The therapeutic potential of sialylated Fc domains of human IgG

Elevated neuroinflammation, autoimmunity, and altered IgG glycosylation profile in the cerebral spinal fluid of severe COVID-19 patients

BACKGROUND AND OBJECTIVES

A spectrum of neurologic complications associated with COVID-19 are well documented. While neuroinflammation in the brain of COVID-19 patients likely contributes to these complications, the mechanisms of neuroinflammation and correlates of neurologic complications remain elusive, especially since the etiologic pathogen of COVID-19, SARS-CoV-2, minimally invades the CNS. This study aimed to evaluate markers of neuroinflammation, IgG glycosylation patterns indicative of pro- or anti-inflammatory state, and prevalence of brain auto-reactive antibodies in the CSF of COVID-19 patients and their relationship to brain neuropathology.

METHODS

We evaluated the CSF of 11 deceased unvaccinated COVID-19 donors and 13 matched non-COVID-19 controls. Markers of neuroinflammation, IgG glycosylation patterns, and brain auto-reactive antibodies were assessed, along with their correlation to brain neuropathology. Statistical analyses were performed to compare groups and assess relationships between variables, using non-parametric tests and bootstrap analysis.

RESULTS

COVID-19 CSF showed higher levels of neopterin and ANNA-1, markers of neuroinflammation and autoimmunity, respectively, and lower IFN response compared to non-COVID-19 donors. In brain regions of high microglial activation, IL4 and RANTES were significantly increased. SARS-CoV-2 was undetectable in the CSF and brain of COVID-19 donors, yet anti-SARS-CoV-2 CSF antibodies were detected. Fucosylated IgG were associated with Spike IgG, CSF protein, and soluble CD14, whereas afucosylated bisecting IgG were inversely correlated with Spike IgG. Sialic acid containing IgG were positively correlated with IL1β and TNFα. These associations were not found in non-COVID-19 donors. Inflammatory agalactosylated fucosylated IgG (G0F) were associated with infiltrating CD4 + T cells in the brains of COVID-19 donors. COVID-19 donor CSF displayed higher levels of auto-reactive antibodies to human brain antigens compared to non-COVID-19 donors and donors with positive autoantibodies showed higher levels of neopterin.

DISCUSSION

These data describe increased neuroinflammation and autoreactive antibody markers in the CSF of COVID-19 donors and suggest that IgG glycosylation and autoimmunity may contribute to COVID-19 pathology, highlighting potential mechanisms underlying the neurologic complications associated with COVID-19.

Immunoglobulin G N-Glycome as a biomarker of mortality risk in Escherichia coli induced sepsis

Background

Sepsis is a life-threatening syndrome caused by an imbalance in the inflammatory response to an infection that can lead to a high mortality rate. Escherichia coli is a common pathogen that causes sepsis. The role of immunoglobulin G N-glycome in estimating the mortality in patients with sepsis remains unknown. This study aims to reveal the clinical application of immunoglobulin G N-glycome as a potentially novel biomarker to predict mortality risk in Escherichia coli-induced sepsis.

Methods

The serum immunoglobulin G N-glycome levels in 100 adult septic patient serum samples on the day of intensive care unit (ICU) admission, and 100 healthy volunteers were measured and analyzed. Immunoglobulin G N-glycome was compared with existing risk scores on predicting in-hospital death.

Results

We identified that the fucosylation level was significantly decreased in patients. Importantly, bisecting GlcNAc, sialylation, and galactosylation have different levels between sepsis and control groups. In addition, the AUC values of the SOFA score combined with GP4, GP5, and GP9 were 0.76 (95%CI: 0.61 to 0.90), 0.58 (95%CI: 0.40 to 0.7) and 0.57 (95%CI: 0.38 to 0.76). The AUC value of the SOFA score combined with GP4 and GP7 was 0.85 (95%CI: 0.76 to 0.93) in predicting in-hospital mortality in patients with sepsis.

Conclusions

Immunoglobulin G N-glycome concentrations at ICU admission are valuable for predicting the in-hospital mortality risk of patients with sepsis, suggesting that immunoglobulin G N-glycome may be a novel biomarker.

Integrated SegFlow, µSIA, and UPLC for Online Sialic Acid Quantitation of Glycoproteins Directly from Bioreactors

This study emphasizes the critical importance of closely monitoring and controlling the sialic acid content in therapeutic glycoproteins, including EPO, interferon-γ, Orencia, Enbrel, and others, as the level of sialylation directly impacts their pharmacokinetics (PK), immunogenicity, potency, and overall clinical performance due to its influence on protein clearance via hepatic asialoglycoprotein receptors (ASGPR). The ASGPR recognizes and binds to glycoproteins exposed to terminal galactose or N-acetylgalactosamine residues, leading to receptor-mediated endocytosis. Recent studies have demonstrated that sialylation of O-linked glycan plays a role in protecting against macrophage galactose lectin (MGL)-mediated clearance. In addition to the impact on serum half-life, sialylation can influence other clinical outcomes, including immunogenicity, potency, and cytotoxicity. Therefore, the level of sialic acid is a critical quality attribute (CQA), and monitoring and regulating sialylation has become a regulatory requirement to ensure desired clinical performance. To achieve consistent levels of sialic acid-to-protein ratio, the time of upstream harvest and conductivity of downstream wash buffers must be tightly regulated based on the sialic acid content. Therefore, the utilization of process analytical technology (PAT) tools for generating real-time or near-real-time sialic acid content is a business-critical requirement. This work demonstrates the utility of an integrated PAT system for near real-time online sialic acid measurements. The system consists of a micro-sequential injection analyzer (µSIA) interfaced with SegFlow and an ultra performance liquid chromatography (UPLC). The fully automated architecture exemplifies the execution of online sampling, automatic sample preparation, and subsequent online UPLC analysis. This carefully orchestrated PAT framework effectively supports the requirements of QbD-driven continuous bioprocessing.

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

High-Throughput Glycan Profiling of Human Serum IgG Subclasses Using Parallel Reaction Monitoring Peptide Bond Fragmentation of Glycopeptides and Microflow LC-MS

LC-MS-based N-glycosylation profiling in four human serum IgG subclasses (IgG1, IgG2, IgG3, and IgG4) often requires additional affinity-based enrichment of specific IgG subclasses, owing to the high amino acid sequence similarity of Fc glycopeptides among subclasses. Notably, for IgG4 and the major allotype of IgG3, the glycopeptide precursors share identical retention time and mass and therefore cannot be distinguished based on precursor or glycan fragmentation. Here, we developed a parallel reaction monitoring (PRM)-based method for quantifying Fc glycopeptides through combined transitions generated from both glycosidic and peptide bond fragmentation. The latter enables the subpopulation of IgG3 and IgG4 to be directly distinguished according to mass differences without requiring further enrichment of specific IgG subclasses. In addition, a multinozzle electrospray emitter coupled to a capillary flow liquid chromatograph was used to increase the robustness and detection sensitivity of the method for low-yield peptide backbone fragment ions. The gradient was optimized to decrease the overall run time and make the method compatible with high-throughput analysis. We demonstrated that this method can be used to effectively monitor the relative levels of 13 representative glycoforms, with a good limit of detection for individual IgG subclasses.

Host glycosylation of immunoglobulins impairs the immune response to acute Lyme disease

BACKGROUND

Lyme disease is caused by the bacteria Borreliella burgdorferi sensu lato (Bb) transmitted to humans from the bite of an infected Ixodes tick. Current diagnostics for Lyme disease are insensitive at the early disease stage and they cannot differentiate between active infections and people with a recent history of antibiotic-treated Lyme disease.

METHODS

Machine learning technology was utilized to improve the prediction of acute Lyme disease and identify sialic acid and galactose sugar structures (N-glycans) on immunoglobulins associated specifically at time points during acute Lyme disease time. A plate-based approach was developed to analyze sialylated N-glycans associated with anti-Bb immunoglobulins. This multiplexed approach quantitates the abundance of Bb-specific IgG and the associated sialic acid, yielding an accuracy of 90% in a powered study.

FINDINGS

It was demonstrated that immunoglobulin sialic acid levels increase during acute Lyme disease and following antibiotic therapy and a 3-month convalescence, the sialic acid level returned to that found in healthy control subjects (p < 0.001). Furthermore, the abundance of sialic acid on Bb-specific IgG during acute Lyme disease impaired the host's ability to combat Lyme disease via lymphocytic receptor FcγRIIIa signaling. After enzymatically removing the sialic acid present on Bb-specific antibodies, the induction of cytotoxicity from acute Lyme disease patient antigen-specific IgG was significantly improved.

INTERPRETATION

Taken together, Bb-specific immunoglobulins contain increased sialylation which impairs the host immune response during acute Lyme disease. Furthermore, this Bb-specific immunoglobulin sialyation found in acute Lyme disease begins to resolve following antibiotic therapy and convalescence.

FUNDING

Funding for this study was provided by the Coulter-Drexel Translational Research Partnership Program as well as from a Faculty Development Award from the Drexel University College of Medicine Institute for Molecular Medicine and Infectious Disease and the Department of Microbiology and Immunology.

IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition

The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.

IgG glycans in health and disease: Prediction, intervention, prognosis, and therapy

Immunoglobulin (IgG) glycosylation is a complex enzymatically controlled process, essential for the structure and function of IgG. IgG glycome is relatively stable in the state of homeostasis, yet its alterations have been associated with aging, pollution and toxic exposure, as well as various diseases, including autoimmune and inflammatory diseases, cardiometabolic diseases, infectious diseases and cancer. IgG is also an effector molecule directly involved in the inflammation processes included in the pathogenesis of many diseases. Numerous recently published studies support the idea that IgG N-glycosylation fine-tunes the immune response and plays a significant role in chronic inflammation. This makes it a promising novel biomarker of biological age, and a prognostic, diagnostic and treatment evaluation tool. Here we provide an overview of the current state of knowledge regarding the IgG glycosylation in health and disease, and its potential applications in pro-active prevention and monitoring of various health interventions.

IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition

The glycosylation of IgG plays a critical role during human SARS-CoV-2, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during acute viral infection in humans. In vitro evidence suggests that the glycosylation of IgM inhibits T cell proliferation and alters complement activation rates. The analysis of IgM N-glycosylation from healthy controls and hospitalized COVID-19 patients reveals that mannosylation and sialyation levels associate with COVID-19 severity. Specifically, we find increased di- and tri-sialylated glycans and altered mannose glycans in total serum IgM in severe COVID-19 patients when compared to moderate COVID-19 patients. This is in direct contrast with the decrease of sialic acid found on the serum IgG from the same cohorts. Moreover, the degree of mannosylation and sialylation correlated significantly with markers of disease severity: D-dimer, BUN, creatinine, potassium, and early anti-COVID-19 amounts of IgG, IgA, and IgM. Further, IL-16 and IL-18 cytokines showed similar trends with the amount of mannose and sialic acid present on IgM, implicating these cytokines' potential to impact glycosyltransferase expression during IgM production. When examining PBMC mRNA transcripts, we observe a decrease in the expression of Golgi mannosidases that correlates with the overall reduction in mannose processing we detect in the IgM N-glycosylation profile. Importantly, we found that IgM contains alpha-2,3 linked sialic acids in addition to the previously reported alpha-2,6 linkage. We also report that antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients. Taken together, this work links the immunoglobulin M N-glycosylation with COVID-19 severity and highlights the need to understand the connection between IgM glycosylation and downstream immune function during human disease.

Minor structural changes, major functional impacts: posttranslational modifications and drug targets

Posttranslational modifications (PTMs) are essential mechanisms that provide chemical diversity to proteins. The additional functional and structural elements can be introduced to exceed the primary amino acid composition. PTMs impact key biological and physiological processes including cell signaling, metabolism, protein degradation and influences interactions with other macromolecules. However, characterization of the structural and functional signatures of modified proteins has been historically limited. Since defects in PTMs are linked to numerous disorders and diseases, PTMs and their modifying enzymes are considered as potential drug targets. This has fueled new initiatives to determine how PTMs affect protein structure and function. In this review, I summarize some of the major, well-studied protein PTMs and related drug targets. Since PTMs are widely used for therapeutic targets or disease markers, highlighting structural changes after PTM provides new frontiers in understanding the detailed mechanism and related drug developments.

Acute lyme disease IgG N-linked glycans contrast the canonical inflammatory signature

Lyme disease (LD) infection is caused by Borrelia burgdorferi sensu lato (Bb). Due to the limited presence of this pathogen in the bloodstream in humans, diagnosis of LD relies on seroconversion. Immunoglobulins produced in response to infection are differentially glycosylated to promote or inhibit downstream inflammatory responses by the immune system. Immunoglobulin G (IgG) N-glycan responses to LD have not been characterized. In this study, we analyzed IgG N-glycans from cohorts of healthy controls, acute LD patient serum, and serum collected after acute LD patients completed a 2- to 3-week course of antibiotics and convalesced for 70-90 days. Results indicate that during the acute phase of Bb infection, IgG shifts its glycosylation profile to include structures that are not associated with the classic proinflammatory IgG N-glycan signature. This unexpected result is in direct contrast to what is reported for other inflammatory diseases. Furthermore, IgG N-glycans detected during acute LD infection discriminated between control, acute, and treated cohorts with a sensitivity of 75-100% and specificity of 94.7-100%.

Sialylation as an Important Regulator of Antibody Function

Antibodies play a critical role in linking the adaptive immune response to the innate immune system. In humans, antibodies are categorized into five classes, IgG, IgM, IgA, IgE, and IgD, based on constant region sequence, structure, and tropism. In serum, IgG is the most abundant antibody, comprising 75% of antibodies in circulation, followed by IgA at 15%, IgM at 10%, and IgD and IgE are the least abundant. All human antibody classes are post-translationally modified by sugars. The resulting glycans take on many divergent structures and can be attached in an N-linked or O-linked manner, and are distinct by antibody class, and by position on each antibody. Many of these glycan structures on antibodies are capped by sialic acid. It is well established that the composition of the N-linked glycans on IgG exert a profound influence on its effector functions. However, recent studies have described the influence of glycans, particularly sialic acid for other antibody classes. Here, we discuss the role of glycosylation, with a focus on terminal sialylation, in the biology and function across all antibody classes. Sialylation has been shown to influence not only IgG, but IgE, IgM, and IgA biology, making it an important and unappreciated regulator of antibody function.