulating permeability factors (CPFs) responsible for recurrence have been suggested, but were never validated. We aimed to find proteins involved in the mechanism of action of CPF(s) and/or potential biomarkers for the presence of CPF(s). Cultured human podocytes were exposed to plasma from patients with FSGS with presumed CPF(s) or healthy and disease controls. Podocyte proteomes were analyzed by LC-MS. Results were validated using flow cytometry, RT-PCR, and immunofluorescence. Podocyte granularity was examined using flow cytometry, electron microscopy imaging, and BODIPY staining. Perilipin-2 protein expression was increased in podocytes exposed to presumed CPF-containing plasmas, and correlated with the capacity of plasma to induce podocyte granularity, identified as lipid droplet accumulation. Elevated podocyte perilipin-2 was confirmed at protein and mRNA level and was also detected in glomeruli of FSGS patients whose active disease plasmas induced podocyte perilipin-2 and lipid droplets. Our study demonstrates that presumably, CPF-containing plasmas from FSGS patients induce podocyte lipid droplet accumulation and perilipin-2 expression, identifying perilipin-2 as a potential biomarker. Future research should address the mechanism underlying CPF-induced alterations in podocyte lipid metabolism, which ultimately may result in novel leads for treatment.

Clonotypic Features of Rearranged Immunoglobulin Genes Yield Personalized Biomarkers for Minimal Residual Disease Monitoring in Multiple Myeloma.

BACKGROUND: Due to improved treatment, more patients with multiple myeloma (MM) reach a state of minimal residual disease (MRD). Different strategies for MM MRD monitoring include flow cytometry, allele-specific oligonucleotide-quantitative PCR, next-generation sequencing, and mass spectrometry (MS). The last 3 methods rely on the presence and the stability of a unique immunoglobulin fingerprint derived from the clonal plasma cell population. For MS-MRD monitoring it is imperative that MS-compatible clonotypic M-protein peptides are identified. To support implementation of molecular MRD techniques, we studied the presence and stability of these clonotypic features in the CoMMpass database. METHODS: An analysis pipeline based on MiXCR and HIGH-VQUEST was constructed to identify clonal molecular fingerprints and their clonotypic peptides based on transcriptomic datasets. To determine the stability of the clonal fingerprints, we compared the clonal fingerprints during disease progression for each patient. RESULTS: The analysis pipeline to establish the clonal fingerprint and MS-suitable clonotypic peptides was successfully validated in MM cell lines. In a cohort of 609 patients with MM, we demonstrated that the most abundant clone harbored a unique clonal molecular fingerprint and that multiple unique clonotypic peptides compatible with MS measurements could be identified for all patients. Furthermore, the clonal immunoglobulin gene fingerprints of both the light and heavy chain remained stable during MM disease progression. CONCLUSIONS: Our data support the use of the clonal immunoglobulin gene fingerprints in patients with MM as a suitable MRD target for MS-MRD analyses.

Multiple Myeloma Minimal Residual Disease Detection: Targeted Mass Spectrometry in Blood vs Next-Generation Sequencing in Bone Marrow.

BACKGROUND: Minimal residual disease (MRD) status assessed on bone marrow aspirates is a major prognostic biomarker in multiple myeloma (MM). In this study we evaluated blood-based targeted mass spectrometry (MS-MRD) as a sensitive, minimally invasive alternative to measure MM disease activity. METHODS: Therapy response of 41 MM patients in the IFM-2009 clinical trial (NCT01191060) was assessed with MS-MRD on frozen sera and compared to routine state-of-the-art monoclonal protein (M-protein) diagnostics and next-generation sequencing (NGS-MRD) at 2 time points. RESULTS: In all 41 patients we were able to identify clonotypic M-protein-specific peptides and perform serum-based MS-MRD measurements. MS-MRD is significantly more sensitive to detect M-protein compared to either electrophoretic M-protein diagnostics or serum free light chain analysis. The concordance between NGS-MRD and MS-MRD status in 81 paired bone marrow/sera samples was 79%. The 50% progression-free survival (PFS) was identical (49 months) for patients who were either NGS-positive or MS-positive directly after maintenance treatment. The 50% PFS was 69 and 89 months for NGS-negative and MS-negative patients, respectively. The longest 50% PFS (96 months) was observed in patients who were MRD-negative for both methods. MS-MRD relapse during maintenance treatment was significantly correlated to poor PFS (P < 0.0001). CONCLUSIONS: Our data indicate proof-of-principle that MS-MRD evaluation in blood is a feasible, patient friendly alternative to NGS-MRD assessed on bone marrow. Clinical validation of the prognostic value of MS-MRD and its complementary value in MRD-evaluation of patients with MM is warranted in an independent larger cohort.

Affimers as an alternative to antibodies for protein biomarker enrichment.

INTRODUCTION: Assessing the specificity of protein binders is an essential first step in protein biomarker assay development. Affimers are novel protein binders and can potentially replace antibodies in multiple protein capture-based assays. Affimers are selected for their high specificity against the target protein and have benefits over antibodies like batch-to-batch reproducibility and are stable across a wide range of chemical conditions. Here we mimicked a typical initial screening of affimers and commercially available monoclonal antibodies against two non-related proteins, IL-37b and proinsulin, to assess the potential of affimers as alternative to antibodies. METHODS: Binding specificity of anti-IL-37b and anti-proinsulin affimers and antibodies was investigated via magnetic bead-based capture of their recombinant protein targets in human plasma. Captured proteins were analyzed using SDS-PAGE, Coomassie blue staining, Western blotting and LC-MS/MS-based proteomics. RESULTS: All affimers and antibodies were able to bind their target protein in human plasma. Gel and LC-MS/MS analysis showed that both affimer and antibody-based captures resulted in co-purified background proteins. However, affimer-based captures showed the highest relative enrichment of IL-37b and proinsulin. CONCLUSIONS: For both proteins tested, affimers show higher specificity in purifying their target proteins from human plasma compared to monoclonal antibodies. These results indicate that affimers are promising antibody-replacement tools for protein biomarker assay development.

Inflammation biomarker discovery in Parkinson's disease and atypical parkinsonisms.

BACKGROUND: Parkinson's disease (PD) and atypical parkinsonisms (APD) have overlapping symptoms challenging an early diagnosis. Diagnostic accuracy is important because PD and APD have different prognosis and response to treatment. We aimed to identify diagnostic inflammatory biomarkers of PD and APD in cerebrospinal fluid (CSF) using the multiplex proximity extension assay (PEA) technology and to study possible correlations of biomarkers with disease progression. METHODS: CSF from a longitudinal cohort study consisting of PD and APD patients (PD, n = 44; multiple system atrophy (MSA), n = 14; vascular parkinsonism (VaP), n = 9; and PD with VaP, n = 7) and controls (n = 25) were analyzed. RESULTS: Concentrations of CCL28 were elevated in PD compared to controls (p = 0.0001). Five other biomarkers differentiated both MSA and PD from controls (p < 0.05) and 10 biomarkers differentiated MSA from controls, of which two proteins, i.e. beta nerve growth factor (ß-NGF) and Delta and Notch like epidermal growth factor-related receptor (DNER), were also present at lower levels in MSA compared to PD (both p = 0.032). Two biomarkers (MCP-1 and MMP-10) positively correlated with PD progression (rho > 0.650; p < 0.01). CONCLUSIONS: PEA technique identified potential new CSF biomarkers to help to predict the prognosis of PD. Also, we identified new candidate biomarkers to distinguish MSA from PD.

Evaluation of chitotriosidase as a biomarker for adipose tissue inflammation in overweight individuals and type 2 diabetic patients.

BACKGROUND: Overweight and obesity can lead to adipose tissue inflammation, which causes insulin resistance and on the long-term type 2 diabetes mellitus (T2D). The inflammatory changes of obese-adipose tissue are characterized by macrophage infiltration and activation, but validated circulating biomarkers for adipose tissue inflammation for clinical use are still lacking. One of the most secreted enzymes by activated macrophages is chitotriosidase (CHIT1). OBJECTIVE: To test whether circulating CHIT1 enzymatic activity levels reflect adipose tissue inflammation. METHODS: Plasma and adipose tissue samples of 105 subjects (35 lean, 37 overweight, and 33 T2D patients) were investigated. CHIT1 mRNA levels were determined in adipose tissue-resident innate immune cells. CHIT1 mRNA levels, protein abundance, and plasma enzymatic activity were subsequently measured in adipose tissue biopsies and plasma of control subjects with varying levels of obesity and adipose tissue inflammation as well as in T2D patients. RESULTS: In adipose tissue, CHIT1 mRNA levels were higher in stromal vascular cells compared to adipocytes, and higher in adipose tissue-residing macrophages compared to circulating monocytes (p < 0.001). CHIT1 mRNA levels in adipose tissue were enhanced in overweightcompared to lean subjects and even more in T2D patients (p < 0.05). In contrast, plasma CHIT1 enzymatic activity did not differ between lean, overweight subjects and T2D patients. A mutation of the CHIT1 gene decreases plasma CHIT1 activity. CONCLUSIONS: CHIT1 is expressed by adipose tissue macrophages and expression is higher in overweight subjects and T2D patients, indicating its potential as tissue biomarker for adipose tissue inflammation. However, these differences do not translate into different plasma CHIT1 activity levels. Moreover, a common CHIT1 gene mutation causing loss of plasma CHIT1 activity interferes with its use as a biomarker of adipose tissue inflammation. These results indicate that plasma CHIT1 activity is of limited value as a circulating biomarker for adipose tissue inflammation in human subjects.

The future of protein biomarker research in type 2 diabetes mellitus.

INTRODUCTION: The onset of type 2 diabetes mellitus (T2DM) is strongly associated with obesity and subsequent perturbations in immuno-metabolic responses. To understand the complexity of these systemic changes and better monitor the health status of people at risk, validated clinical biomarkers are needed. Omics technologies are increasingly applied to measure the interplay of genes, proteins and metabolites in biological systems, which is imperative in understanding molecular mechanisms of disease and selecting the best possible molecular biomarkers for clinical use. Areas covered: This review describes the complex onset of T2DM, the contribution of obesity and adipose tissue inflammation to the T2DM disease mechanism, and the output of current biomarker strategies. A new biomarker approach is described that combines published and new self-generated data to merge multiple -omes (i.e. genome, proteome, metabolome etc.) toward understanding of mechanism of disease on the individual level and design multiparameter biomarker panels that drive significant impacts on personalized healthcare. Expert commentary: We here propose an approach to use cross-omics analyses to contextualize published biomarker data and better understand molecular mechanisms of health and disease. This will improve the current and future innovation gaps in translation of discovered putative biomarkers to clinically applicable biomarker tests.

Clinical biomarker innovation: when is it worthwhile?

Background Choosing which biomarker tests to select for further research and development is not only a matter of diagnostic accuracy, but also of the clinical and monetary benefits downstream. Early health economic modeling provides tools to assess the potential effects of biomarker innovation and support decision-making. Methods We applied early health economic modeling to the case of diagnosing primary aldosteronism in patients with resistant hypertension. We simulated a cohort of patients using a Markov cohort state-transition model. Using the headroom method, we compared the currently used aldosterone-to-renin ratio to a hypothetical new test with perfect diagnostic properties to determine the headroom based on quality-adjusted life-years (QALYs) and costs, followed by threshold analyses to determine the minimal diagnostic accuracy for a cost-effective product. Results Our model indicated that a perfect diagnostic test would yield 0.027 QALYs and increase costs by €43 per patient. At a cost-effectiveness threshold of €20,000 per QALY, the maximum price for this perfect test to be cost-effective is €498 (95% confidence interval [CI]: €275-€808). The value of the perfect test was most strongly influenced by the sensitivity of the current biomarker test. Threshold analysis showed the novel test needs a sensitivity of at least 0.9 and a specificity of at least 0.7 to be cost-effective. Conclusions Our model-based approach evaluated the added value of a clinical biomarker innovation, prior to extensive investment in development, clinical studies and implementation. We conclude that early health economic modeling can be a valuable tool when prioritizing biomarker innovations in the laboratory.

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

BACKGROUND: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities.

INTRODUCTION: The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease. OBJECTIVES: Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics. METHODS: Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data. RESULTS: Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors. CONCLUSION: Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Circulating FGF21 Concentration, Fasting Plasma Glucose, and the Risk of Type 2 Diabetes: Results From the PREVEND Study.

OBJECTIVE: Fibroblast growth factor 21 (FGF21) is a peptide hormone synthesized by several organs and regulates, among others, energy homeostasis. In obesity, insulin resistance and type 2 diabetes (T2D), higher circulating FGF21 concentrations have been found. Temporal analyses in murine studies demonstrate that FGF21 increases before insulin resistance occurs. The current study aims to investigate in time-to-event analyses whether FGF21 may be an early biomarker in the development of T2D. RESEARCH DESIGN AND METHODS: Circulating FGF21 was measured using an immunoassay of the Mesoscale U-PLEX assay platform. The study outcome was incident T2D. Associations of circulating FGF21 concentration with T2D were quantified using Cox proportional hazards models with adjustments for potential confounders. RESULTS: We included 5244 participants aged 52 ± 12 years, of whom 50% were male. Median [interquartile range] circulating FGF21 concentration was 860 [525-1329] pg/mL. During 7.3 [6.1-7.7] years of follow-up, 299 (5.7%) participants developed T2D. In fully adjusted analyses, higher circulating FGF21 concentration was associated with an increased risk of incident T2D (hazard ratio per doubling: 1.26 [95% CI, 1.06-1.51]; P = 0.008), with effect modification by fasting plasma glucose, consistent with strengthening of the association at lower fasting glucose (interaction coefficient: -0.12; P = 0.022). CONCLUSION: Higher circulating FGF21 concentrations are independently associated with an increased risk of incident T2D in participants with a low fasting plasma glucose, making circulating FGF21 concentration a potential early biomarker for type 2 diabetes.

Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens.

Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.