Influenza virus typically alters protein glycosylation in order to escape immune pressure from hosts and hence to facilitate survival in different host environments. In this study, the patterns and conservation of glycosylation sites on HA and NA of influenza A/H1N1 viruses isolated from various hosts at different time periods were systematically analyzed, by employing a new strategy combining genome-based glycosylation site prediction and 3D modeling of glycoprotein structures, for elucidation of the modes and laws of glycosylation site alteration in the evolution of influenza A/H1N1 viruses. The results showed that influenza H1N1 viruses underwent different alterations of protein glycosylation in different hosts. Two alternative modes of glycosylation site alteration were involved in the evolution of human influenza virus: One was an increase in glycosylation site numbers, which mainly occurred with high frequency in the early stages of evolution. The other was a change in the positional conversion of the glycosylation sites, which was the dominating mode with relatively low frequency in the later evolutionary stages. The mechanisms and possibly biological functions of glycosylation site alteration for the evolution of influenza A/H1N1 viruses were also discussed. Importantly, the significant role of positional alteration of glycosylation sites in the host adaptation of influenza virus was elucidated. Although the results still need to be supported by experimental data, the information here may provide some constructive suggestions for research into the glycosylation of influenza viruses as well as even the design of surveillance and the production of viral vaccines.

Immunoglobulin A (IgA) is the most abundant immunoglobulin synthesized in the human body. It has the highest concentration in the mucosa and is second only to IgG in serum. IgA plays an important role in mucosal immunity, and is the predominant antibody used to protect the mucosal surface from pathogens invasion and to maintain the homeostasis of intestinal flora. Moreover, The binding IgA to the FcαRI (Fc alpha Receptor I) in soluble or aggregated form can mediate anti- or pro- inflammatory responses, respectively. IgA is also known as one of the most heavily glycosylated antibodies among human immunoglobulins. The glycosylation of IgA has been shown to have a significant effect on its immune function. Variation in the glycoform of IgA is often the main characteration of autoimmune diseases such as IgA nephropathy (IgAN), IgA vasculitis (IgAV), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA). However, compared with the confirmed glycosylation function of IgG, the pathogenic mechanism of IgA glycosylation involved in related diseases is still unclear. This paper mainly summarizes the recent reports on IgA’s glycan structure, its function, its relationship with the occurrence and development of diseases, and the potential application of glycoengineered IgA in clinical antibody therapeutics, in order to provide a potential reference for future research in this field.

Lung cancer is the most common malignancy worldwide. Thus, there is a critical need for diagnostic biomarkers with adequate sensitivity and specificity for lung cancer detection. Glycans in glycoproteins are significantly altered in cancer, and may serve as a tool for identifying potential diagnostic biomarkers. Recent studies have reported changes in α-1-antitrypsin (A1AT) glycosylation in lung cancer serum, tissue and cell lines. In this study, a lectin microarray was used to detect glycosylation changes in serum A1AT from patients with lung adenocarcinoma (ADC), squamous cell lung cancer, small-cell lung cancer (SCLC) and benign pulmonary diseases. Differentially expressed glycosylated patterns of A1AT were identified by lectin arrays and were confirmed by lectin-based enzyme-linked immunosorbent assay (ELISA). We found that galactosylated A1AT could distinguish non-small-cell lung cancer (NSCLC) from benign pulmonary diseases (AUC = 0.834); fucosylated A1AT showed exceptional capability in distinguishing ADC from benign diseases (AUC = 0.919) or other lung cancer subtypes (AUC = 0.844), and A1AT containing poly-LacNAc could detect SCLC from benign diseases (AUC = 0.905) or NSCLC (AUC = 0.707). The present study indicates that glycosylated patterns of A1AT may serve as potential biomarkers for detection of lung cancer. Further studies in larger sample sizes are necessary to validate the clinical utility of these markers.

We systematically investigated and assessed the alterations of salivary glycopatterns and possibility as biomarkers for diagnosis of early-stage breast cancer. Alterations of salivary glycopatterns were probed using lectin microarrays and blotting analysis from 337 patients with breast benign cyst or tumor (BB) or breast cancer (I/II stage) and 110 healthy humans. Their diagnostic models were constructed by a logistic stepwise regression in the retrospective cohort. Then, the performance of the diagnostic models were assessed by ROC analysis in the validation cohort. Finally, a double-blind cohort was tested to confirm the application potential of the diagnostic models. The diagnostic models were constructed based on 9 candidate lectins (e.g., PHA-E+L, BS-I, and NPA) that exhibited significant alterations of salivary glycopatterns, which achieved better diagnostic powers with an AUC value >0.750 (p<0.001) for the diagnosis of BB (AUC: 0.752, sensitivity: 0.600, and specificity: 0.835) and I stage breast cancer (AUC: 0.755, sensitivity: 0.733, and specificity: 0.742) in the validation cohort. The diagnostic model of I stage breast cancer exhibited a high accuracy of 0.902 in double-blind cohort. This study could contribute to the screening for patients with early-stage breast cancer based on precise alterations of salivary glycopatterns.