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Science 340 (6131): 475-478

Copyright © 2013 by the American Association for the Advancement of Science

Direct Proteomic Quantification of the Secretome of Activated Immune Cells

Felix Meissner1, Richard A. Scheltema1, Hans-Joachim Mollenkopf2, and Matthias Mann1,*

1 Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
2 Microarray Core Facility, Max Planck Institute for Infection Biology, Berlin, Germany.


Figure 1
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Fig. 1. The TLR4 induced protein secretome. (A) Schematic illustration of the experimental setup, proteomics workflow, and data analysis. (B) Number of proteins with the indicated fold change in release upon LPS treatment. Centers of bins are depicted; bars represent the median of replicates with range. (C) PCA of significantly released proteins plotted as median. Numbers indicate time points after LPS stimulation. R2 of the second-order polynomial fits of the genotypes over time: WT = 0.99, MYD-ko = 0.83 and, TRIF-ko = 0.79. PCA vectors PC1 and PC3 contributed 41.7% and 5.3%, respectively, to the genotypic variance.

 

Figure 2
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Fig. 2. Relation of transcription to protein secretion. (A) Frequency distribution of correlations for induced protein secretion to transcriptome. Centers of the bins are indicated. (B and C) Proteins with correlated secretory and transcriptional regulations. (B) Secreted proteins accumulate over time; transcripts decline at late time points. (C) Secreted proteins and transcripts accumulate over time. (D) Proteins with anticorrelated secretory and transcriptional regulations. [(B) to (D)] (Left) Density estimation of correlation. (Middle) Median with interquartile range of secreted proteins. (Right) Median with interquartile range of transcriptional regulation. (E) Gene ontology enrichment for cellular component (GOCC slim) of the anticorrelated proteins using the Fisher’s exact test on the complete data set, plotted versus Benjamini Hochberg false discovery rate (B.H.FDR) corrected significance.

 

Figure 3
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Fig. 3. Effect of signaling adaptors on secretory signatures. (A) Adaptor dominance ranked as maximal difference between MyD88- and TRIF-mediated secretion. (B) Number of proteins predominantly released by signal transduction through MyD88 or TRIF, respectively. Shades indicate the strength of differential regulation in log10. (C) TRIF-dominated and (D) MyD88-dominated protein secretion. (Top) Median with interquartile range for proteins whose secretion depended on the adaptor. (Bottom) Heat map of differentially regulated proteins (gray denotes missing values). (E) Contribution of MyD88 and TRIF to the secretory response induced by both adaptors. Number of proteins plotted versus the strength of the contribution for the indicated genotype.

 

Figure 4
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Fig. 4. Signaling adaptor interplay. (A) Contribution of MyD88 and TRIF to the secretory output illustrated for redundant and synergistic adaptor interplay. (B) Number of proteins with redundant and synergistic regulation over time. Centers of the bins are indicated. (C) Progressive adaptor interplay with median and ranges. Trends for synergistic and redundant protein regulations as median with interquartile range are indicated in red and gray, respectively. (D) Adaptor interplay ranked as maximal difference between WT and a combination of MyD88 and TRIF. Proteins with a maximal redundant regulation <–1.5 are shaded. (E) Synergistic protein secretion. (Top) Median with interquartile range of proteins requiring both adaptors for maximal secretion. (Bottom) Heat map of regulated proteins. (F and G) Proteins with increased secretion in the single-adaptor KOs compared with WT. (F) Number of proteins with the indicated strength of differential regulation in log10. (G) Protein secretion of MyD88- and TRIF-dependent proteins. (Top) Median with interquartile range. (Bottom) Heat map of regulated proteins.

 


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