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. 2018 Jun 5;9:407. doi: 10.3389/fneur.2018.00407

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Copyright © 2018 Ruland, Wolbert, Gottschalk, König, Schulte-Mecklenbeck, Minnerup, Meuth, Groß, Wiendl and Meyer zu Hörste.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Unbiased proteomics identifies proteins with differential abundance in CSF in PACNS. (A) Volcano plot showing all identified proteins and highlighting proteins with differential abundance. Proteins with log2(fold change) >1 are highlighted in orange and proteins with an adjusted p-value ≤ 0.05 and a log2(fold change) >1 are highlighted in magenta comparing PACNS vs. IIH samples. Names of proteins with significantly different abundance are provided in the figure. (B) Network of proteins with an adjusted p-value ≤ 0.05 built with NetworkAnalyst. The network shows the proteins identified in our LC-UDMSe analysis as well as proteins that directly interact with a given protein to study key nodes of functional connectivity. Red color indicates upregulated, green color downregulated proteins based on fold change. (C) Boxplot of the percentage of CD3+ T cells in peripheral blood for PACNS vs. IIH samples (p = 0.029). (D) Boxplot of the concentrations of APP in CSF of PACNS vs. IHH samples (p = 6.41E-05).