A new strategy using tandem (18)O stable isotope labeling (TOSIL) to quantify the N-glycosylation site occupancy is reported. Three heavy oxygen atoms are introduced into N-glycosylated peptides: two (18)O atoms are incorporated into the carboxyl terminal of all peptides during a tryptic digestion, and the third (18)O atom is incorporated into the N-glycosylation site of asparagines-linked sugar chains specifically via a N-glycosidase F (PNGase F)-mediated hydrolysis. Comparing samples treated in H(2)(18)O and samples treated in H(2)(16)O, a unique mass shift of 6 Da can be shown for N-glycosylated peptide with single glycosylation site, which could be easily distinguished from those nonglycosite peptide pairs with a mass difference of 4 Da only. The relative quantities of N-glycosylated and its parent protein-levels were obtained simultaneous by measuring the intensity ratios of (18)O/(16)O for glycosylated (6 Da) and for nonglycosylated (4 Da) peptides, respectively. Thus, a comparison of these two ratios can be utilized to evaluate the changes of occupancy of N-glycosylation at specific sites between healthy and diseased individuals. The TOSIL approach yielded good linearity in quantitative response within 10-fold dynamic range with the correlation coefficient r(2) > 0.99. The standard deviation (SD) ranged from 0.06 to 0.21, for four glycopeptides from two model glycoproteins. Furthermore, serums from a patient with ovarian cancer and healthy individual were used as test examples to validate the novel TOSIL method. A total of 86 N-glycosylation sites were quantified and N-glycosylation levels of 56 glycopeptides showed significant changes. Most changes in N-glycosylation at specific sites have the same trends as those of protein expression levels; however, the occupancies of three N-glycosylation sites were significantly changed with no change in proteins levels.

N-linked glycosylation is prevalent in proteins destined for extracellular environments; nearly all secreted proteins are glycosylated. However, with respect to their glycosylation sites, little attention has been paid. Here, we report the analysis of N-glycosylation sites on secreted proteins of human hepatocellular carcinoma cells. For the enrichment of glycopeptides, capture methods with hydrophilic affinity (HA) and hydrazide chemistry (HC) were used complementarily. With the use of both methods in combination with nano-LC-ESI-MS/MS analysis, 300 different glycosylation sites within 194 unique glycoproteins were identified, and 172 glycosites have not been determined experimentally previously. A direct comparison between HA and HC methods was also investigated for the first time. In brief, in terms of selectivity for glycopeptides, HC is superior to HA (92.9% vs 51.3%); however, based on the number of glycosites identified, HA outweighs HC (265 vs 159). Furthermore, unavoidable contaminants such as actin and bovine serum albumin which are not N-glycosylated could be easily depleted by using this glycoproteomic strategy. As a consequence, more low-abundance and genuinely secreted proteins were identified. Among the glycoproteins identified, alpha-fetoprotein, CD44 and laminin have been reported to be implicated in HCC and its metastasis.

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real ( N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture ( N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.