Predictive analysis of changes in protease activity associated with peptides differentially regulated in RA ¹.

1<p>Frequency distribution analysis based on all peptides (n = 131).</p><p>Mathematical calculations are based on the following parameter and calculations:</p><p><b>occ(up)</b> = Sum of all occurrences for each individual protease in the up-regulated peptides,</p><p><b>occ(down)</b> = Sum of all occurrences for each individual protease in the down-regulated peptides,</p><p><b>N(up)</b> = Total number of peptides being up-regulated,</p><p><b>N(down)</b> = Total number of peptides being down-regulated,</p><p><b>% frequency(up)</b> = (occ(up)/N(up)) * 100.</p><p><b>% frequency(down)</b> = (occ(down)/N(down)) * 100.</p><p><b>% frequency difference ratio</b> = | ((freq%(up) − freq%(down))/(freq%(up)+freq%(down)) * 100 |.</p><p><b>Frequency scores</b> = %freq * (occ(up)-occ(down)).</p

Work flow used for the determination of urinary biomarkers associated with RA.

<p>Work flow used for the determination of urinary biomarkers associated with RA.</p

Baseline characteristics of the training and test set populations (case and control)¹.

<p>nd, not determined; ACPA, anti-citrullinated protein antibodies; RF, rheumatoid factor; DAS28, 28 joint count disease activity score; HAQ, health assessment questionnaire score; CRP, C-reactive protein.</p>1<p>Differences between training set and test set within both RA and control groups were not statistically significant (Mann-Whitney for continuous values and Chi Square for categorical values; p<0.05) with the exception for the proportion of female in the control group between the training and test sets (p<0.05).</p>2<p>Difference in the median age value between groups is statistically significant between RA and control groups of the training set (p = 0.0023) and between RA and control groups of the test set (p = 0.0059).</p>3<p>Difference in the gender distribution between groups is statistically significant between RA and control groups of the training set (p<0.01) but not between RA and control groups of the test set (p>0.05) (Chi Square test).</p>4<p>Data missing for 18 patients in the training set and 9 patients in test set, percentage refers to proportion of patients tested.</p

Urinary polypeptide signatures in cases and controls from the validation set based on 39 significantly different sequenced peptides.

<p>Normalized molecular weight (500–15000 Da) in logarithmic scale is plotted against normalized migration time (18–45 minutes). The mean signal intensity of the polypeptide peak is given in 3-dimensional depiction (n = 15 controls and 16 cases).</p

Graphical representation of the frequency distribution of proteases with modified activity associated with RA.

<p>Percentage frequency of peptide occurrences in the down-regulation group is plotted on the x-axis, whereas the percentage frequency of occurrences in the up-regulated group is plotted on the y-axis. Circled data points represent the proteases which activity is the most affected in RA compared to that of healthy controls (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104625#pone-0104625-t004" target="_blank">Table 4</a>).</p

Correlation of urinary peptides with kidney function.

<p>a) Heatmap of the signal of the 88 peptides of the CKD273 model showing monotone variations. Each line represents a single peptide, and the four columns correspond to the four groups of patients separated according to their eGFR value (90–61, 60–31, 30–16 and 15–0 mL/min/1.73 m²). The mean signal per peptide and per group is represented following the color key included in the figure. <b>b)</b> and <b>c)</b>: the median scoring and interquartile range for 2 of the 88 peptides is presented in logarithmic scale. The upper panel shows the distribution of LLSPYSYSTTAVVTNPKE, a peptide derived from Transthyretin (AA 130–147). The lower panel depicts the distribution of SGSVIDQSRVL, a peptide derived from Uromodulin (AA 589–599). The abundance of these peptides changes significantly with decreasing MDRD-estimated GFR.</p

Cluster analysis.

<p>Clustering of the 53 patients was performed according to their CKD273 pattern using an average linkage clustering and standard Euclidean distances to assign the patients to clusters.</p