Statistical analysis of antibody glycosylation

The glycosylation profiling was determined for each IgG subclass of the 30 plasma samples, as described in the previous section. The three most abundant glycoforms observed were G0F, G1F and G2F, such that subclass specific changes in galactosylation were analyzed as ratio of G0F relative to the sum G0F+G1F+G2F. These numbers, representing the percentages of G0F (% G0F) for each subclass of an individual, can be obtained by dividing either the ion counts or the relative abundance of G0F to the sum G0F+G1F+G2F of the corresponding values. Following these calculations, the determined

% G0F were represented for each of the 10 plasma sets, for IgG1 (Figure 2.60 A) and for IgG2,3 (Figure 2.60 B), respectively. The IgG4 subclass was not analyzed, because in several instances this subclass was barely observed in the LC-MS analyses and hence the ion abundances for the low abundant glycoforms were affected by a greater experimental error than the other subclasses. In Figure 2.60, the green, pink and blue graphs represent the % G0F in patients, twins/siblings, and unrelated controls, respectively. Statistical analysis of these results was performed in order to determine whether the three groups of

% G0F data – patients, siblings and controls – represent statistically different events.

A.

B.

Figure 2.60: Subclass specific representation of % G₀F for each individual in each of the 10 sample sets: (A) for IgG1, and (B) for IgG2,3. The value % G0F was calculated for each IgG subclass and for each individual as the ratio of the relative abundance of G0F to the sum (G0F+G1F+G2F). Colour code: green – patients, pink – siblings, and blue – unrelated controls.

The Mann-Whitney U test represents a non-parametric significance test for assessing whether two samples of observables come from the same distribution [274, 275]. The null hypothesis is that the two samples are drawn from a single population, and, therefore, their probability distributions are equal. The prerequisite is that the observables in the two samples are derived from continuous measurements so that the conclusion can be drawn, of any two sets of observations, which is greater. This two-sample test can be thought of as testing the null hypothesis that the probability of the observables from one population exceeding the observables from the second population is equal to 0.5. The test involves calculation of a statistic, termed U, whose distribution under the null hypothesis is known.

In the case of small sample sets, such as the present clinical study (n=10), the distribution is tabulated, while for larger samples a good approximation is the normal distribution. The U statistic is calculated by ranking the data contained in the two data sets to be compared, disregarding their raw values. In the case of the twin-sibling study, the % G0F data sets obtained for IgG1 and IgG2,3 subclasses were assessed two-by-two, i.e. patients vs.

siblings, patients vs. unrelated controls, and siblings vs. controls, such that three independent tests were performed. The determined U values were compared to the table of critical values for U, based on the sample size in each group. If U exceeds the critical value for U at a significance level of 0.05, then the null hypothesis can be rejected, i.e. the two sample sets are coming from distinct events. The U test is included in most modern statistical packages. For small sample sets, it can be easily calculated by hand. Statistical analysis of glycosylation in the three data sets was performed using the add-in feature for statistical analysis available in Microsoft Excel 2002.

The results of the U – ranking test for IgG1 for the three data sets, are shown in Figure 2.61, in which the vertical bars represent the arithmetic mean of % G0F for all 10 patients, siblings and controls. Glycosylation analysis of the IgG1 of the 10 myositis patients revealed that these individuals have a statistically significant higher level of the G0F glycoform, in comparison to healthy unrelated age matched controls (p=0.05), while no significant difference was determined between patients and siblings (p=0.29), or between siblings and controls (p=0.33).

Figure 2.61: Arithmetic mean of % G0F within IgG1 subclass for: unrelated controls (left), siblings (middle) and myositis patients (right). As a result of the Mann-Whitney U-test, the levels of galactosylation are statistically different between controls and patients (p=0.05), whereas no statistical difference exists between controls and siblings (p=0.33), and siblings and patients (p=0.29), respectively.

Similar results were obtained for the IgG2,3 subclasses, i.e. the levels of agalactosylation in myositis patients are significantly different from those of unrelated controls. These findings are similar to the decreased galactosylation reported for rheumatoid arthritis patients. The observation of increased levels of galactosylation in these autoimmune patients may reflect changes in intracellular processing pathways associated with disease state.

In conclusion, statistical analyses of glycosylation profiles of IgG subclasses in myositis patients, healthy siblings and unrelated controls reveal statistically significant increased levels of agalactosylation in the myositis patients compared to controls, whereas for the healthy twins/siblings intermediate levels of G0F were determined. We speculate that these findings indicate the existence of a genetic predisposition in the group of unaffected siblings towards the development of autoimmune conditions, which can be observed at the protein level in their total plasma IgG fraction. Furthermore, the phenotype observed in the patient group suggests that the increased agalactosylation is the result of environmental exposures on the genes. We hypothesize that the environment may alter the expression levels of ß-galactosyl-transferases and/or of ß-galactosydases. In order to confirm this hypothesis, future work will employ proteomic approaches for determination of differential expression of total plasma proteins in the three clinical groups.

2.4 Prospectives for mass spectrometry in the analysis of protein structures and