IgG N-glycans are an emerging source of disease-specific biomarkers. Over the last decade, the continued development of glycomic databases and the evolution of glyco-analytic methods have resulted in increased throughput, resolution, and sensitivity. IgG N-glycans promote adaptive immune responses through antibody-dependent cellular cytotoxicity (ADCC) and complement activation to combat infection or cancer and promote autoimmunity. In addition to the functional assays, researchers are examining the ability of protein-specific glycosylation to serve as biomarkers of disease. This literature review demonstrates that IgG N-glycans can discriminate between healthy controls, autoimmune disease, infectious disease, and cancer with high sensitivity. The literature also indicates that the IgG glycosylation patterns vary across disease state, thereby supporting their role as specific biomarkers. In addition, IgG N-glycans can be collected longitudinally from patients to track treatment responses or predict disease reoccurrence. This review focuses on IgG N-glycan profiles applied as diagnostics, cohort discriminators, and prognostics. Recent successes, remaining challenges, and upcoming approaches are critically discussed.

Background: The opioid epidemic is a rapidly growing public health concern in the USA, as the number of overdose deaths continues to increase each year. One strategy for combating the rising number of overdoses is through opioid overdose prevention programs (OOPPs). Objective: To evaluate the effectiveness of an innovative OOPP, with changes in knowledge and attitudes serving as the primary outcome measures. Methods: The OOPP was developed by a group of medical students under guidance from faculty advisors. Training sessions focused on understanding stigmatizing factors of opioid use disorder (OUD), as well as protocols for opioid overdose reversal through naloxone administration. Pre- and post-surveys were partially adapted from the opioid overdose attitudes and knowledge scales and administered to all participants. Paired t-tests were conducted to assess differences between pre- and post-surveys. Results: A total of 440 individuals participated in the training; 381 completed all or the majority of the survey. Participants came from a diverse set of backgrounds, ages, and experiences. All three knowledge questions showed significant improvements. For attitude questions, significant improvements were found in all three questions evaluating confidence, two of three questions assessing attitudes towards overdose reversal, and four of five questions evaluating stigma and attitudes towards individuals with OUD. Conclusions: Our innovative OOPP was effective not only in increasing knowledge but also in improving attitudes towards overdose reversal and reducing stigma towards individuals with OUD. Given the strong improvements in attitudes towards those with OUD, efforts should be made to incorporate the unique focus on biopsychosocial and sociohistorical components into future OOPPs.

Glycosidases are hydrolytic enzymes studied principally in the context of intracellular catabolism within the lysosome. Therefore, glycosidase activities are classically measured in experimentally acidified assay conditions reflecting their low pH optima. However, glycosidases are also present in the bloodstream where they may retain sufficient activity to participate in the regulation of glycoprotein half-lives, proteostasis, and disease pathogenesis. We have, herein, established at physiological pH 7.4 in blood plasma and sera the normal ranges of four major glycosidase activities essential for blood glycoprotein remodeling in healthy mice and humans. These activities included β-galactosidase, β-N-acetylglucosaminidase, α-mannosidase, and α-fucosidase. We have identified their origins to include the mammalian genes Glb1, HexB, Man2a1, and Fuca1. In experimental sepsis, excursions of glycosidase activities occurred with differences in host responses to discrete bacterial pathogens. Among similar excursions in human sepsis, the elevation of β-galactosidase activity was a prognostic indicator of increased likelihood of patient death.

The acceleration of climate change has been associated with an alarming increase in the prevalence and geographic range of tick-borne diseases (TBD), many of which have severe and long-lasting effects—particularly when treatment is delayed principally due to inadequate diagnostics and lack of physician suspicion. Moreover, there is a paucity of treatment options for many TBDs that are complicated by diagnostic limitations for correctly identifying the offending pathogens. This review will focus on the biology, disease pathology, and detection methodologies used for the Borreliaceae family which includes the Lyme disease agent Borreliella burgdorferi. Previous work revealed that Borreliaceae genomes differ from most bacteria in that they are composed of large numbers of replicons, both linear and circular, with the main chromosome being the linear with telomeric-like termini. While these findings are novel, additional gene-specific analyses of each class of these multiple replicons are needed to better understand their respective roles in metabolism and pathogenesis of these enigmatic spirochetes. Historically, such studies were challenging due to a dearth of both analytic tools and a sufficient number of high-fidelity genomes among the various taxa within this family as a whole to provide for discriminative and functional genomic studies. Recent advances in long-read whole-genome sequencing, comparative genomics, and machine-learning have provided the tools to better understand the fundamental biology and phylogeny of these genomically-complex pathogens while also providing the data for the development of improved diagnostics and therapeutics.

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. 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. 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. 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 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.

Glycoproteins are post-translationally modified proteins containing branched sugar structures referred to as glycans. One major class of glycoproteins are the immunoglobulins. Immunoglobulins play a key role in the adaptive immune response and require glycosylation in the Golgi apparatus before release into circulation. The size, charge, and shape of glycans added to immunoglobulins determine half-life, effector function, and Fc receptor binding affinity. In the laboratory, classes of immunoglobulins can be isolated from patient blood and analyzed for glycan content using lectin, chromatographic, and mass spectrometry methods. In this thesis, glycans from human IgG and IgM are analyzed for changes during health and disease states. We elected to study Lyme disease because the host response to Lyme disease had not been characterized using a glycomic approach. We hypothesized that the host antibody glycomic response to acute Lyme disease would reveal more information about how the host immune response is mounted. Further, we hypothesized that this study could reveal mechanisms of host adaptive immune system evasion by the Lyme disease bacterium. We first asked: what immunoglobulin glycan alterations are associated with acute Lyme disease infection in humans? Here, we demonstrate IgG and IgM glycans produced in response to acute Lyme disease contain elevated levels of galactose and sialic acid. Next, we link the changes in immunoglobulin glycan profiles observed during acute Lyme disease to the functional impairment of IgG to signal for antibody-dependent cellular cytotoxicity. Following antibiotic therapy for Lyme disease, we demonstrate antibody glycosylation decreased the sialic acid content to reflect the resolution of Lyme disease. Later in this thesis, we characterize the glycan-associated serological response to COVID-19 by studying hospitalized patients infected from the Immunophenotyping assessment in a COVID-19 cohort. Studies of the COVID-19 glycomic response were previously limited to IgG glycans. We sought to characterize the glycomic response of both IgG and IgM to gain more insight into the adaptive immune response during COVID-19. We hypothesized that glycans on IgM would stratify patients with nonsevere versus severe COVID-19 better than IgG glycans. Further, we hypothesized that IgM glycans characterization would provide more insight into the acute host response to the viral infection. Here, we report significant differences in the IgG and IgM glycan profiles - particularly the ability of IgM glycans to aid in the stratification of nonsevere versus severe COVID-19 patients. We next hypothesized that the IgM glycans were modulating IgM- directed complement deposition. To test this, we developed an antigen-specific assay using purified IgM with and without sialidase digestions. Using this assay, we demonstrate a potential mechanism of the sialic acid glycans on IgM to promote complement deposition in severely infected COVID-19 patients. In conclusion, this thesis examines immunoglobulin glycosylation during human adaptive immune responses and links these glycan changes to modulated immunoglobulin effector function. In the case of acute Lyme disease, the sialic acid on antigen-specific IgG responding to Lyme disease impairs the induction of ADCC signaling. This impairment could be one mechanism of host immune evasion benefiting the Lyme disease bacterium. In contrast, elevated levels of sialic acid on IgM isolated from severe COVID-19 infections were associated with increased rates of complement deposition. Our data suggest this elevated IgM sialic acid could promote acute respiratory distress due to elevated complement deposition in the lung epithelia and endothelia of severe COVID-19 patients. Future studies should determine which pathways and cytokines are responsible for shifting the glycosylation of IgG and IgM during acute immune responses.