A complement C4-derived glycopeptide is a biomarker for PMM2-CDG.

BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).

Mass spectrometric analysis of chondroitin sulfate-linked peptides.

Chondroitin sulfate proteoglycans (CSPGs) are extracellular matrix components composed of linear glycosaminoglycan (GAG) side chains attached to a core protein. CSPGs play a vital role in neurodevelopment, signal transduction, cellular proliferation and differentiation and tumor metastasis through interaction with growth factors and signaling proteins. These pleiotropic functions of proteoglycans are regulated spatiotemporally by the GAG chains attached to the core protein. There are over 70 chondroitin sulfate-linked proteoglycans reported in cells, cerebrospinal fluid and urine. A core glycan linker of 3-6 monosaccharides attached to specific serine residues can be extended by 20-200 disaccharide repeating units making intact CSPGs very large and impractical to analyze. The current paradigm of CSPG analysis involves digesting the GAG chains by chondroitinase enzymes and analyzing either the protein part, the disaccharide repeats, or both by mass spectrometry. This method, however, provides no information about the site of attachment or the composition of linker oligosaccharides and the degree of sulfation and/or phosphorylation. Further, the analysis by mass spectrometry and subsequent identification of novel CSPGs is hampered by technical challenges in their isolation, less optimal ionization and data analysis. Unknown identity of the linker oligosaccharide also makes it more difficult to identify the glycan composition using database searching approaches. Following chondroitinase digestion of long GAG chains linked to tryptic peptides, we identified intact GAG-linked peptides in clinically relevant samples including plasma, urine and dermal fibroblasts. These intact glycopeptides including their core linker glycans were identified by mass spectrometry using optimized stepped higher energy collision dissociation and electron-transfer/higher energy collision dissociation combined with hybrid database search/de novo glycan composition search. We identified 25 CSPGs including three novel CSPGs that have not been described earlier. Our findings demonstrate the utility of combining enrichment strategies and optimized high-resolution mass spectrometry analysis including alternative fragmentation methods for the characterization of CSPGs. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-022-00092-3.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Maternal serum lipidomics identifies lysophosphatidic acid as a predictor of small for gestational age neonates.

To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.

High-resolution mass spectrometric analysis of cardiolipin profiles in Barth syndrome.

Barth syndrome is an X-linked recessive disorder caused by pathogenic variants in TAZ, which leads to a reduction in cardiolipin with a concomitant elevation of monolysocardiolipins. There is a paucity of studies characterizing changes in individual species of monolysocardiolipins, dilysocardiolipins and cardiolipin in Barth syndrome using high resolution untargeted lipidomics that can accurately annotate and quantify diverse lipids. We confirmed the structural diversity monolysocardiolipins, dilysocardiolipins and cardiolipin and identified individual species that showed previously unreported alterations in BTHS. Development of mass spectrometry-based targeted assays for these lipid biomarkers should provide an important tool for clinical diagnosis of Barth syndrome.

Applications of quantitative metabolomics to revolutionize early diagnosis of inborn errors of metabolism in India.

Inborn errors of metabolism (IEMs) are a group of disorders caused by disruption of metabolic pathways, which leads to accumulation, decreased circulating levels, or increased excretion of metabolites as a consequence of the underlying genetic defects. These heterogeneous groups of disorders cause significant neonatal and infant mortality across the whole world and it is of utmost concern for developing countries like India owing to lack of awareness and standard preventive strategies like newborn screening (NBS). Though the predictive cumulative incidence of IEMs is said to be ∼1:800 newborns, data pertaining to the true prevalence of individual IEMs is not available in the context of Indian population. There is a need for a large population-based study to get a clear picture of the prevalence of different IEMs. One of the best ways to screen for IEMs is by applying advanced liquid chromatography-mass spectrometry (LC-MS) technology using a quantitative metabolomics approaches such as selected or multiple reaction monitoring (SRM or MRM). Recent developments in LC-MS/MRM based quantification of marker metabolites in newborns have opened a novel opportunity to screen multiple disorders simultaneously from a minuscule volume of biological fluids. In this review article, we have highlighted how LC-MS/MRM based metabolomics approach with its high sensitivity and diagnostic capability can make an impact on the nation's public health through NBS programs.