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.

Association Between Immunoglobulin G N-glycosylation and Vascular Cognitive Impairment in a Sample With Atherosclerosis: A Case-Control Study.

BACKGROUND: Atherosclerosis is considered a crucial component in the pathogenesis of decreased cognitive function, as occurs in vascular cognitive impairment (VCI). Inflammation and the immune response play a significant role in the development of many chronic diseases. Immunoglobulin G (IgG) N-glycosylation has been implicated in the development of a variety of diseases by affecting the anti-inflammatory and proinflammatory responses of IgG. This study aimed to investigate the association between IgG N-glycosylation and VCI in a sample of patients with atherosclerosis through a case-control study. METHOD: We recruited a total of 330 patients with atherosclerosis to participate in this case-control study, including 165 VCI patients and 165 sex- and age-matched participants with normal cognitive function. The plasma IgG N-glycans of participants were separated by ultrahigh-performance liquid chromatography. An enzyme-linked immunosorbent assay (ELISA) kit was used to determine the corresponding serum inflammatory factors. Atherosclerosis was diagnosed by carotid ultrasound, and the diagnosis of VCI was based on the "Guidelines for the Diagnosis and Treatment of Vascular Cognitive Impairment in China (2019)". A multivariate logistic regression model was used to explore the association between IgG N-glycans and VCI. We also analyzed the relationship between IgG N-glycans and the inflammatory state of VCI through canonical correlation analysis (CCA). RESULTS: Through the multivariate logistic regression analysis, 8 glycans and 13 derived traits reflecting decreased sialylation and galactosylation and increased bisecting GlcNAc significantly differed between the case and control groups after adjusting for confounding factors (P < 0.05, q < 0.05). Similarly, the differences in TNF-α, IL-6, and IL-10 were statistically significant between the case and control groups after adjusting for the effects of confounding factors (P < 0.05, q < 0.05). The CCA results showed that VCI-related initial N-glycans were significantly correlated with VCI-related inflammatory factors (r = 0.272, P = 0.004). The combined AUC value (AUC combined = 0.885) of 7 initial glycans and inflammatory factors was higher than their respective values (AUC initial glycans = 0.818, AUC inflammatory factors = 0.773). CONCLUSION: The findings indicate that decreased sialylation and galactosylation and increased bisecting GlcNAc reflected by IgG N-glycans might affect the occurrence of VCI in patients with atherosclerosis though promoting the proinflammatory function of IgG. IgG N-glycans may serve as potential biomarkers to distinguish VCI in individuals with atherosclerosis.

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%.

Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers.

Blood biomarkers may be used to detect physiological imbalance and potential disease. However, blood sampling is difficult and expensive, and not applicable in commercial settings. Instead, individual milk samples are readily available at low cost, can be sampled easily and analysed instantly. The present observational study sampled blood and milk from 234 Holstein dairy cows from experimental herds in six European countries. The objective was to compare the use of three different sets of milk biomarkers for identification of cows in physiological imbalance and thus at risk of developing metabolic or infectious diseases. Random forests was used to predict body energy balance (EBAL), index for physiological imbalance (PI-index) and three clusters differentiating the metabolic status of cows created on basis of concentrations of plasma glucose, ß-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and serum IGF-1. These three metabolic clusters were interpreted as cows in balance, physiological imbalance and "intermediate cows" with physiological status in between. The three sets of milk biomarkers used for prediction were: milk Fourier transform mid-IR (FT-MIR) spectra, 19 immunoglobulin G (IgG) N-glycans and 8 milk metabolites and enzymes (MME). Blood biomarkers were sampled twice; around 14 days after calving (days in milk (DIM)) and around 35 DIM. MME and FT-MIR were sampled twice weekly 1-50 DIM whereas IgG N-glycan were measured only four times. Performances of EBAL and PI-index predictions were measured by coefficient of determination (R2cv) and root mean squared error (RMSEcv) from leave-one-cow-out cross-validation (cv). For metabolic clusters, performance was measured by sensitivity, specificity and global accuracy from this cross-validation. Best prediction of PI-index was obtained by MME (R2cv = 0.40 (95 % CI: 0.29-0.50) at 14 DIM and 0.35 (0.23-0.44) at 35 DIM) while FT-MIR showed a better performance than MME for prediction of EBAL (R2cv = 0.28 (0.24-0.33) vs 0.21 (0.18-0.25)). Global accuracies of predicting metabolic clusters from MME and FT-MIR were at the same level ranging from 0.54 (95 % CI: 0.39-0.68) to 0.65 (0.55-0.75) for MME and 0.51 (0.37-0.65) to 0.68 (0.53-0.81) for FT-MIR. R2cv and accuracies were lower for IgG N-glycans. In conclusion, neither EBAL nor PI-index were sufficiently well predicted to be used as a management tool for identification of risk cows. MME and FT-MIR may be used to predict the physiological status of the cows, while the use of IgG N-glycans for prediction still needs development. Nevertheless, accuracies need to be improved and a larger training data set is warranted.

Association of the IgG N-glycome with the course of kidney function in type 2 diabetes.

INTRODUCTION: Inflammatory processes are thought to be involved in kidney function decline in individuals with type 2 diabetes. Glycosylation of immunoglobulin G (IgG) is an important post-translation process affecting the inflammatory potential of IgG. We investigated the prospective relationship between IgG N-glycosylation patterns and kidney function in type 2 diabetes. RESEARCH DESIGN AND METHODS: In the DiaGene study, an all-lines-of-care case-control study (n=1886) with mean prospective follow-up of 7.0 years, the association between 58 IgG N-glycan profiles and estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR) per year and during total follow-up was analyzed. Models were adjusted for clinical variables and multiple comparisons. RESULTS: Eleven traits were significantly associated with eGFR change per year. Bisecting GlcNAc in fucosylated and fucosylated disialylated structures and monosialylation of fucosylated digalactosylated structures were associated with a faster decrease of eGFR. Fucosylation of neutral and monogalactosylated structures was associated with less eGFR decline per year. No significant associations between IgG glycans and ACR were found. CONCLUSIONS: In type 2 diabetes, we found IgG N-glycosylation patterns associated with a faster decline of kidney function, reflecting a pro-inflammatory state of IgG. eGFR, but not ACR, was associated with IgG glycans, which suggests these associations may represent renal macroangiopathy rather than microvascular disease.