Sepsis is an extreme host response to infection that leads to loss of organ function and cardiovascular integrity. Mortality from sepsis is on the rise. Despite more than three decades of research and clinical trials, specific diagnostic and therapeutic strategies for sepsis are still absent. The use of LFQ‐ and TMT‐based quantitative proteomics is reported here to study the plasma proteome in five mouse models of sepsis. A knowledge‐based interpretation of the data reveals a protein network with extensive connectivity through documented functional or physical interactions. The individual proteins in the network all have a documented role in sepsis and are known to be extracellular. The changes in protein abundance observed in the mouse models of sepsis have for the most part the same directionality (increased or decreased abundance) as reported in the literature for human sepsis. This network has been named the Plasma Proteome Signature of Sepsis (PPSS). The PPSS is a quantifiable molecular readout that can supplant the current symptom‐based approach used to diagnose sepsis. This type of molecular interpretation of sepsis, its progression, and its response to therapeutic intervention are an important step in advancing our understanding of sepsis, and for discovering and evaluating new therapeutic strategies.

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Efficient immune responses depend on signaling networks coordinated by dynamic protein-protein interactions. Adaptor proteins, like TRAF3, participates in distinct signaling pathways critical for immune development and host defense. To identify distinct complexes of TRAF3 that may serve as important modulators, we carried out Affinity-Purification-Mass Spectrometry from different human immune cell lines. We identified a core group of cell line-independent protein-protein interactions and then used machine learning to infer distinct functional complexes. Our analysis revealed a unique, switch-like relationship between TRAF3, signaling molecule TRAF2, and RNA-binding protein EWS. With protein-binding experiments, we found that EWS associates with TRAF3 in the absence of TRAF2. Given that formation of the TRAF3:TRAF2 complex is a regulatory mechanism for some Tumor Necrosis Factor Receptor Superfamily members, we hypothesized that EWS may have a novel function downstream of these pathways. To test our hypothesis, we used LTβR as a prototype given its well-studied signaling through the TRAF3:TRAF2 complex. With endogenous proteins, we found that EWS associates with TRAF3 in unstimulated cells; however, dissociation occurs following LTβR stimulation. To infer a potential function of EWS for LTβR signaling, we carried out machine learning analysis of RNAseq expression data generated from unmodified fibroblasts, following LTβR stimulation. Our unsupervised analysis predicts a critical function for EWS in mRNA transport and expression of several NF-κB regulatory molecules. With RNAi, transcriptomics, and a NF-κB reporter system, we show that EWS is required for optimal activation of LTβR signaling.

One of the most common genetic alterations in glioblastoma (30-40%) occurs in the tumor suppressor gene PTEN (phosphatase and tensin homolog), where loss of function has been mechanistically linked to increased tumor cell invasion, and to a lack of radio- and chemo-therapy response. To identify new drug compounds that target PTEN-deficient brain tumors we performed a high throughput drug screening using patient-derived GBM spheres and found that PTEN-deficient samples were highly sensitive to proteasome inhibitors. We confirmed PTEN dependency to proteasome inhibition by genetically overexpressing or deleting PTEN in GBM cells and established that the drug inhibition response relied on PTEN enzymatic activity. Additionally, proteasome inhibition specifically suppressed tumor growth and increased survival of mice orthotopically engrafted with human PTEN-null glioblastoma cancer stem cells. Mechanistically, we determined that PTEN-deficient cells were more sensitive to proteasome inhibition due to an increase in protein synthesis rate and loss of autophagy activity associated with activation of the PI3K/mTOR pathway. Finally, integrated in silico analysis of the Samsung Medical Center dataset corroborated that samples with PTEN/EGFR/PI3K alterations were significantly more sensitive to Carfilzomib, an FDA-approved proteasome inhibitor. This study reveals that PTEN-deficient cells are "proteasome addictive" and opens a new therapeutic opportunity to treat glioblastoma PTEN-deficient patients. Citation Format: Jorge A. Benitez, Darren Finlay, Alexandre Rosa-Campos, Jianhui Ma, Tomoyuki Koga, Kristiina Vuori, Frank Furnari. Proteasome addiction a new therapeutic opportunity to treat PTEN-deficient brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-110.