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Development of a T7 Phage Display Library to Detect Sarcoidosis and Tuberculosis by a Panel of Novel Antigens.

Talwar H, Rosati R, Li J, Kissner D, Ghosh S, -Madrid FF, Samavati L - EBioMedicine (2015)

Bottom Line: Using a high throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients.Additionally, interrogating the same microarray platform with sera from subjects with TB, we identified 50 clones that distinguish between TB, sarcoidosis and healthy controls.These novel biomarkers can improve diagnosis of sarcoidosis and TB, and may aid to develop or evaluate a TB vaccine.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Wayne State University School of Medicine and Detroit Medical Center, Detroit, MI 48201.

ABSTRACT

Sarcoidosis is a granulomatous inflammatory disease, diagnosed through tissue biopsy of involved organs in the absence of other causes such as tuberculosis (TB). No specific serologic test is available to diagnose and differentiate sarcoidosis from TB. Using a high throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This complex cDNA library was biopanned to obtain 1152 potential sarcoidosis antigens and a microarray was constructed to immunoscreen two different sets of sera from healthy controls and sarcoidosis. Meta-analysis identified 259 discriminating sarcoidosis antigens, and multivariate analysis identified 32 antigens with a sensitivity of 89% and a specificity of 83% to classify sarcoidosis from healthy controls. Additionally, interrogating the same microarray platform with sera from subjects with TB, we identified 50 clones that distinguish between TB, sarcoidosis and healthy controls. The top 10 sarcoidosis and TB specific clones were sequenced and homologies were searched in the public database revealing unique epitopes and mimotopes in each group. Here, we show for the first time that immunoscreenings of a library derived from sarcoidosis tissue differentiates between sarcoidosis and tuberculosis antigens. These novel biomarkers can improve diagnosis of sarcoidosis and TB, and may aid to develop or evaluate a TB vaccine.

No MeSH data available.


Related in: MedlinePlus

A) Heatmap generated by applying meta-analysis using microarray analysis of 2 separate datasets derived from 115 sarcoidosis patients and 64 healthy controls. Data reflecting 259 antigens expressed significantly differently between healthy controls and sarcoidosis subjects in immunoscreening using sera. The 259 antigens are further divided into three categories according to the AW-OC method. I: 78 antigens are consistently over- or under-expressed in sarcoidosis in both datasets; II: 115 antigens are over- or under-expressed in sarcoidosis in the second dataset only; III: 66 antigens are over- or under-expressed in sarcoidosis in the first dataset only. B) Receiver operating characteristics (ROC) curve demonstrating the performance of 32 classifiers to discriminate between healthy controls and sarcoidosis subjects.
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f0010: A) Heatmap generated by applying meta-analysis using microarray analysis of 2 separate datasets derived from 115 sarcoidosis patients and 64 healthy controls. Data reflecting 259 antigens expressed significantly differently between healthy controls and sarcoidosis subjects in immunoscreening using sera. The 259 antigens are further divided into three categories according to the AW-OC method. I: 78 antigens are consistently over- or under-expressed in sarcoidosis in both datasets; II: 115 antigens are over- or under-expressed in sarcoidosis in the second dataset only; III: 66 antigens are over- or under-expressed in sarcoidosis in the first dataset only. B) Receiver operating characteristics (ROC) curve demonstrating the performance of 32 classifiers to discriminate between healthy controls and sarcoidosis subjects.

Mentions: A novel aspect of our work was the integration of data from two independent trials of printing allowing the development of two datasets obtained from two independent cohorts of sarcoidosis patients and healthy controls utilized for hybridization. To generate the first dataset, sera from 54 sarcoidosis subjects and 45 healthy controls were immune-screened against 1152 sarcoidosis specific peptides. In the second dataset, sera from 19 healthy controls and 61 sarcoidosis subjects were immune-screened using the same platform of clones. Sera used in both datasets for hybridization had not been previously used for biopanning or selection of clones. Table 2 shows the clinical characteristics of sarcoidosis and healthy control subjects. Within array loess normalization was performed for each spot and summarized by median of triplicates and followed by between array quantile normalization. After preprocessing, 1101 antigens common in both datasets were used for further analysis. Univariate and multivariate analyses were performed. Limma's empirical Bayes moderated t-test was used to identify fold-changes in expression of antigens that differed significantly between sarcoidosis and controls for each dataset separately. Then both datasets were combined using an integrative-analysis method, an adaptively-weighted method with one-sided correction (AW-OC) (Li and Tseng, 2011). Out of the 1101 potential antigens, 259 showed a strong differentiation between sarcoidosis and healthy control subjects with adjusted p value (q value) of < 0.05 and FDR (false discovery rate) of < 0.05. Fig. 2A shows the heatmap of the 259 significant antigens that were differentially expressed in both datasets. Seventy eight markers out of 259 were consistently over- or under-expressed in sarcoidosis subjects. Fig. 2B shows the AUROC for this classifier. KNN method performed slightly better than SVM. Using the highly significant 32 antigens selected by AW-OC and KNN methods to classify sarcoidosis and healthy controls (AW-OC + KNN), the area under the curve (AUROC) was 0.78, with a sensitivity of 89% and a specificity of 83% estimated after 10-fold cross-validation (Fig. 2B).


Development of a T7 Phage Display Library to Detect Sarcoidosis and Tuberculosis by a Panel of Novel Antigens.

Talwar H, Rosati R, Li J, Kissner D, Ghosh S, -Madrid FF, Samavati L - EBioMedicine (2015)

A) Heatmap generated by applying meta-analysis using microarray analysis of 2 separate datasets derived from 115 sarcoidosis patients and 64 healthy controls. Data reflecting 259 antigens expressed significantly differently between healthy controls and sarcoidosis subjects in immunoscreening using sera. The 259 antigens are further divided into three categories according to the AW-OC method. I: 78 antigens are consistently over- or under-expressed in sarcoidosis in both datasets; II: 115 antigens are over- or under-expressed in sarcoidosis in the second dataset only; III: 66 antigens are over- or under-expressed in sarcoidosis in the first dataset only. B) Receiver operating characteristics (ROC) curve demonstrating the performance of 32 classifiers to discriminate between healthy controls and sarcoidosis subjects.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4465182&req=5

f0010: A) Heatmap generated by applying meta-analysis using microarray analysis of 2 separate datasets derived from 115 sarcoidosis patients and 64 healthy controls. Data reflecting 259 antigens expressed significantly differently between healthy controls and sarcoidosis subjects in immunoscreening using sera. The 259 antigens are further divided into three categories according to the AW-OC method. I: 78 antigens are consistently over- or under-expressed in sarcoidosis in both datasets; II: 115 antigens are over- or under-expressed in sarcoidosis in the second dataset only; III: 66 antigens are over- or under-expressed in sarcoidosis in the first dataset only. B) Receiver operating characteristics (ROC) curve demonstrating the performance of 32 classifiers to discriminate between healthy controls and sarcoidosis subjects.
Mentions: A novel aspect of our work was the integration of data from two independent trials of printing allowing the development of two datasets obtained from two independent cohorts of sarcoidosis patients and healthy controls utilized for hybridization. To generate the first dataset, sera from 54 sarcoidosis subjects and 45 healthy controls were immune-screened against 1152 sarcoidosis specific peptides. In the second dataset, sera from 19 healthy controls and 61 sarcoidosis subjects were immune-screened using the same platform of clones. Sera used in both datasets for hybridization had not been previously used for biopanning or selection of clones. Table 2 shows the clinical characteristics of sarcoidosis and healthy control subjects. Within array loess normalization was performed for each spot and summarized by median of triplicates and followed by between array quantile normalization. After preprocessing, 1101 antigens common in both datasets were used for further analysis. Univariate and multivariate analyses were performed. Limma's empirical Bayes moderated t-test was used to identify fold-changes in expression of antigens that differed significantly between sarcoidosis and controls for each dataset separately. Then both datasets were combined using an integrative-analysis method, an adaptively-weighted method with one-sided correction (AW-OC) (Li and Tseng, 2011). Out of the 1101 potential antigens, 259 showed a strong differentiation between sarcoidosis and healthy control subjects with adjusted p value (q value) of < 0.05 and FDR (false discovery rate) of < 0.05. Fig. 2A shows the heatmap of the 259 significant antigens that were differentially expressed in both datasets. Seventy eight markers out of 259 were consistently over- or under-expressed in sarcoidosis subjects. Fig. 2B shows the AUROC for this classifier. KNN method performed slightly better than SVM. Using the highly significant 32 antigens selected by AW-OC and KNN methods to classify sarcoidosis and healthy controls (AW-OC + KNN), the area under the curve (AUROC) was 0.78, with a sensitivity of 89% and a specificity of 83% estimated after 10-fold cross-validation (Fig. 2B).

Bottom Line: Using a high throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients.Additionally, interrogating the same microarray platform with sera from subjects with TB, we identified 50 clones that distinguish between TB, sarcoidosis and healthy controls.These novel biomarkers can improve diagnosis of sarcoidosis and TB, and may aid to develop or evaluate a TB vaccine.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Wayne State University School of Medicine and Detroit Medical Center, Detroit, MI 48201.

ABSTRACT

Sarcoidosis is a granulomatous inflammatory disease, diagnosed through tissue biopsy of involved organs in the absence of other causes such as tuberculosis (TB). No specific serologic test is available to diagnose and differentiate sarcoidosis from TB. Using a high throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This complex cDNA library was biopanned to obtain 1152 potential sarcoidosis antigens and a microarray was constructed to immunoscreen two different sets of sera from healthy controls and sarcoidosis. Meta-analysis identified 259 discriminating sarcoidosis antigens, and multivariate analysis identified 32 antigens with a sensitivity of 89% and a specificity of 83% to classify sarcoidosis from healthy controls. Additionally, interrogating the same microarray platform with sera from subjects with TB, we identified 50 clones that distinguish between TB, sarcoidosis and healthy controls. The top 10 sarcoidosis and TB specific clones were sequenced and homologies were searched in the public database revealing unique epitopes and mimotopes in each group. Here, we show for the first time that immunoscreenings of a library derived from sarcoidosis tissue differentiates between sarcoidosis and tuberculosis antigens. These novel biomarkers can improve diagnosis of sarcoidosis and TB, and may aid to develop or evaluate a TB vaccine.

No MeSH data available.


Related in: MedlinePlus