Highlights d A pan-tissue AHR signature identifies IL4I1 as a major AHRactivating enzyme d IL4I1-mediated Trp catabolism yields indoles and kynurenic acid that activate the AHR d IL4I1 promotes AHR-driven cancer cell motility and suppresses adaptive immunity d IL4I1 enhances CLL progression and is induced by immune checkpoint blockade
All cells and organisms exhibit stress-coping mechanisms to ensure survival. Cytoplasmic protein-RNA assemblies termed stress granules are increasingly recognized to promote cellular survival under stress. Thus, they might represent tumor vulnerabilities that are currently poorly explored. The translation-inhibitory eIF2α kinases are established as main drivers of stress granule assembly. Using a systems approach, we identify the translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regulator mammalian target of rapamycin complex 1 (mTORC1) to promote stress granule assembly. When highly active, PI3K is the main driver of stress granules; however, the impact of p38 becomes apparent as PI3K activity declines. PI3K and p38 thus act in a hierarchical manner to drive mTORC1 activity and stress granule assembly. Of note, this signaling hierarchy is also present in human breast cancer tissue. Importantly, only the recognition of the PI3K-p38 hierarchy under stress enabled the discovery of p38’s role in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as they hierarchically promote stress granule formation.
The PI3K/Akt pathway promotes skeletal muscle growth and myogenic differentiation. Although its importance in skeletal muscle biology is well documented, many of its substrates remain to be identified. We here studied PI3K/Akt signaling in contracting skeletal muscle cells by quantitative phosphoproteomics. We identified the extended basophilic phosphosite motif RxRxxp[S/T]xxp[S/T] in various proteins including filamin-C (FLNc). Importantly, this extended motif, located in a unique insert in Ig-like domain 20 of FLNc, is doubly phosphorylated. The protein kinases responsible for this dual-site phosphorylation are Akt and PKCα. Proximity proteomics and interaction analysis identified filamin A-interacting protein 1 (FILIP1) as direct FLNc binding partner. FILIP1 binding induces filamin degradation, thereby negatively regulating its function. Here, dual-site phosphorylation of FLNc not only reduces FILIP1 binding, providing a mechanism to shield FLNc from FILIP1-mediated degradation, but also enables fast dynamics of FLNc necessary for its function as signaling adaptor in cross-striated muscle cells.
We describe the approach to event extraction which the JULIELab Team from FSU Jena (Germany) pursued to solve Task 1 in the "BioNLP'09 Shared Task on Event Extraction". We incorporate manually curated dictionaries and machine learning methodologies to sort out associated event triggers and arguments on trimmed dependency graph structures. Trimming combines pruning irrelevant lexical material from a dependency graph and decorating particularly relevant lexical material from that graph with more abstract conceptual class information. Given that methodological framework, the JULIELab Team scored on 2nd rank among 24 competing teams, with 45.8% precision, 47.5% recall and 46.7% F1-score on all 3,182 events.
In our approach to event extraction, dependency graphs constitute the fundamental data structure for knowledge capture. Two types of trimming operations pave the way to more effective relation extraction. First, we simplify the syntactic representation structures resulting from parsing by pruning informationally irrelevant lexical material from dependency graphs. Second, we enrich informationally relevant lexical material in the simplified dependency graphs with additional semantic meta data at several layers of conceptual granularity. These two aggregation operations on linguistic representation structures are intended to avoid overfitting of machine learning-based classifiers which we use for event extraction (besides manually curated dictionaries). Given this methodological framework, the corresponding JREX system developed by the JULIELab Team from Friedrich-Schiller-Universität Jena (Germany) scored on 2nd rank among 24 competing teams for Task 1 in the "BioNLP'09 Shared Task on Event Extraction," with 45.8% recall, 47.5% precision and 46.7% F1-score on all 3,182 events. In more recent experiments, based on slight modifications of JREX and using the same data sets, we were able to achieve 45.9% recall, 57.7% precision, and 51.1% F1-score.
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