Beyond IgG: Novel Insights into IgA and IgM Glycosylation in Tuberculosis and Their Role in Differentiating Infection Status

Beyond IgG: Novel Insights into IgA and IgM Glycosylation in Tuberculosis and Their Role in Differentiating Infection Status | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond IgG: Novel Insights into IgA and IgM Glycosylation in Tuberculosis and Their Role in Differentiating Infection Status Yun-Jung Yang, Chih-Hsin Lee, Yung-Kun Chuang, San-Yuan Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6381409/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a global health challenge, particularly among the elderly. Accurate differentiation between active TB (ATB) and latent TB infection (LTBI) is essential, yet current diagnostic tools often fall short. While immunoglobulin (Ig) G glycosylation has been investigated in TB differential diagnosis, the glycosylation profiles of IgA and IgM have not been systematically studied—especially in older adults, who are more susceptible to TB reactivation. Methods: We analyzed the glycosylation patterns of IgG, IgA, and IgM in 59 elderly participants, including 22 ATB patients, 17 LTBI individuals, and 20 healthy controls. Antibody glycosylation was profiled using liquid chromatography-tandem mass spectrometry (LC-MS/MS), with a focus on distinguishing features between ATB and LTBI. Results: This study is the first to identify distinct glycosylation alterations in IgA and IgM among TB patients. Compared to LTBI and controls, ATB patients showed reduced galactosylation and increased fucosylation in IgG and IgM, indicative of an enhanced inflammatory state. Novel glycosylation changes in IgA were observed at N144/131. When combining glycosylation features across all three immunoglobulin isotypes, diagnostic performance in differentiating ATB from LTBI improved (AUC = 0.808), suggesting added value beyond IgG alone. Conclusion: Our findings demonstrate that glycosylation changes in IgA and IgM accompany active TB and are not limited to IgG. These alterations reveal broader humoral immune modulation in TB. In elderly individuals, where clinical differentiation of TB status is especially challenging, IgA and IgM glycosylation may warrant greater attention in both research and diagnostic contexts. immunoglobulin glycosylation tuberculosis active TB latent TB LC-MS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Tuberculosis (TB), caused by Mycobacterium tuberculosis ( Mtb ) infection, remains a significant global health threat, responsible for 1.3 million deaths in 2022 [ 1 , 2 ]. Patients are classified as having active TB (ATB) or latent TB infection (LTBI) based on clinical symptoms and bacterial activity; in LTBI, bacteria remain dormant without active replication [ 3 ]. Patients with ATB exhibit symptoms such as cough, fever, fatigue, and weight loss, and are potentially contagious [ 4 ]. In contrast, patients with LTBI show no clinical symptoms, imaging findings, or microbiological evidence [ 3 ]. However, approximately 5–15% of patients will develop TB reactivation and progress to ATB [ 5 ]. In terms of disease control, accurate diagnosis, treatment, and management of TB infections are of the utmost importance, particularly when providing distinct medical treatments for patients with LTBI and ATB. Currently, diagnostic tools for TB infection primarily rely on tuberculin skin tests (TST) and interferon-gamma (IFN-γ) release assays (IGRA) [ 6 ]. However, these tests cannot effectively differentiate between LTBI and ATB states. While chest radiography, sputum acid-fast bacillus smears, sputum culture for Mtb , and molecular diagnostics have been utilized for ATB diagnosis, challenges arise due to the limited availability of sputum samples in some patients, the lengthy time required for bacterial culturing [ 7 ], and the inability to identify extrapulmonary TB using these diagnostic methods. Predicting the reactivation and progression of untreated LTBI in the absence of clinical symptoms poses a significant challenge. The shift from LTBI to a reactivated state can turn patients into inadvertent carriers of the disease, thereby increasing public health concerns. Aging is an important risk factor for an increased incidence of ATB. During aging, we face the challenges of lower lung function, immunosenescence or chronic inflammation, low tolerance to anti-TB drugs, a higher possibility of adverse drug reactions, and more comorbidities [ 8 , 9 ]. A special focus on the elderly population, including a better understanding of the molecular and cellular mechanisms of lung aging and infection, and incorporating more predictive factors, is recommended for disease control [ 8 , 10 ]. To address this issue and enhance disease control measures, the development of supplementary biomarkers capable of predicting disease progression holds immense promise. Antibodies, also known as immunoglobulins, consist of two regions: the fragment crystallizable region (Fc) and the fragment antigen-binding region (Fab) [ 11 ]. Figure 1 A shows the glycosylation sites of IgG, IgA, and IgM, as well as the major glycoforms, including high-mannose, hybrid type, fucosylation (F), bisection (B), galactosylation (Gal)/agalactosylation (G0), and sialylation (S) [ 12 – 14 ]. Antibody glycosylation affects the conformation, stability, and affinity to corresponding targets [ 15 ]. Glycosylation of the Fc region triggers immune responses such as inflammation, antibody-dependent cell-mediated cytotoxicity (ADCC), and antibody-dependent cellular phagocytosis (ADCP) by interacting with receptors on effector cells [ 16 , 17 ]. IgG from patients with LTBI contained less fucose but more galactose and sialic acid than IgG from patients with ATB, which presented fewer inflammatory features in the LTBI group [ 18 ]. Glycosylation of the Fc domain also showed the potential to discriminate between LTBI and ATB infections, and galactosylation profiles were evaluated as auxiliary diagnostic biomarkers [ 19 , 20 ]. Distinct IgG glycosylation and antigen-specific antibodies have also been associated with TB drug treatment [ 21 ]. The average age of the participants in these studies was approximately 25–35, and we found limited reports regarding antibody molecular profiles in older TB patients. Despite the extensive focus on IgG glycoprofiles, other immunoglobulin isotypes are relatively understudied. Kumagai et al. reported changes in mouse IgM glycosylation associated with TB infection [ 22 ], which also triggered our attention to human antibody isotypes. Building on this limited knowledge from clinical samples, our study aimed to broaden the understanding of antibody glycosylation patterns in tuberculosis infection, especially in elderly patients. We investigated the antibody levels and Fc glycosylation patterns of three antibody isotypes (IgG, IgA, and IgM) in control subjects, patients with LTBI, and patients with ATB. Although the study primarily focused on bulk antibody profiles owing to the limited antigen-specific antibodies available from plasma samples, it marks the first instance of reporting antibody glycosylation changes across the three primary isotypes in elderly patients with TB. 2. Experimental Procedures 2.1 Study population Overall, 59 individuals were recruited from Taipei Municipal Wan Fang Hospital (Taipei, Taiwan), including 22 patients with ATB, 17 patients with LTBI, and 20 controls ( Table 1 ) . The age and sex distributions of the three groups were similar. The average ages are 54.1, 55.8, and 60.2 in the controls, LTBI, and ATB groups, respectively. ATB diagnosis was based on a positive Mtb sputum culture result. LTBI was defined by a positive IGRA exhibiting no symptoms nor radiographic evidence of ATB. Controls were confirmed to have negative IGRA results. This study was approved by the research ethics committee (IRB number: N201903025). Signed informed consent was obtained from each participant recruited in the study. Table 1 Basic characteristics of Controls, LTBI and ATB patients. Statistics of age distribution was calculated using one-way ANOVA. Statistics of gender distribution was calculated using chi-squared test. Controls LTBI ATB P -value (n = 20) (n = 18) (n = 20) Age 54.1 ± 8.8 55.8 ± 13.3 60.2 ± 18.8 0.3942 (Mean ± SD) Gender 13/7 12/6 14/6 0.9430 (Male / Female) 2.2 Chemicals and reagents Affinity beads, CaptureSelect™ KappaXL Affinity Matrix and CaptureSelect™ LC-lambda (Hu) Affinity Matrix were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Ammonium bicarbonate, dithiothreitol (DTT), iodoacetamide (IAA), and formic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA). Acetonitrile (ACN) was purchased from J. T. Baker (Phillips, NJ, USA). Phosphate buffered saline (PBS) was obtained from VWR International, LLC (PA, USA). Trypsin and Glu-C were purchased from Promega (Madison, WI, USA). Stable isotope-labeled peptides, used as internal standards, were synthesized by Genomics (New Taipei, Taiwan). 2.3 Immunoglobulin purification and protein digestion An overview of the workflow is presented in Fig. 1 B. To purify all classes of immunoglobulins from human plasma, affinity purification beads, CaptureSelect™ KappaXL Affinity Matrix and CaptureSelect™ LC-lambda (Hu) Affinity Matrix, were used to capture the constant region of immunoglobulin light chains [ 23 ]. The two types of bead slurry were mixed in a 1:1 ratio. Next, 40 µL of the mixed slurry were conditioned with 150 µL PBS twice, and 16 µL of plasma was added to beads in 184 µL of PBS. Two Ig purification samples were prepared from each plasma sample for further dual-enzyme digestion. The samples were incubated at 4°C overnight on a mixer. After incubation, the supernatants were removed, and the beads were washed with 150 µL PBS twice to prevent nonspecific binding. After the immunoglobulins were purified from human plasma, on-bead enzymatic protein digestion was performed. Fifty microliters of 50 mM ammonium bicarbonate were added, containing four stable isotope-labeled peptides as internal standards (IS): 240 ng of IS 1, 200 ng of IS 2, 400 ng of IS 3, and 1 ng of IS 4. One microliter of 550 mM DTT was added to the solution as a reducing reagent and incubated for 45 min at 56°C to disrupt the disulfide bonds in the protein. Then, 2 µL of 450 mM IAA was added as the alkylating reagent and incubated for 45 min in the dark at room temperature. Duplexed Ig purification samples prepared from each plasma sample were treated with trypsin and Glu-C/trypsin in parallel. Specifically, one Ig purification sample was treated sequentially with 5 µL of Glu-C (0.1 µg/µL) and incubated for 1 hour at 37°C on a desktop shaker (300 rpm model CB-1703, CLUBIO, Taiwan). After the Glu-C digestion, 5 µL of trypsin (0.2 µg/µL) was added, and the sample was incubated overnight. The other Ig purification sample was treated with trypsin alone, without prior Glu-C treatment, and also incubated overnight. At the end of the on-bead digestion, 6 µL of 10% formic acid was added to stop the enzymatic digestion reaction. For LC-MS/MS analysis, the samples were centrifuged at 12,000 rpm (approximately 13,800×g) for 10 min, and equal volumes of supernatants from each paired sample with and without Glu-C treatment were pooled together. Because of their relatively high concentration in human plasma, the pooled samples were diluted five-fold with 0.1% formic acid as an additional step for IgG peptide and glycopeptide analysis. 2.4 LC-MS/MS glycopeptides analysis A Xevo TQ-XS Triple Quadrupole Mass Spectrometry system (Waters Corporation, Milford, MA, USA) was used to analyze the targets. For chromatographic separation, a Core-Shell C18 Kinetex column with 50 mm of length, 2.1 mm of internal diameter, and 2.6 µm of particle size was used. The mobile phase comprised solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in ACN). The flow rate was set to 0.3 mL/min. For IgG peptide and glycopeptide analyses, the following elution gradient was used: 2% solvent B at 0 min and for the first 0.5 min, then organic solvent B was increased to 50% at 8 min and lasted for 0.5 min, and then increased to 100% at 9 min. After maintaining the mobile phase at 100% solvent B for 2 min, the gradient was restored to 2% solvent B at 11.5 min and maintained for 1.5 min to achieve system equilibration. For IgA, IgM peptide, and glycopeptide analyses, the elution gradient was set as follows: 2% solvent B at 0 min and for the first 0.5 min, then solvent B was increased to 30% at 9 min and 35% at 9.5 min. Solvent B was increased to 100% after 10 min and maintained at 100% for 1 min. The gradient was then restored to 2% solvent B at 11.5 min and maintained for 1.5 min to achieve system equilibration. The samples were stored in an autosampler set at 4°C, and the column oven was set at 40°C. The injection volume was 1 µL for IgG analytes and 5 µL for IgA and IgM analytes with Waters partial loop injection mode was used. The parameters of electrospray ionization positive mode were set as follows: capillary voltage was 2.5 kV, source offset was 30 V, source temperature was 150°C, desolvation temperature was 450°C, collision gas flow was 0.15 mL/min, and the nebulizer gas flow was 7 bar. The optimal MRM transitions, retention times, cone voltages, and collision energies for IgG, IgA, and IgM peptides and glycopeptides can be found in our previously published study [ 23 ]. 2.5 Statistical and data analysis Basic statistical analysis and graphing were performed using GraphPad Prism 8 for Windows version 8.0.2 (GraphPad Software, La Jolla, California, USA). Comparisons between each combination of ATB, LTBI, and controls were performed using the nonparametric unpaired Mann-Whitney test. Sparse partial least squares discriminant analysis (sPLS-DA) and multivariate receiver operating characteristic (ROC) curve analysis were performed using MetaboAnalyst 6.0 ( https://www.metaboanalyst.ca/ ). The normalized responses of immunoglobulin glycopeptides (the responses of glycopeptides divided by the responses of the corresponding antibody subclasses) were used in these two analyses. The ROC curves were generated using the random forest algorithm as the classification method and built-in random forest as the feature ranking method. 3. Results 3.1 Distinct IgG glycosylation of ATB, LTBI, and control groups Plasma levels of total IgG and IgG subclasses were not very different among the controls, patients with LTBI, and patients with ATB (Supplementary Fig. 1A) . The only IgG subclass that showed a marginally significant difference was IgG2 ( p = 0.051), with higher levels observed in the control group than in the LTBI group. When we focused on the 20 IgG glycosylation traits, we observed similar glycosylation profiles in the control and LTBI groups, whereas the ATB group showed distinct glycosylation patterns. On the score plot of sPLS-DA, sample clusters of the control and LTBI groups overlapped, and the ATB sample cluster was slightly shifted to the right side of the plot (Fig. 2 A). The heatmap (Fig. 2 B) shows that the ATB group had higher fucosylation, but lower galactosylation of IgG1 and IgG2. In contrast, the control and LTBI groups showed increased galactosylation and sialylation, especially for IgG1 and IgG2 subclasses. These results were supported and validated by statistical analysis; the boxplots are shown in Fig. 2 C and Supplementary Fig. 1 . Regarding the monogalactosylation and digalactosylation of IgG subclasses, we also observed a decreasing trend in the ATB group (Fig. 2 C). The fucosylation levels were significantly higher in the ATB group than in IgG1, but not IgG2 ( Supplementary Fig. 1B ). IgG1 sialylation was not significantly different among the three groups; however, IgG2 sialylation was higher in the control group than in the LTBI group. IgG3/4 sialylation levels were also higher in the control group than in the ATB group ( Supplementary Fig. 1C ). Bisection was only found to be lower in the ATB group with IgG3/4 ( Supplementary Fig. 1D ). As shown in Fig. 2 D, three groups of glycosylation traits were identified based on their correlations: (1st ) Fucosylation and Agalactosylation; (2nd ) Bisection; (3rd ) Galactosylation and Sialylation. The 1st group of glycosylation traits was higher in the ATB group, which was the opposite of the 3rd group of glycosylation traits. The latter was higher in the control and LTBI groups (Fig. 2 E). Although it is more complicated to investigate the biological functions of individual glycopeptides, we summarized 11 out of 26 IgG glycopeptides that were statistically different among the three groups ( Supplementary Fig. 2 ). Similar to our investigation of glycosylation traits, glycopeptides carrying more galactose, such as IgG1 H5N4, IgG1 H5N5F1, and IgG2 H5N4F1 were upregulated in the control group. Glycopeptides carrying fucose but less galactose, such as IgG1 H3N4F1 and IgG2 H3N4F1, were higher in the ATB group. The response of the glycopeptides was normalized to the response of their respective IgG subclasses; therefore, the comparison was not affected by the protein level in each sample. 3.2 Significant differences in the level of IgA subclasses and glycosylation profiles Neither IgA1 nor IgA2 levels were significantly different between the control, LTBI, and ATB groups (Supplementary Fig. 3A) . Regarding the IgA glycosylation traits, the glycosylation patterns were similar between the LTBI and control groups, whereas the ATB group had relatively different glycosylation profiles, which resulted in the sample cluster shifting to the right side of the sPLS-DA score plot (Fig. 3 A). Notably, distinct galactosylation-related profiles were found only in the IgA1/2-N144/131 position, rather than in IgA2-N205 or IgA1/2-N340/327 (Fig. 3 B and 3 C, Supplementary Fig. 3C ). As shown in Fig. 3 C, on IgA1/2-N144/131, patients with ATB showed lower total galactosylation, which resulted from a combination of lower digalactosylation and higher monogalactosylation. We assumed that the galactosylation profile at this site shifted from digalactosylation to mono- or nongalactosylation (agalactosylation) in the ATB group. The lower galactosylation trend in the ATB group was similar to that observed for the IgG isotype. When we investigated the correlations among different glycosylation traits, the sialylation and digalactosylation of N144/131 had higher positive correlation coefficients with galactosylation (Fig. 3 D), which were clustered together in Fig. 3 E. In contrast, the monogalactosylation and agalactosylation of N144/131 were negatively correlated with galactosylation and clustered independently in Fig. 3 E. Other glycosylation traits of IgA, such as fucosylation, sialylation, and bisection, were similar among the three groups ( Supplementary Fig. 3B-3D ). We have also reported distinct glycopeptides for the IgA isotype. Six IgA glycopeptides with glycosylation site, IgA1/2-N144/131, were found to be more abundant in ATB than in LTBI ( Fig. 3 F ) : H3N5, H4N4S1, H4N5, H4N5S1, H5N2, and H5N3S1. Compared to controls, patients with ATB showed higher IgA1/2-N144/131 H3N5, H4N4S1, and H4N5S1, and lower IgA1/2-N144/131 H5N5S1 and IgA2-N205 H5N5F1 levels. To discriminate between LTBI and controls, significantly decreased IgA1/2-N144/131 H4N5S1 and H5N5S1 levels were found in LTBI groups. 3.3 Lower galactosylation of IgM was identified in the ATB group No significant differences were observed in plasma levels of IgM among the control, LTBI, and ATB groups (data not shown) . However, when 29 IgM glycosylation traits were input into the sPLS-DA analysis, a significant cluster shift was observed in the ATB group (Fig. 4 A, red cluster ). In Fig. 4 B, we constructed a heatmap of the top 10 features that showed significant differences among the three groups. Galactosylation of IgM-N171, N332, and N395 was lower, whereas N71 fucosylation and sialylation were higher in the ATB group. These findings were supported by the results of the statistical analysis, and the box plots are summarized in Fig. 4 C– 4 E, and 4 H. Furthermore, we observed lower monogalactosylation, higher mannosylation, and lower hybrid-type glycosylation on IgM N402 in the ATB group ( Fig. 4 F ) . For N563, only agalactosylation was significantly higher in ATB, which also represents lower galactosylation at this site (Fig. 4 G ) . Looking into the details, we observed special patterns of monogalactosylation and digalactosylation for N171, N332, and N395 (Fig. 4 C to 4 E). Increased monogalactosylation was accompanied by decreased digalactosylation in the ATB group. This trend highlighted the shift from digalactosylation to monogalactosylation or agalactosylation, which resulted in reduced galactosylation in the ATB group. The inversely proportional relationships between (di)galactosylation and monogalactosylation at the three N-glycosylation sites can also be found in Fig. 4 I and 4 J. Seven IgM glycopeptides showed the potential ability to discriminate ATB from LTBI, as they increased significantly in ATB (Supplementary Fig. 4) : N171 H4N3F1S1, N171 H5N3F1S1, N171 H6N3F1S1, N402 H9N2, N563 H3N5F1, N71 H5N4S2, and N71 H5N4F1S2. Among them, N171 H4N3F1S1, N402 H9N2, and N71 H5N4F1S2 also significantly increased in patients with ATB when compared with controls. 3.4 Improved differential ability for ATB and LTBI beyond IgG glycosylation After comparing the differences in Ig glycosylation profiles among the control, LTBI, and ATB groups, we evaluated whether incorporating IgA and IgM glycosylation traits into IgG glycosylation could improve the differential ability for ATB and LTBI. We first imported all Ig glycosylation traits into the Statistical Analysis tool in MetaboAnalyst 6.0, and filtered out 18 glycosylation traits that showed statistical differences between the ATB and LTBI groups. Among the 18 glycosylation traits, 7 belonged to IgG, 4 belonged to IgA, and 6 belonged to IgM. Since most of the literature has focused on the differences in IgG glycosylation traits, we used seven significantly different IgG traits to generate ROC curves. As shown in Fig. 5 A, the AUC ranged from 0.646 to 696, and the use of two glycosylation traits provided the best AUC (red curve). In Fig. 5 B, we highlight the samples that were incorrectly classified using two IgG glycosylation traits (IgG1-Agalactosylation and IgG1-Galactosylation). Among the 20 ATB samples, 7 samples were incorrectly classified; among the 18 LTBI samples, 7 samples were incorrectly classified. We further added IgA and IgM glycosylation traits to generate ROC curves; the AUCs ranged from 0.714 to 0.808, and 18 variables provided the best performance (Fig. 5 C). While using the 18 variables to predict the classification, 8 out of 20 ATB samples were incorrectly classified, and 3 out of 18 LTBI samples were incorrectly classified (Fig. 5 D). Notably, we need to highlight that the incorrectly classified samples were not overlapped in Fig. 5 B and 5 D. When we evaluated the univariate of the glycosylation traits in GraphPad Prism software, all 18 glycosylation traits showed AUCs higher than 0.736, among which IgG1-Fucosylation provided the highest AUC of 0.799 ( Supplementary Fig. 5 ). 4. Discussion The addition of N-glycans, such as galactosylation, sialylation, fucosylation, and bisection, to the Fc domain of antibodies is an important post-translational modification that can greatly affect immune functions. For TB infection, specifically, Lu et al. have demonstrated that the in vitro protective functions of IgG, including ADCP and ADCC, were associated with distinct glycosylation profiles in the IgG Fc region [ 18 ]. IgG galactosylation structures can change quickly in one’s inflammatory status [ 15 ]. Galactose-lacking IgG glycoforms possess proinflammatory activity by binding to mannose-binding lectin (MBL) and subsequently activating complement via alternative and lectin pathways [ 24 ]. Galactosylation levels are decreased in various infectious diseases, including TB, which may provide another means for TB diagnosis [ 15 ]. Liu et al . demonstrated the diagnostic value of a high IgG G0/(G1 + G2×2) ratio for ATB infections [ 20 ]. Furthermore, IgG galactosylation levels varied significantly between patients with LTBI and those with ATB. Lu et al. reported significantly more digalactosylated IgG glycoforms in individuals with LTBI than in those with ATB [ 18 ]. These may reflect that controlled LTBI does not induce as much inflammatory activity as ATB [ 25 , 26 ]. Consistent with previous studies, our study revealed a significant decrease in IgG galactosylation levels in the ATB group ( Fig. 2 ) , indicating a pro-inflammatory status in patients with active infection. Furthermore, we found that galactosylation changes were not limited to IgG but were also observed in IgA and IgM glycans. These novel findings suggest that IgA and IgM may also contribute to inflammation through glycosylation and have regulatory effects similar to those of IgG. Notably, we found distinct galactosylation patterns for IgA1/2-N144/131, IgM-N171, IgM-N332, and IgM-N395 in ATB: increased monogalactosylation and decreased digalactosylation ( Fig. 3 C, 4 C to 4 E ) . This may reflect a shift from digalactosylation to monogalactosylation or agalactosylation, resulting in an overall decrease in the galactosylation levels. The sialylation of IgG is responsible for its anti-inflammatory activity. Two known mechanisms mediate the anti-inflammatory effects: 1) activation of the inhibitory FcγRIIB via dendritic cell-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN) and 2) decreased affinity for activating FcγRIIIA on NK cells, resulting in reduced ADCC [ 15 , 24 ]. Decreased IgG sialylation has been observed in several pro-inflammatory diseases, such as rheumatoid arthritis, HIV infection, and hepatitis B [ 15 , 26 ]. Regarding TB infection, significantly decreased IgG sialylation has been reported in patients with ATB compared to LTBI [ 18 ]. This reduced sialylation, together with the above-mentioned galactosylation profiles, may indicate an active inflammatory response in ATB infection compared to the controlled LTBI state [ 25 , 26 ]. Although we did not observe a significant decrease in IgG1 sialylation in the ATB group, we observed a decrease not only in IgG3/4 of ATB individuals but also in IgG2 of LTBI individuals when compared to controls (Supplementary Fig. 1C) . The activated immune response indicated by the decrease in IgG2 sialylation might be associated with a better defense mechanism against bacteria in patients with LTBI, as IgG2, a poor complement activator, is responsible for the bacterial capsular polysaccharide antigen response [ 27 , 28 ]. Core fucose is present in over 90% of serum IgG. The lack of core fucose on IgG glycans results in a significant increase in ADCC due to up to 100-fold enhanced affinity for the activating FcγRIIIA and FcγRIIIB [ 15 ]. Lu et al. reported that IgG isolated from individuals with LTBI contained less fucose [ 18 ]. Meanwhile, they did find higher binding of IgG to FcγRIIIA along with enhanced PPD-specific ADCC in LTBI. These unique features may be responsible for the enhanced killing of intracellular Mtb by infected macrophages, indicating that distinct Fc glycosylation patterns in LTBI are associated with enhanced Mtb control [ 26 ]. In contrast, Liu et al. found that IgG afucosylated glycans did not differ significantly between patients with ATB and healthy donors [ 20 ]. In addition to the relatively well-studied IgG glycoprofiles, Kumagai et al. used a mouse infection model to characterize the changes in IgM glycosylation [ 22 ]. They observed a > 5-fold increase in IgM core fucosylation after Mtb infection in BCG-naïve mice. Notably, BCG vaccination attenuated this increase. Consistent with the literature, our study found that patients with ATB had higher levels of IgG1 fucosylation than those with LTBI and controls (Supplementary Fig. 1B) . However, we did not observe significant differences in IgA and IgM fucosylation at most N-glycosylation sites, except for IgM N71 (Supplementary Fig. 3C, 3D and Fig. 4 H ) , which is inconsistent with a previous study in mice [ 22 ]. Bisecting GlcNAc indirectly affects the antibody effector function by inhibiting the addition of fucose at the glycan synthesis level [ 26 ]. As a result, although to a lower degree, the presence of bisecting GlcNAc had similar effects as the lack of fucose. That is, a bisecting GlcNAc on IgG is associated with greater affinity for FcγRIII and consequently enhances ADCC activity [ 24 ]. Decreased levels of IgG-bisecting glycans in patients with ATB compared to healthy donors have been reported by Liu et al. [ 20 ]. Reasonably, in patients with ATB, IgG bisection and fucosylation change in opposite directions, thus having the same ADCC-modulating effect. However, Lu et al. did not observe a significant difference in bisecting GlcNAc between patients with LTBI and ATB [ 18 ]. Consistent with the findings of Liu et al. , our study observed significantly lower levels of IgG3/4 bisecting glycans in ATB group than in LTBI and control groups (Supplementary Fig. 1D) . Similar to fucosylation, the levels of bisecting GlcNAc in IgA and IgM appeared to have little association with TB infection (Supplementary Fig. 3B to 3D; data not shown for IgM) . Our study has some limitations that should be considered. Firstly, the relatively small sample size may limit the generalizability of our findings to larger populations. Secondly, we analyzed bulk immunoglobulins instead of TB-specific immunoglobulins, which may have affected the representativeness of our results. Thirdly, several N-glycosylation sites were evaluated using a limited number of glycopeptides, which may have hindered the exact percentages of glycosylation traits. 5. Conclusions Our study introduces a novel approach to investigate the site-specific glycosylation profiles of IgG, IgA, and IgM, aiming to distinguish between patients with LTBI and those with ATB. In terms of glycosylation changes in bulk IgG, our findings in the elderly population show trends consistent with those observed in younger individuals in previous studies. Furthermore, our study is the first to reveal significant alterations in IgA and IgM glycosylation profiles in TB patients. By combining the glycosylation analysis of IgG, IgA, and IgM, we achieved improved classification performance, highlighting the importance of incorporating multiple immunoglobulins, beyond IgG, in diagnostic assessments. Despite some limitations, our study represents a crucial step toward better understanding the roles of IgA and IgM glycosylation in TB infection. Abbreviations Tuberculosis, TB; Mycobacterium tuberculosis, Mtb; Active TB, ATB; Latent TB infection, LTBI; Immunoglobulin, Ig; Liquid chromatography-mass spectrometry, LC-MS/MS; Receiver operating characteristic, ROC; Area under the ROC curve, AUC; Tuberculin skin tests, TST; Interferon-gamma, IFN-γ; IFN-γ release assays, IGRA; Fragment crystallizable region, Fc; Fragment antigen-binding region, Fab; Antibody-dependent cell-mediated cytotoxicity, ADCC; Antibody-dependent cellular phagocytosis, ADCP; Dithiothreitol, DTT; Acetonitrile, ACN; Iodoacetamide, IAA; Phosphate buffered saline, PBS; Internal standards, IS. Declarations Author Contribution Yun-Jung Yang: Writing - Original Draft, Formal analysis Chih-Hsin Lee: Writing - Original Draft, Conceptualization, Resources Yung-Kun Chuang: Resources San-Yuan Wang: Resources Michael X. Chen: Writing - Review & Editing Hsih-Chang Shih: Writing - Original Draft I-Lin Tsai: Writing - Review & Editing, Conceptualization, Funding acquisition, Supervision Acknowledgement This study was funded by the Ministry of Science and Technology, Taiwan (grant number 108-2320-B-038-060-MY3). The authors are grateful for the technical support provided by the TMU Core Facility. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Chai Q, Zhang Y, Liu CH. Mycobacterium tuberculosis: An Adaptable Pathogen Associated With Multiple Human Diseases. Front Cell Infect Microbiol. 2018;8:158. World Health Organization. Tuberculosis. [Internet] 2021; Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis Lee SH. Tuberculosis Infection and Latent Tuberculosis. Tuberc Respir Dis (Seoul). 2016;79(4):201–6. Nathavitharana RR, et al. Diagnosing active tuberculosis in primary care. BMJ. 2021;374:n1590. Getahun H, et al. Management of latent Mycobacterium tuberculosis infection: WHO guidelines for low tuberculosis burden countries. Eur Respir J. 2015;46(6):1563–76. Gong W, Wu X. Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy. Front Microbiol. 2021;12:745592. Li TL, et al. Acquired Resistance to Isoniazid During Isoniazid Monotherapy in a Subject with Latent Infection Following Household Rifampicin-Resistant Tuberculosis Contact: A Case Report. Infect Drug Resist. 2021;14:1505–9. Olmo-Fontánez AM, Turner J. Tuberculosis in an Aging World. Pathogens, 2022. 11(10). Lee C-H, et al. Treatment delay and fatal outcomes of pulmonary tuberculosis in advanced age: a retrospective nationwide cohort study. BMC Infect Dis. 2017;17(1):449. Li SJ, et al. Population aging and trends of pulmonary tuberculosis incidence in the elderly. BMC Infect Dis. 2021;21(1):302. Maverakis E, et al. Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: a critical review. J Autoimmun. 2015;57:1–13. Cobb BA. The history of IgG glycosylation and where we are now. Glycobiology. 2020;30(4):202–13. Dotz V, et al. O- and N-Glycosylation of Serum Immunoglobulin A is Associated with IgA Nephropathy and Glomerular Function. J Am Soc Nephrol. 2021;32(10):2455–65. Arnold JN, et al. Human serum IgM glycosylation: identification of glycoforms that can bind to mannan-binding lectin. J Biol Chem. 2005;280(32):29080–7. Gudelj I, Lauc G, Pezer M. Immunoglobulin G glycosylation in aging and diseases. Cell Immunol. 2018;333:65–79. Subedi GP, Barb AW. The Structural Role of Antibody N-Glycosylation in Receptor Interactions. Structure. 2015;23(9):1573–83. Wang X, Mathieu M, Brezski RJ. IgG Fc engineering to modulate antibody effector functions. Protein Cell. 2018;9(1):63–73. Lu LL, et al. A Functional Role for Antibodies in Tuberculosis. Cell. 2016;167(2):433–e44314. Lu LL, et al. Antibody Fc Glycosylation Discriminates Between Latent and Active Tuberculosis. J Infect Dis. 2020;222(12):2093–102. Liu P, et al. Quantitative analysis of serum-based IgG agalactosylation for tuberculosis auxiliary diagnosis. Glycobiology. 2020;30(9):746–59. Grace PS, et al. Antibody Subclass and Glycosylation Shift Following Effective TB Treatment. Front Immunol. 2021;12:679973. Kumagai T et al. Serum IgM Glycosylation Associated with Tuberculosis Infection in Mice. mSphere, 2019. 4(2). Cheng YH, et al. Multiplexed Antibody Glycosylation Profiling Using Dual Enzyme Digestion and Liquid Chromatography-Triple Quadrupole Mass Spectrometry Method. Mol Cell Proteom. 2024;23(2):100710. Štambuk T, et al. N-glycans as functional effectors of genetic and epigenetic disease risk. Mol Aspects Med. 2021;79:100891. McLean MR, et al. An Inflammatory Story: Antibodies in Tuberculosis Comorbidities. Front Immunol. 2019;10:2846. Irvine EB, Alter G. Understanding the role of antibody glycosylation through the lens of severe viral and bacterial diseases. Glycobiology. 2020;30(4):241–53. Redpath S, et al. Activation of complement by human IgG1 and human IgG3 antibodies against the human leucocyte antigen CD52. Immunology. 1998;93(4):595–600. Vidarsson G, Dekkers G, Rispens T. IgG subclasses and allotypes: from structure to effector functions. Front Immunol. 2014;5:520. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6381409","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450545933,"identity":"cd6bad83-36a9-496f-b362-5f3b7d9cde1b","order_by":0,"name":"Yun-Jung Yang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun-Jung","middleName":"","lastName":"Yang","suffix":""},{"id":450545934,"identity":"2873ee7f-1d5e-45e5-b467-59c5d278a8b8","order_by":1,"name":"Chih-Hsin Lee","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chih-Hsin","middleName":"","lastName":"Lee","suffix":""},{"id":450545935,"identity":"f611474c-bc4e-4c1f-a758-f13a5d4729a9","order_by":2,"name":"Yung-Kun Chuang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yung-Kun","middleName":"","lastName":"Chuang","suffix":""},{"id":450545936,"identity":"f06c6bb7-3153-4b21-874b-1b5d38eeb640","order_by":3,"name":"San-Yuan Wang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"San-Yuan","middleName":"","lastName":"Wang","suffix":""},{"id":450545937,"identity":"730253f9-f9ee-4c8a-bb60-7ac26d025bf3","order_by":4,"name":"Michael X. Chen","email":"","orcid":"","institution":"The University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"X.","lastName":"Chen","suffix":""},{"id":450545938,"identity":"e49e3163-0ced-458e-bf4f-1837c79f7008","order_by":5,"name":"Hsi-Chang Shih","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hsi-Chang","middleName":"","lastName":"Shih","suffix":""},{"id":450545939,"identity":"312f9553-a6d4-4cce-9509-5604a4330d68","order_by":6,"name":"I-Lin Tsai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACNjAqkODhh/CZidViYCEn2QbTwkaULoMKY4NjxGrh4z/87MEHA4nEzfe70yQYKqwTG+R7DPBbIZFmbjgDqGXbMd5tEgxn0hMb2HgIaWEwk+aBaWFsOwzUwrsBvxb+49+k/4Ac1gbS8o8YLQw5ZtIMBhLGBmwgLQ3EaJHIKTfsMZCQkziWu9ki4Vi6cRtb/ge8WuT7j2978KOijoe/+ezGGx9qrGX7mY8l4NWCChIYiIrJUTAKRsEoGAWEAAAN0zxwBUD/hgAAAABJRU5ErkJggg==","orcid":"","institution":"Taipei Medical University","correspondingAuthor":true,"prefix":"","firstName":"I-Lin","middleName":"","lastName":"Tsai","suffix":""}],"badges":[],"createdAt":"2025-04-05 10:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6381409/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6381409/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81954692,"identity":"c11a66b6-6425-481c-992e-7cc38f82296b","added_by":"auto","created_at":"2025-05-05 09:44:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDemonstration of N-glycan types and N-glycosylation sites on human immunoglobulin and the experimental design. \u003c/strong\u003e(A) Black spots indicate the N-glycosylation sites on the IgG, IgA, and IgM isotypes with the sequence number of asparagine. Six major N-glycan types including high-mannose, hybrid type, bisection, fucosylation, galactosylation, and sialylation. (B) Three groups of clinical samples from the controls, patients with latent TB infection (LTBI), and patients with active TB (ATB) infection. Plasma samples were incubated with immunoglobulin affinity beads followed by on-bead digestion. Trypsin or Glu-C/Trypsin were used for immunoglobulin digestion, and the supernatants were collected for UHPLC-MS/MS analysis. Glycosylation traits were calculated and used for between group comparison.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/a443e1b47e2a70ad6f71599a.jpg"},{"id":81954687,"identity":"20b2dfc4-1b69-4e1a-8bdb-afa9697656fe","added_by":"auto","created_at":"2025-05-05 09:44:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential IgG glycosylation profiles among the control, latent tuberculosis infection (LTBI), and active TB (ATB) groups.\u003c/strong\u003e(A) Score plot of the sPLS-DA analysis. Red: ATB group; Green: control group; Blue: LTBI group. (B) Heatmap of the glycosylation profile differences among the three groups. (C) Boxplots and the statistical results of IgG galactosylation-related traits among the three groups. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. (D) Correlation heatmaps of the 20 IgG glycosylation traits. (E) Correlation analysis for different IgG glycosylation traits to the IgG1-Galactosylation.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/feabecf5bac3c3b033e5ed8a.jpg"},{"id":81955531,"identity":"d06b3eea-89e0-4faa-a034-4d6d9aa28b76","added_by":"auto","created_at":"2025-05-05 09:52:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistinct IgA glycosylation traits among the control, latent tuberculosis infection (LTBI), and active TB (ATB) groups. \u003c/strong\u003e(A) Score plot of the sPLS-DA analysis. Red: ATB group; Green: control group; Blue: LTBI group. (B) Heatmap of top 10 IgA glycosylation traits demonstrating the glycosylation profile differences among the three groups. (C) Boxplots and the statistical results of IgA1/2-N144/131 and IgA2-N205 galactosylation-related traits among the three groups. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. (D) Correlation analysis for different IgA glycosylation traits to the IgA1/2 N144/131-Galactosylation. (E) Correlation heatmaps of the 20 IgA glycosylation traits. (F) IgA glycopeptides which showed statistical differences among the three groups. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; **** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/015e2546f200f6d1c5d6924d.jpg"},{"id":81954689,"identity":"3701b9fb-efbc-4f87-924f-27d0da49d210","added_by":"auto","created_at":"2025-05-05 09:44:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDivergent IgM glycosylation traits among the control, latent tuberculosis infection (LTBI), and active TB (ATB) groups. \u003c/strong\u003e(A) Score plot of the sPLS-DA analysis. Red: ATB group; Green: control group; Blue: LTBI group. (B) Heatmap of top 10 IgM glycosylation traits demonstrating the glycosylation profile differences among the three groups. (C) Boxplots and the statistical results of IgM-N171 glycosylation traits among the three groups. (D) Boxplots and the statistical results of IgM-N332 galactosylation-related traits among the three groups. (E) Boxplots and the statistical results of IgM-N395 galactosylation-related traits among the three groups. (F) Boxplots and the statistical results of IgM-N402 glycosylation traits among the three groups. (G) Boxplot and the statistical results of IgM-N563 agalactosylation among the three groups. (H) Boxplots and the statistical results of IgM-N71 fucosylation and sialylation among the three groups. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01. (I) Correlation heatmaps of the 29 IgM glycosylation traits. (J) Correlation analysis for different IgM glycosylation traits to the IgM N171-Galactosylation.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/5a59d3c7cbc4d1f031130c23.jpg"},{"id":81954693,"identity":"1cd60b73-ee82-4805-8e45-e740ed7c9dc6","added_by":"auto","created_at":"2025-05-05 09:44:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curves and predicted class probabilities for models\u003c/strong\u003e\u003cbr\u003e\n(A) Multivariate ROC analysis using 2 to 7 IgG glycosylation traits to differentiate between the active TB (ATB) and latent TB infection (LTBI) groups. x-axis represents Specificity; y-axis represents Sensitivity. (B) Predicted class probabilities for each sample (ATB and LTBI) based on 2 IgG glycosylation traits, which achieved an AUC of 0.696 in panel (A). x-axis represents predicted class probabilities; y-axis represents individual samples. Filled circles denote ATB samples, while open circles represent LTBI samples. (C) Multivariate ROC analysis using 2 to 18 glycosylation traits, including IgA and IgM glycosylation traits, to differentiate between the ATB and LTBI groups. (D) Predicted class probabilities for each sample (ATB and LTBI) based on 18 glycosylation traits, which achieved an AUC of 0.808 in panel (C).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/cc4a44ab41e6e3e4bbb72b48.jpg"},{"id":81956803,"identity":"66fd8764-fdec-48c6-b291-20141abc0ea7","added_by":"auto","created_at":"2025-05-05 10:00:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1901659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/c532aaab-a94e-4c8b-ab87-18fc12f0b139.pdf"},{"id":81955530,"identity":"5c8df22e-fcea-4cc5-93c7-9bd592348462","added_by":"auto","created_at":"2025-05-05 09:52:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":707875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6381409/v1/ff29884bced59d5da081d66b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond IgG: Novel Insights into IgA and IgM Glycosylation in Tuberculosis and Their Role in Differentiating Infection Status","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB), caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eMtb\u003c/em\u003e) infection, remains a significant global health threat, responsible for 1.3\u0026nbsp;million deaths in 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Patients are classified as having active TB (ATB) or latent TB infection (LTBI) based on clinical symptoms and bacterial activity; in LTBI, bacteria remain dormant without active replication [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with ATB exhibit symptoms such as cough, fever, fatigue, and weight loss, and are potentially contagious [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In contrast, patients with LTBI show no clinical symptoms, imaging findings, or microbiological evidence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, approximately 5\u0026ndash;15% of patients will develop TB reactivation and progress to ATB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of disease control, accurate diagnosis, treatment, and management of TB infections are of the utmost importance, particularly when providing distinct medical treatments for patients with LTBI and ATB. Currently, diagnostic tools for TB infection primarily rely on tuberculin skin tests (TST) and interferon-gamma (IFN-γ) release assays (IGRA) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these tests cannot effectively differentiate between LTBI and ATB states. While chest radiography, sputum acid-fast bacillus smears, sputum culture for \u003cem\u003eMtb\u003c/em\u003e, and molecular diagnostics have been utilized for ATB diagnosis, challenges arise due to the limited availability of sputum samples in some patients, the lengthy time required for bacterial culturing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the inability to identify extrapulmonary TB using these diagnostic methods. Predicting the reactivation and progression of untreated LTBI in the absence of clinical symptoms poses a significant challenge. The shift from LTBI to a reactivated state can turn patients into inadvertent carriers of the disease, thereby increasing public health concerns.\u003c/p\u003e \u003cp\u003eAging is an important risk factor for an increased incidence of ATB. During aging, we face the challenges of lower lung function, immunosenescence or chronic inflammation, low tolerance to anti-TB drugs, a higher possibility of adverse drug reactions, and more comorbidities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A special focus on the elderly population, including a better understanding of the molecular and cellular mechanisms of lung aging and infection, and incorporating more predictive factors, is recommended for disease control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To address this issue and enhance disease control measures, the development of supplementary biomarkers capable of predicting disease progression holds immense promise.\u003c/p\u003e \u003cp\u003eAntibodies, also known as immunoglobulins, consist of two regions: the fragment crystallizable region (Fc) and the fragment antigen-binding region (Fab) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows the glycosylation sites of IgG, IgA, and IgM, as well as the major glycoforms, including high-mannose, hybrid type, fucosylation (F), bisection (B), galactosylation (Gal)/agalactosylation (G0), and sialylation (S) [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Antibody glycosylation affects the conformation, stability, and affinity to corresponding targets [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Glycosylation of the Fc region triggers immune responses such as inflammation, antibody-dependent cell-mediated cytotoxicity (ADCC), and antibody-dependent cellular phagocytosis (ADCP) by interacting with receptors on effector cells [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. IgG from patients with LTBI contained less fucose but more galactose and sialic acid than IgG from patients with ATB, which presented fewer inflammatory features in the LTBI group [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Glycosylation of the Fc domain also showed the potential to discriminate between LTBI and ATB infections, and galactosylation profiles were evaluated as auxiliary diagnostic biomarkers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Distinct IgG glycosylation and antigen-specific antibodies have also been associated with TB drug treatment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The average age of the participants in these studies was approximately 25\u0026ndash;35, and we found limited reports regarding antibody molecular profiles in older TB patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite the extensive focus on IgG glycoprofiles, other immunoglobulin isotypes are relatively understudied. Kumagai \u003cem\u003eet al.\u003c/em\u003e reported changes in mouse IgM glycosylation associated with TB infection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which also triggered our attention to human antibody isotypes. Building on this limited knowledge from clinical samples, our study aimed to broaden the understanding of antibody glycosylation patterns in tuberculosis infection, especially in elderly patients. We investigated the antibody levels and Fc glycosylation patterns of three antibody isotypes (IgG, IgA, and IgM) in control subjects, patients with LTBI, and patients with ATB. Although the study primarily focused on bulk antibody profiles owing to the limited antigen-specific antibodies available from plasma samples, it marks the first instance of reporting antibody glycosylation changes across the three primary isotypes in elderly patients with TB.\u003c/p\u003e"},{"header":"2. Experimental Procedures","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eOverall, 59 individuals were recruited from Taipei Municipal Wan Fang Hospital (Taipei, Taiwan), including 22 patients with ATB, 17 patients with LTBI, and 20 controls \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The age and sex distributions of the three groups were similar. The average ages are 54.1, 55.8, and 60.2 in the controls, LTBI, and ATB groups, respectively. ATB diagnosis was based on a positive \u003cem\u003eMtb\u003c/em\u003e sputum culture result. LTBI was defined by a positive IGRA exhibiting no symptoms nor radiographic evidence of ATB. Controls were confirmed to have negative IGRA results. This study was approved by the research ethics committee (IRB number: N201903025). Signed informed consent was obtained from each participant recruited in the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBasic characteristics of Controls, LTBI and ATB patients.\u003c/b\u003e Statistics of age distribution was calculated using one-way ANOVA. Statistics of gender distribution was calculated using chi-squared test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLTBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e54.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Male / Female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chemicals and reagents\u003c/h2\u003e \u003cp\u003eAffinity beads, CaptureSelect\u0026trade; KappaXL Affinity Matrix and CaptureSelect\u0026trade; LC-lambda (Hu) Affinity Matrix were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Ammonium bicarbonate, dithiothreitol (DTT), iodoacetamide (IAA), and formic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA). Acetonitrile (ACN) was purchased from J. T. Baker (Phillips, NJ, USA). Phosphate buffered saline (PBS) was obtained from VWR International, LLC (PA, USA). Trypsin and Glu-C were purchased from Promega (Madison, WI, USA). Stable isotope-labeled peptides, used as internal standards, were synthesized by Genomics (New Taipei, Taiwan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Immunoglobulin purification and protein digestion\u003c/h2\u003e \u003cp\u003eAn overview of the workflow is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. To purify all classes of immunoglobulins from human plasma, affinity purification beads, CaptureSelect\u0026trade; KappaXL Affinity Matrix and CaptureSelect\u0026trade; LC-lambda (Hu) Affinity Matrix, were used to capture the constant region of immunoglobulin light chains [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The two types of bead slurry were mixed in a 1:1 ratio. Next, 40 \u0026micro;L of the mixed slurry were conditioned with 150 \u0026micro;L PBS twice, and 16 \u0026micro;L of plasma was added to beads in 184 \u0026micro;L of PBS. Two Ig purification samples were prepared from each plasma sample for further dual-enzyme digestion. The samples were incubated at 4\u0026deg;C overnight on a mixer. After incubation, the supernatants were removed, and the beads were washed with 150 \u0026micro;L PBS twice to prevent nonspecific binding.\u003c/p\u003e \u003cp\u003eAfter the immunoglobulins were purified from human plasma, on-bead enzymatic protein digestion was performed. Fifty microliters of 50 mM ammonium bicarbonate were added, containing four stable isotope-labeled peptides as internal standards (IS): 240 ng of IS 1, 200 ng of IS 2, 400 ng of IS 3, and 1 ng of IS 4. One microliter of 550 mM DTT was added to the solution as a reducing reagent and incubated for 45 min at 56\u0026deg;C to disrupt the disulfide bonds in the protein. Then, 2 \u0026micro;L of 450 mM IAA was added as the alkylating reagent and incubated for 45 min in the dark at room temperature. Duplexed Ig purification samples prepared from each plasma sample were treated with trypsin and Glu-C/trypsin in parallel. Specifically, one Ig purification sample was treated sequentially with 5 \u0026micro;L of Glu-C (0.1 \u0026micro;g/\u0026micro;L) and incubated for 1 hour at 37\u0026deg;C on a desktop shaker (300 rpm model CB-1703, CLUBIO, Taiwan). After the Glu-C digestion, 5 \u0026micro;L of trypsin (0.2 \u0026micro;g/\u0026micro;L) was added, and the sample was incubated overnight. The other Ig purification sample was treated with trypsin alone, without prior Glu-C treatment, and also incubated overnight. At the end of the on-bead digestion, 6 \u0026micro;L of 10% formic acid was added to stop the enzymatic digestion reaction. For LC-MS/MS analysis, the samples were centrifuged at 12,000 rpm (approximately 13,800\u0026times;g) for 10 min, and equal volumes of supernatants from each paired sample with and without Glu-C treatment were pooled together. Because of their relatively high concentration in human plasma, the pooled samples were diluted five-fold with 0.1% formic acid as an additional step for IgG peptide and glycopeptide analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 LC-MS/MS glycopeptides analysis\u003c/h2\u003e \u003cp\u003eA Xevo TQ-XS Triple Quadrupole Mass Spectrometry system (Waters Corporation, Milford, MA, USA) was used to analyze the targets. For chromatographic separation, a Core-Shell C18 Kinetex column with 50 mm of length, 2.1 mm of internal diameter, and 2.6 \u0026micro;m of particle size was used. The mobile phase comprised solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in ACN). The flow rate was set to 0.3 mL/min.\u003c/p\u003e \u003cp\u003eFor IgG peptide and glycopeptide analyses, the following elution gradient was used: 2% solvent B at 0 min and for the first 0.5 min, then organic solvent B was increased to 50% at 8 min and lasted for 0.5 min, and then increased to 100% at 9 min. After maintaining the mobile phase at 100% solvent B for 2 min, the gradient was restored to 2% solvent B at 11.5 min and maintained for 1.5 min to achieve system equilibration.\u003c/p\u003e \u003cp\u003eFor IgA, IgM peptide, and glycopeptide analyses, the elution gradient was set as follows: 2% solvent B at 0 min and for the first 0.5 min, then solvent B was increased to 30% at 9 min and 35% at 9.5 min. Solvent B was increased to 100% after 10 min and maintained at 100% for 1 min. The gradient was then restored to 2% solvent B at 11.5 min and maintained for 1.5 min to achieve system equilibration.\u003c/p\u003e \u003cp\u003eThe samples were stored in an autosampler set at 4\u0026deg;C, and the column oven was set at 40\u0026deg;C. The injection volume was 1 \u0026micro;L for IgG analytes and 5 \u0026micro;L for IgA and IgM analytes with Waters partial loop injection mode was used. The parameters of electrospray ionization positive mode were set as follows: capillary voltage was 2.5 kV, source offset was 30 V, source temperature was 150\u0026deg;C, desolvation temperature was 450\u0026deg;C, collision gas flow was 0.15 mL/min, and the nebulizer gas flow was 7 bar. The optimal MRM transitions, retention times, cone voltages, and collision energies for IgG, IgA, and IgM peptides and glycopeptides can be found in our previously published study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical and data analysis\u003c/h2\u003e \u003cp\u003eBasic statistical analysis and graphing were performed using GraphPad Prism 8 for Windows version 8.0.2 (GraphPad Software, La Jolla, California, USA). Comparisons between each combination of ATB, LTBI, and controls were performed using the nonparametric unpaired Mann-Whitney test. Sparse partial least squares discriminant analysis (sPLS-DA) and multivariate receiver operating characteristic (ROC) curve analysis were performed using MetaboAnalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The normalized responses of immunoglobulin glycopeptides (the responses of glycopeptides divided by the responses of the corresponding antibody subclasses) were used in these two analyses. The ROC curves were generated using the random forest algorithm as the classification method and built-in random forest as the feature ranking method.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distinct IgG glycosylation of ATB, LTBI, and control groups\u003c/h2\u003e \u003cp\u003ePlasma levels of total IgG and IgG subclasses were not very different among the controls, patients with LTBI, and patients with ATB \u003cb\u003e(Supplementary Fig.\u0026nbsp;1A)\u003c/b\u003e. The only IgG subclass that showed a marginally significant difference was IgG2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051), with higher levels observed in the control group than in the LTBI group. When we focused on the 20 IgG glycosylation traits, we observed similar glycosylation profiles in the control and LTBI groups, whereas the ATB group showed distinct glycosylation patterns. On the score plot of sPLS-DA, sample clusters of the control and LTBI groups overlapped, and the ATB sample cluster was slightly shifted to the right side of the plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) shows that the ATB group had higher fucosylation, but lower galactosylation of IgG1 and IgG2. In contrast, the control and LTBI groups showed increased galactosylation and sialylation, especially for IgG1 and IgG2 subclasses. These results were supported and validated by statistical analysis; the boxplots are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC \u003cb\u003eand Supplementary Fig.\u0026nbsp;1\u003c/b\u003e. Regarding the monogalactosylation and digalactosylation of IgG subclasses, we also observed a decreasing trend in the ATB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The fucosylation levels were significantly higher in the ATB group than in IgG1, but not IgG2 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). IgG1 sialylation was not significantly different among the three groups; however, IgG2 sialylation was higher in the control group than in the LTBI group. IgG3/4 sialylation levels were also higher in the control group than in the ATB group (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C\u003c/b\u003e). Bisection was only found to be lower in the ATB group with IgG3/4 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1D\u003c/b\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, three groups of glycosylation traits were identified based on their correlations: (1st ) Fucosylation and Agalactosylation; (2nd ) Bisection; (3rd ) Galactosylation and Sialylation. The 1st group of glycosylation traits was higher in the ATB group, which was the opposite of the 3rd group of glycosylation traits. The latter was higher in the control and LTBI groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough it is more complicated to investigate the biological functions of individual glycopeptides, we summarized 11 out of 26 IgG glycopeptides that were statistically different among the three groups (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Similar to our investigation of glycosylation traits, glycopeptides carrying more galactose, such as IgG1 H5N4, IgG1 H5N5F1, and IgG2 H5N4F1 were upregulated in the control group. Glycopeptides carrying fucose but less galactose, such as IgG1 H3N4F1 and IgG2 H3N4F1, were higher in the ATB group. The response of the glycopeptides was normalized to the response of their respective IgG subclasses; therefore, the comparison was not affected by the protein level in each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Significant differences in the level of IgA subclasses and glycosylation profiles\u003c/h2\u003e \u003cp\u003eNeither IgA1 nor IgA2 levels were significantly different between the control, LTBI, and ATB groups \u003cb\u003e(Supplementary Fig.\u0026nbsp;3A)\u003c/b\u003e. Regarding the IgA glycosylation traits, the glycosylation patterns were similar between the LTBI and control groups, whereas the ATB group had relatively different glycosylation profiles, which resulted in the sample cluster shifting to the right side of the sPLS-DA score plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, distinct galactosylation-related profiles were found only in the IgA1/2-N144/131 position, rather than in IgA2-N205 or IgA1/2-N340/327 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cb\u003eSupplementary Fig.\u0026nbsp;3C\u003c/b\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, on IgA1/2-N144/131, patients with ATB showed lower total galactosylation, which resulted from a combination of lower digalactosylation and higher monogalactosylation. We assumed that the galactosylation profile at this site shifted from digalactosylation to mono- or nongalactosylation (agalactosylation) in the ATB group. The lower galactosylation trend in the ATB group was similar to that observed for the IgG isotype. When we investigated the correlations among different glycosylation traits, the sialylation and digalactosylation of N144/131 had higher positive correlation coefficients with galactosylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), which were clustered together in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE. In contrast, the monogalactosylation and agalactosylation of N144/131 were negatively correlated with galactosylation and clustered independently in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE. Other glycosylation traits of IgA, such as fucosylation, sialylation, and bisection, were similar among the three groups (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B-3D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe have also reported distinct glycopeptides for the IgA isotype. Six IgA glycopeptides with glycosylation site, IgA1/2-N144/131, were found to be more abundant in ATB than in LTBI \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e: H3N5, H4N4S1, H4N5, H4N5S1, H5N2, and H5N3S1. Compared to controls, patients with ATB showed higher IgA1/2-N144/131 H3N5, H4N4S1, and H4N5S1, and lower IgA1/2-N144/131 H5N5S1 and IgA2-N205 H5N5F1 levels. To discriminate between LTBI and controls, significantly decreased IgA1/2-N144/131 H4N5S1 and H5N5S1 levels were found in LTBI groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Lower galactosylation of IgM was identified in the ATB group\u003c/h2\u003e \u003cp\u003eNo significant differences were observed in plasma levels of IgM among the control, LTBI, and ATB groups \u003cb\u003e(data not shown)\u003c/b\u003e. However, when 29 IgM glycosylation traits were input into the sPLS-DA analysis, a significant cluster shift was observed in the ATB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cb\u003ered cluster\u003c/b\u003e). In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, we constructed a heatmap of the top 10 features that showed significant differences among the three groups. Galactosylation of IgM-N171, N332, and N395 was lower, whereas N71 fucosylation and sialylation were higher in the ATB group. These findings were supported by the results of the statistical analysis, and the box plots are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH. Furthermore, we observed lower monogalactosylation, higher mannosylation, and lower hybrid-type glycosylation on IgM N402 in the ATB group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. For N563, only agalactosylation was significantly higher in ATB, which also represents lower galactosylation at this site (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. Looking into the details, we observed special patterns of monogalactosylation and digalactosylation for N171, N332, and N395 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC to \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Increased monogalactosylation was accompanied by decreased digalactosylation in the ATB group. This trend highlighted the shift from digalactosylation to monogalactosylation or agalactosylation, which resulted in reduced galactosylation in the ATB group. The inversely proportional relationships between (di)galactosylation and monogalactosylation at the three N-glycosylation sites can also be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeven IgM glycopeptides showed the potential ability to discriminate ATB from LTBI, as they increased significantly in ATB \u003cb\u003e(Supplementary Fig.\u0026nbsp;4)\u003c/b\u003e: N171 H4N3F1S1, N171 H5N3F1S1, N171 H6N3F1S1, N402 H9N2, N563 H3N5F1, N71 H5N4S2, and N71 H5N4F1S2. Among them, N171 H4N3F1S1, N402 H9N2, and N71 H5N4F1S2 also significantly increased in patients with ATB when compared with controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Improved differential ability for ATB and LTBI beyond IgG glycosylation\u003c/h2\u003e \u003cp\u003eAfter comparing the differences in Ig glycosylation profiles among the control, LTBI, and ATB groups, we evaluated whether incorporating IgA and IgM glycosylation traits into IgG glycosylation could improve the differential ability for ATB and LTBI. We first imported all Ig glycosylation traits into the Statistical Analysis tool in MetaboAnalyst 6.0, and filtered out 18 glycosylation traits that showed statistical differences between the ATB and LTBI groups. Among the 18 glycosylation traits, 7 belonged to IgG, 4 belonged to IgA, and 6 belonged to IgM. Since most of the literature has focused on the differences in IgG glycosylation traits, we used seven significantly different IgG traits to generate ROC curves. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the AUC ranged from 0.646 to 696, and the use of two glycosylation traits provided the best AUC (red curve). In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, we highlight the samples that were incorrectly classified using two IgG glycosylation traits (IgG1-Agalactosylation and IgG1-Galactosylation). Among the 20 ATB samples, 7 samples were incorrectly classified; among the 18 LTBI samples, 7 samples were incorrectly classified. We further added IgA and IgM glycosylation traits to generate ROC curves; the AUCs ranged from 0.714 to 0.808, and 18 variables provided the best performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). While using the 18 variables to predict the classification, 8 out of 20 ATB samples were incorrectly classified, and 3 out of 18 LTBI samples were incorrectly classified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, we need to highlight that the incorrectly classified samples were not overlapped in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD. When we evaluated the univariate of the glycosylation traits in GraphPad Prism software, all 18 glycosylation traits showed AUCs higher than 0.736, among which IgG1-Fucosylation provided the highest AUC of 0.799 (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe addition of N-glycans, such as galactosylation, sialylation, fucosylation, and bisection, to the Fc domain of antibodies is an important post-translational modification that can greatly affect immune functions. For TB infection, specifically, Lu \u003cem\u003eet al.\u003c/em\u003e have demonstrated that the \u003cem\u003ein vitro\u003c/em\u003e protective functions of IgG, including ADCP and ADCC, were associated with distinct glycosylation profiles in the IgG Fc region [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIgG galactosylation structures can change quickly in one\u0026rsquo;s inflammatory status [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Galactose-lacking IgG glycoforms possess proinflammatory activity by binding to mannose-binding lectin (MBL) and subsequently activating complement via alternative and lectin pathways [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Galactosylation levels are decreased in various infectious diseases, including TB, which may provide another means for TB diagnosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Liu \u003cem\u003eet al\u003c/em\u003e. demonstrated the diagnostic value of a high IgG G0/(G1\u0026thinsp;+\u0026thinsp;G2\u0026times;2) ratio for ATB infections [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, IgG galactosylation levels varied significantly between patients with LTBI and those with ATB. Lu \u003cem\u003eet al.\u003c/em\u003e reported significantly more digalactosylated IgG glycoforms in individuals with LTBI than in those with ATB [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These may reflect that controlled LTBI does not induce as much inflammatory activity as ATB [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consistent with previous studies, our study revealed a significant decrease in IgG galactosylation levels in the ATB group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, indicating a pro-inflammatory status in patients with active infection. Furthermore, we found that galactosylation changes were not limited to IgG but were also observed in IgA and IgM glycans. These novel findings suggest that IgA and IgM may also contribute to inflammation through glycosylation and have regulatory effects similar to those of IgG. Notably, we found distinct galactosylation patterns for IgA1/2-N144/131, IgM-N171, IgM-N332, and IgM-N395 in ATB: increased monogalactosylation and decreased digalactosylation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC to \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. This may reflect a shift from digalactosylation to monogalactosylation or agalactosylation, resulting in an overall decrease in the galactosylation levels.\u003c/p\u003e \u003cp\u003eThe sialylation of IgG is responsible for its anti-inflammatory activity. Two known mechanisms mediate the anti-inflammatory effects: 1) activation of the inhibitory FcγRIIB via dendritic cell-specific intercellular adhesion molecule grabbing non-integrin (DC-SIGN) and 2) decreased affinity for activating FcγRIIIA on NK cells, resulting in reduced ADCC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Decreased IgG sialylation has been observed in several pro-inflammatory diseases, such as rheumatoid arthritis, HIV infection, and hepatitis B [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Regarding TB infection, significantly decreased IgG sialylation has been reported in patients with ATB compared to LTBI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This reduced sialylation, together with the above-mentioned galactosylation profiles, may indicate an active inflammatory response in ATB infection compared to the controlled LTBI state [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although we did not observe a significant decrease in IgG1 sialylation in the ATB group, we observed a decrease not only in IgG3/4 of ATB individuals but also in IgG2 of LTBI individuals when compared to controls \u003cb\u003e(Supplementary Fig.\u0026nbsp;1C)\u003c/b\u003e. The activated immune response indicated by the decrease in IgG2 sialylation might be associated with a better defense mechanism against bacteria in patients with LTBI, as IgG2, a poor complement activator, is responsible for the bacterial capsular polysaccharide antigen response [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCore fucose is present in over 90% of serum IgG. The lack of core fucose on IgG glycans results in a significant increase in ADCC due to up to 100-fold enhanced affinity for the activating FcγRIIIA and FcγRIIIB [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lu \u003cem\u003eet al.\u003c/em\u003e reported that IgG isolated from individuals with LTBI contained less fucose [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Meanwhile, they did find higher binding of IgG to FcγRIIIA along with enhanced PPD-specific ADCC in LTBI. These unique features may be responsible for the enhanced killing of intracellular \u003cem\u003eMtb\u003c/em\u003e by infected macrophages, indicating that distinct Fc glycosylation patterns in LTBI are associated with enhanced \u003cem\u003eMtb\u003c/em\u003e control [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast, Liu \u003cem\u003eet al.\u003c/em\u003e found that IgG afucosylated glycans did not differ significantly between patients with ATB and healthy donors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition to the relatively well-studied IgG glycoprofiles, Kumagai \u003cem\u003eet al.\u003c/em\u003e used a mouse infection model to characterize the changes in IgM glycosylation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. They observed a\u0026thinsp;\u0026gt;\u0026thinsp;5-fold increase in IgM core fucosylation after \u003cem\u003eMtb\u003c/em\u003e infection in BCG-na\u0026iuml;ve mice. Notably, BCG vaccination attenuated this increase. Consistent with the literature, our study found that patients with ATB had higher levels of IgG1 fucosylation than those with LTBI and controls \u003cb\u003e(Supplementary Fig.\u0026nbsp;1B)\u003c/b\u003e. However, we did not observe significant differences in IgA and IgM fucosylation at most N-glycosylation sites, except for IgM N71 \u003cb\u003e(Supplementary Fig.\u0026nbsp;3C, 3D and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e, which is inconsistent with a previous study in mice [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBisecting GlcNAc indirectly affects the antibody effector function by inhibiting the addition of fucose at the glycan synthesis level [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As a result, although to a lower degree, the presence of bisecting GlcNAc had similar effects as the lack of fucose. That is, a bisecting GlcNAc on IgG is associated with greater affinity for FcγRIII and consequently enhances ADCC activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Decreased levels of IgG-bisecting glycans in patients with ATB compared to healthy donors have been reported by Liu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Reasonably, in patients with ATB, IgG bisection and fucosylation change in opposite directions, thus having the same ADCC-modulating effect. However, Lu \u003cem\u003eet al.\u003c/em\u003e did not observe a significant difference in bisecting GlcNAc between patients with LTBI and ATB [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consistent with the findings of Liu \u003cem\u003eet al.\u003c/em\u003e, our study observed significantly lower levels of IgG3/4 bisecting glycans in ATB group than in LTBI and control groups \u003cb\u003e(Supplementary Fig.\u0026nbsp;1D)\u003c/b\u003e. Similar to fucosylation, the levels of bisecting GlcNAc in IgA and IgM appeared to have little association with TB infection \u003cb\u003e(Supplementary Fig.\u0026nbsp;3B to 3D; data not shown for IgM)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOur study has some limitations that should be considered. Firstly, the relatively small sample size may limit the generalizability of our findings to larger populations. Secondly, we analyzed bulk immunoglobulins instead of TB-specific immunoglobulins, which may have affected the representativeness of our results. Thirdly, several N-glycosylation sites were evaluated using a limited number of glycopeptides, which may have hindered the exact percentages of glycosylation traits.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study introduces a novel approach to investigate the site-specific glycosylation profiles of IgG, IgA, and IgM, aiming to distinguish between patients with LTBI and those with ATB. In terms of glycosylation changes in bulk IgG, our findings in the elderly population show trends consistent with those observed in younger individuals in previous studies. Furthermore, our study is the first to reveal significant alterations in IgA and IgM glycosylation profiles in TB patients. By combining the glycosylation analysis of IgG, IgA, and IgM, we achieved improved classification performance, highlighting the importance of incorporating multiple immunoglobulins, beyond IgG, in diagnostic assessments. Despite some limitations, our study represents a crucial step toward better understanding the roles of IgA and IgM glycosylation in TB infection.\u003c/p\u003e"},{"header":"Abbreviations","content":"Tuberculosis, TB; Mycobacterium tuberculosis, Mtb; Active TB, ATB; Latent TB infection, LTBI; Immunoglobulin, Ig; Liquid chromatography-mass spectrometry, LC-MS/MS; Receiver operating characteristic, ROC; Area under the ROC curve, AUC; Tuberculin skin tests, TST; Interferon-gamma, IFN-γ; IFN-γ release assays, IGRA; Fragment crystallizable region, Fc; Fragment antigen-binding region, Fab; Antibody-dependent cell-mediated cytotoxicity, ADCC; Antibody-dependent cellular phagocytosis, ADCP; Dithiothreitol, DTT; Acetonitrile, ACN; Iodoacetamide, IAA; Phosphate buffered saline, PBS; Internal standards, IS."},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYun-Jung Yang: Writing - Original Draft, Formal analysis Chih-Hsin Lee: Writing - Original Draft, Conceptualization, Resources Yung-Kun Chuang: Resources San-Yuan Wang: Resources Michael X. Chen: Writing - Review \u0026amp; Editing Hsih-Chang Shih: Writing - Original Draft I-Lin Tsai: Writing - Review \u0026amp; Editing, Conceptualization, Funding acquisition, Supervision\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the Ministry of Science and Technology, Taiwan (grant number 108-2320-B-038-060-MY3). The authors are grateful for the technical support provided by the TMU Core Facility.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChai Q, Zhang Y, Liu CH. Mycobacterium tuberculosis: An Adaptable Pathogen Associated With Multiple Human Diseases. Front Cell Infect Microbiol. 2018;8:158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Tuberculosis. [Internet] 2021; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/tuberculosis\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/tuberculosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SH. Tuberculosis Infection and Latent Tuberculosis. Tuberc Respir Dis (Seoul). 2016;79(4):201\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNathavitharana RR, et al. Diagnosing active tuberculosis in primary care. BMJ. 2021;374:n1590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetahun H, et al. 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J Am Soc Nephrol. 2021;32(10):2455\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold JN, et al. Human serum IgM glycosylation: identification of glycoforms that can bind to mannan-binding lectin. J Biol Chem. 2005;280(32):29080\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGudelj I, Lauc G, Pezer M. Immunoglobulin G glycosylation in aging and diseases. Cell Immunol. 2018;333:65\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubedi GP, Barb AW. The Structural Role of Antibody N-Glycosylation in Receptor Interactions. Structure. 2015;23(9):1573\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Mathieu M, Brezski RJ. IgG Fc engineering to modulate antibody effector functions. Protein Cell. 2018;9(1):63\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu LL, et al. A Functional Role for Antibodies in Tuberculosis. 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Immunology. 1998;93(4):595\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidarsson G, Dekkers G, Rispens T. IgG subclasses and allotypes: from structure to effector functions. Front Immunol. 2014;5:520.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"immunoglobulin, glycosylation, tuberculosis, active TB, latent TB, LC-MS","lastPublishedDoi":"10.21203/rs.3.rs-6381409/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6381409/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003cbr\u003e\nTuberculosis (TB), caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), remains a global health challenge, particularly among the elderly. Accurate differentiation between active TB (ATB) and latent TB infection (LTBI) is essential, yet current diagnostic tools often fall short. While immunoglobulin (Ig) G glycosylation has been investigated in TB differential diagnosis, the glycosylation profiles of IgA and IgM have not been systematically studied—especially in older adults, who are more susceptible to TB reactivation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nWe analyzed the glycosylation patterns of IgG, IgA, and IgM in 59 elderly participants, including 22 ATB patients, 17 LTBI individuals, and 20 healthy controls. Antibody glycosylation was profiled using liquid chromatography-tandem mass spectrometry (LC-MS/MS), with a focus on distinguishing features between ATB and LTBI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nThis study is the first to identify distinct glycosylation alterations in IgA and IgM among TB patients. Compared to LTBI and controls, ATB patients showed reduced galactosylation and increased fucosylation in IgG and IgM, indicative of an enhanced inflammatory state. Novel glycosylation changes in IgA were observed at N144/131. When combining glycosylation features across all three immunoglobulin isotypes, diagnostic performance in differentiating ATB from LTBI improved (AUC = 0.808), suggesting added value beyond IgG alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\nOur findings demonstrate that glycosylation changes in IgA and IgM accompany active TB and are not limited to IgG. These alterations reveal broader humoral immune modulation in TB. In elderly individuals, where clinical differentiation of TB status is especially challenging, IgA and IgM glycosylation may warrant greater attention in both research and diagnostic contexts.\u003c/p\u003e","manuscriptTitle":"Beyond IgG: Novel Insights into IgA and IgM Glycosylation in Tuberculosis and Their Role in Differentiating Infection Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 09:44:33","doi":"10.21203/rs.3.rs-6381409/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86f1a9a7-7acb-4a42-8d93-99d346f9abc1","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T09:44:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 09:44:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6381409","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6381409","identity":"rs-6381409","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}