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Novel autoantigens immunogenic in COPD patients.

Leidinger P, Keller A, Heisel S, Ludwig N, Rheinheimer S, Klein V, Andres C, Hamacher J, Huwer H, Stephan B, Stehle I, Lenhof HP, Meese E - Respir. Res. (2009)

Bottom Line: Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies.By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity.The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Human Genetics, Medical School, Saarland University, Building 60, 66421 Homburg/Saar, Germany. p.leidinger@mx.uni-saarland.de

ABSTRACT

Background: Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies. In this study we analyze the complexity of the autoantibody response and reveal the nature of the antigens that are recognized by autoantibodies in COPD patients.

Methods: An array of 1827 gridded immunogenic peptide clones was established and screened with 17 sera of COPD patients and 60 healthy controls. Protein arrays were evaluated both by visual inspection and a recently developed computer aided image analysis technique. By this computer aided image analysis technique we computed the intensity values for each peptide clone and each serum and calculated the area under the receiver operator characteristics curve (AUC) for each clone and the separation COPD sera versus control sera.

Results: By visual evaluation we detected 381 peptide clones that reacted with autoantibodies of COPD patients including 17 clones that reacted with more than 60% of the COPD sera and seven clones that reacted with more than 90% of the COPD sera. The comparison of COPD sera and controls by the automated image analysis system identified 212 peptide clones with informative AUC values. By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity.

Conclusion: The identification of a rather complex humoral immune response in COPD patients supports the idea of COPD as a disease with strong autoimmune features. The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.

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Related in: MedlinePlus

The separation of intensity values of COPD and control sera is exemplarily shown for an arbitrary antigen A. A: Intensity values of each single COPD (1) and control (0) serum for antigen A are exemplarily shown. The position of the threshold (vertical green bar (2)) determines the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). The vertical bars 1 and 3 indicate the minimal and maximal thresholds. B: The two curves represent density estimations of intensity values of COPD patients (red curve) and controls (black curve) for antigen A corresponding to Figure 1A. C: The specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) of a test are visualized by the receiver operator characteristics (ROC) curve. The performance of the test can be represented by the area under the ROC curve (AUC). Here, the threshold is represented by the green circle. The values for sensitivity and specificity can be modified by moving the threshold.
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Figure 1: The separation of intensity values of COPD and control sera is exemplarily shown for an arbitrary antigen A. A: Intensity values of each single COPD (1) and control (0) serum for antigen A are exemplarily shown. The position of the threshold (vertical green bar (2)) determines the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). The vertical bars 1 and 3 indicate the minimal and maximal thresholds. B: The two curves represent density estimations of intensity values of COPD patients (red curve) and controls (black curve) for antigen A corresponding to Figure 1A. C: The specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) of a test are visualized by the receiver operator characteristics (ROC) curve. The performance of the test can be represented by the area under the ROC curve (AUC). Here, the threshold is represented by the green circle. The values for sensitivity and specificity can be modified by moving the threshold.

Mentions: Since manual inspection offers only a subjective and binary analysis of reacting clones, we developed an automated image analysis procedure. After hybridizing the arrays with the different sera, our approach computes the intensity value for each clone on the arrays. Since all clones were spotted in duplicates, the mean value of the two replicates was assigned to each clone. The evaluated antibody profiles were normalized using quantile normalization to minimize between-array-effects. To access the "value" of an antigen with respect to its ability to separate COPD sera from control sera, we calculated the area under the Receiver Operator Characteristics curve (AUC) for each antigen A as follows: the normalized intensities of all control and COPD sera were used as threshold values. For all thresholds t, we considered COPD sera with intensity value above t as true positives (TP), COPD sera with intensity value below t as false negatives (FN), control sera with intensity value below t as true negatives (TN), and control sera with intensity value above t as false positives (FP). Likewise for all thresholds, specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) were computed. Please note that in some cases the classification has to be inverted. In these cases, diseased sera with intensity value below t are considered as 'true positives' (TP). The Receiver Operator Characteristics (ROC) curve shows the specificity as function of one minus the sensitivity. AUC values can range from 0 to 1. An AUC of 0.5 for a spot means that the distribution of intensity values of COPD sera and control sera can not be distinguished. The more the AUC value of an antigen differs from 0.5, the better this antigen is suited to separate between the two serum groups COPD and control. AUCs of 1 or 0 correspond to a perfect separation of spots generated by COPD and control sera. Antigens with AUC values > 0.5 show higher intensity values in COPD sera than in control sera. Antigens with AUC values < 0.5 show higher intensity values in control sera than in COPD sera. A graphical representation of the AUC value computation is provided in Figure 1.


Novel autoantigens immunogenic in COPD patients.

Leidinger P, Keller A, Heisel S, Ludwig N, Rheinheimer S, Klein V, Andres C, Hamacher J, Huwer H, Stephan B, Stehle I, Lenhof HP, Meese E - Respir. Res. (2009)

The separation of intensity values of COPD and control sera is exemplarily shown for an arbitrary antigen A. A: Intensity values of each single COPD (1) and control (0) serum for antigen A are exemplarily shown. The position of the threshold (vertical green bar (2)) determines the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). The vertical bars 1 and 3 indicate the minimal and maximal thresholds. B: The two curves represent density estimations of intensity values of COPD patients (red curve) and controls (black curve) for antigen A corresponding to Figure 1A. C: The specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) of a test are visualized by the receiver operator characteristics (ROC) curve. The performance of the test can be represented by the area under the ROC curve (AUC). Here, the threshold is represented by the green circle. The values for sensitivity and specificity can be modified by moving the threshold.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667165&req=5

Figure 1: The separation of intensity values of COPD and control sera is exemplarily shown for an arbitrary antigen A. A: Intensity values of each single COPD (1) and control (0) serum for antigen A are exemplarily shown. The position of the threshold (vertical green bar (2)) determines the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). The vertical bars 1 and 3 indicate the minimal and maximal thresholds. B: The two curves represent density estimations of intensity values of COPD patients (red curve) and controls (black curve) for antigen A corresponding to Figure 1A. C: The specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) of a test are visualized by the receiver operator characteristics (ROC) curve. The performance of the test can be represented by the area under the ROC curve (AUC). Here, the threshold is represented by the green circle. The values for sensitivity and specificity can be modified by moving the threshold.
Mentions: Since manual inspection offers only a subjective and binary analysis of reacting clones, we developed an automated image analysis procedure. After hybridizing the arrays with the different sera, our approach computes the intensity value for each clone on the arrays. Since all clones were spotted in duplicates, the mean value of the two replicates was assigned to each clone. The evaluated antibody profiles were normalized using quantile normalization to minimize between-array-effects. To access the "value" of an antigen with respect to its ability to separate COPD sera from control sera, we calculated the area under the Receiver Operator Characteristics curve (AUC) for each antigen A as follows: the normalized intensities of all control and COPD sera were used as threshold values. For all thresholds t, we considered COPD sera with intensity value above t as true positives (TP), COPD sera with intensity value below t as false negatives (FN), control sera with intensity value below t as true negatives (TN), and control sera with intensity value above t as false positives (FP). Likewise for all thresholds, specificity (TN/(TN+FP)) and sensitivity (TP/(TP+FN)) were computed. Please note that in some cases the classification has to be inverted. In these cases, diseased sera with intensity value below t are considered as 'true positives' (TP). The Receiver Operator Characteristics (ROC) curve shows the specificity as function of one minus the sensitivity. AUC values can range from 0 to 1. An AUC of 0.5 for a spot means that the distribution of intensity values of COPD sera and control sera can not be distinguished. The more the AUC value of an antigen differs from 0.5, the better this antigen is suited to separate between the two serum groups COPD and control. AUCs of 1 or 0 correspond to a perfect separation of spots generated by COPD and control sera. Antigens with AUC values > 0.5 show higher intensity values in COPD sera than in control sera. Antigens with AUC values < 0.5 show higher intensity values in control sera than in COPD sera. A graphical representation of the AUC value computation is provided in Figure 1.

Bottom Line: Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies.By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity.The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Human Genetics, Medical School, Saarland University, Building 60, 66421 Homburg/Saar, Germany. p.leidinger@mx.uni-saarland.de

ABSTRACT

Background: Chronic obstructive pulmonary disease (COPD) is a respiratory inflammatory condition with autoimmune features including IgG autoantibodies. In this study we analyze the complexity of the autoantibody response and reveal the nature of the antigens that are recognized by autoantibodies in COPD patients.

Methods: An array of 1827 gridded immunogenic peptide clones was established and screened with 17 sera of COPD patients and 60 healthy controls. Protein arrays were evaluated both by visual inspection and a recently developed computer aided image analysis technique. By this computer aided image analysis technique we computed the intensity values for each peptide clone and each serum and calculated the area under the receiver operator characteristics curve (AUC) for each clone and the separation COPD sera versus control sera.

Results: By visual evaluation we detected 381 peptide clones that reacted with autoantibodies of COPD patients including 17 clones that reacted with more than 60% of the COPD sera and seven clones that reacted with more than 90% of the COPD sera. The comparison of COPD sera and controls by the automated image analysis system identified 212 peptide clones with informative AUC values. By in silico sequence analysis we found an enrichment of sequence motives previously associated with immunogenicity.

Conclusion: The identification of a rather complex humoral immune response in COPD patients supports the idea of COPD as a disease with strong autoimmune features. The identification of novel immunogenic antigens is a first step towards a better understanding of the autoimmune component of COPD.

Show MeSH
Related in: MedlinePlus