Deciphering sources of PET signals in the tumor microenvironment of glioblastoma at cellular resolution.

Various cellular sources hamper interpretation of positron emission tomography (PET) biomarkers in the tumor microenvironment (TME). We developed an approach of immunomagnetic cell sorting after in vivo radiotracer injection (scRadiotracing) with three-dimensional (3D) histology to dissect the cellular allocation of PET signals in the TME. In mice with implanted glioblastoma, translocator protein (TSPO) radiotracer uptake per tumor cell was higher compared to tumor-associated microglia/macrophages (TAMs), validated by protein levels. Translation of in vitro scRadiotracing to patients with glioma immediately after tumor resection confirmed higher single-cell TSPO tracer uptake of tumor cells compared to immune cells. Across species, cellular radiotracer uptake explained the heterogeneity of individual TSPO-PET signals. In consideration of cellular tracer uptake and cell type abundance, tumor cells were the main contributor to TSPO enrichment in glioblastoma; however, proteomics identified potential PET targets highly specific for TAMs. Combining cellular tracer uptake measures with 3D histology facilitates precise allocation of PET signals and serves to validate emerging novel TAM-specific radioligands.

OAS1/RNase L executes RIG-I ligand-dependent tumor cell apoptosis.

Cytoplasmic double-stranded RNA is sensed by RIG-I-like receptors (RLRs), leading to induction of type I interferons (IFN-Is), proinflammatory cytokines, and apoptosis. Here, we elucidate signaling mechanisms that lead to cytokine secretion and cell death induction upon stimulation with the bona fide RIG-I ligand 5'-triphosphate RNA (3p-RNA) in tumor cells. We show that both outcomes are mediated by dsRNA-receptor families with RLR being essential for cytokine production and IFN-I-mediated priming of effector pathways but not for apoptosis. Affinity purification followed by mass spectrometry and subsequent functional analysis revealed that 3p-RNA bound and activated oligoadenylate synthetase 1 and RNase L. RNase L-deficient cells were profoundly impaired in their ability to undergo apoptosis. Mechanistically, the concerted action of translational arrest triggered by RNase L and up-regulation of NOXA was needed to deplete the antiapoptotic MCL-1 to cause intrinsic apoptosis. Thus, 3p-RNA-induced apoptosis is a two-step process consisting of RIG-I-dependent priming and an RNase L-dependent effector phase.

BACE2 distribution in major brain cell types and identification of novel substrates.

ß-Site APP-cleaving enzyme 1 (BACE1) inhibition is considered one of the most promising therapeutic strategies for Alzheimer's disease, but current BACE1 inhibitors also block BACE2. As the localization and function of BACE2 in the brain remain unknown, it is difficult to predict whether relevant side effects can be caused by off-target inhibition of BACE2 and whether it is important to generate BACE1-specific inhibitors. Here, we show that BACE2 is expressed in discrete subsets of neurons and glia throughout the adult mouse brain. We uncover four new substrates processed by BACE2 in cultured glia: vascular cell adhesion molecule 1, delta and notch-like epidermal growth factor-related receptor, fibroblast growth factor receptor 1, and plexin domain containing 2. Although these substrates were not prominently cleaved by BACE2 in healthy adult mice, proinflammatory TNF induced a drastic increase in BACE2-mediated shedding of vascular cell adhesion molecule 1 in CSF. Thus, although under steady-state conditions the effect of BACE2 cross-inhibition by BACE1-directed inhibitors is rather subtle, it is important to consider that side effects might become apparent under physiopathological conditions that induce TNF expression.

Common and phylogenetically widespread coding for peptides by bacterial small RNAs.

BACKGROUND: While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. METHODS: Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in 14 phylogenetically diverse bacteria. Importantly, we quantify uncertainty in our predictions, and follow up on them using mass spectrometry proteomics and comparison to datasets including ribosome profiling. RESULTS: A majority of annotated sRNAs have at least one ORF between 10 and 50 amino acids long, and we conservatively predict that 409±191.7 unannotated sRNA ORFs are under selection to maintain coding (mean estimate and 95% confidence interval), an average of 29 per species considered here. This implies that overall at least 10.3±0.5% of sRNAs have a coding ORF, and in some species around 20% do. 165±69 of these novel coding ORFs have some antisense overlap to annotated ORFs. As experimental validation, many of our predictions are translated in published ribosome profiling data and are identified via mass spectrometry shotgun proteomics. B. subtilis sRNAs with coding ORFs are enriched for high expression in biofilms and confluent growth, and S. pneumoniae sRNAs with coding ORFs are involved in virulence. sRNA coding ORFs are enriched for transmembrane domains and many are predicted novel components of type I toxin/antitoxin systems. CONCLUSIONS: We predict over two dozen new protein-coding genes per bacterial species, but crucially also quantified the uncertainty in this estimate. Our predictions for sRNA coding ORFs, along with predicted novel type I toxins and tools for sorting and visualizing genomic context, are freely available in a user-friendly format at http://disco-bac.web.pasteur.fr. We expect these easily-accessible predictions to be a valuable tool for the study not only of bacterial sRNAs and type I toxin-antitoxin systems, but also of bacterial genetics and genomics.