Furthermore, we revealed that A3B was expressed mainly in plasma cells, CD10+ B cells and erythroid cells, but not in granulocyte-macrophage progenitors. A3B expression profiling in normal blood cells may contribute to understanding the defense mechanism of A3B against viruses, and partially explain the bias of APOBEC mutational signatures in lymphoid but not myeloid malignancies. This study identified the cells and cellular phase in which A3B is highly expressed, which may help reveal the mechanisms behind carcinogenesis and cancer heterogeneity, as well as the biological functions of A3B in normal blood cells.In recent years, pollution of surface waters with xenobiotic compounds became an issue of concern in society and has been the object of numerous studies. Most of these xenobiotic compounds are man-made molecules and some of them are qualified as endocrine disrupting chemicals (EDCs) when they interfere with hormones actions. Several studies have investigated the teratogenic impacts of EDCs in vertebrates (including marine vertebrates). However, the impact of such EDCs on marine invertebrates is much debated and still largely obscure. In addition, DNA-altering genotoxicants can induce embryonic malformations. The goal of this study is to develop a reliable and effective test for assessing toxicity of chemicals using embryos of the ascidian (Phallusia mammillata) in order to find phenotypic signatures associated with xenobiotics. We evaluated embryonic malformations with high-content analysis of larval phenotypes by scoring several quantitative and qualitative morphometric endpoints on a single image of Phallusia tadpole larvae with semi-automated image analysis. Using this approach we screened different classes of toxicants including genotoxicants, known or suspected EDCs and nuclear receptors (NRs) ligands. The screen presented here reveals a specific phenotypic signature for ligands of retinoic acid receptor/retinoid X receptor. Analysis of larval morphology combined with DNA staining revealed that embryos with DNA aberrations displayed severe malformations affecting multiple aspects of embryonic development. In contrast EDCs exposure induced no or little DNA aberrations and affected mainly neural development. Therefore the ascidian embryo/larval assay presented here can allow to distinguish the type of teratogenicity induced by different classes of toxicants.Extracellular vesicle (EV) proteins from acute myeloid leukemia (AML) cell lines were analyzed using mass spectrometry. The analyses identified 2450 proteins, including 461 differentially expressed proteins (290 upregulated and 171 downregulated). CD53 and CD47 were upregulated and were selected as candidate biomarkers. The association between survival of patients with AML and the expression levels of CD53 and CD47 at diagnosis was analyzed using mRNA expression data from The Cancer Genome Atlas database. Patients with higher expression levels showed significantly inferior survival than those with lower expression levels. ELISA results of the expression levels of CD53 and CD47 from EVs in the bone marrow of patients with AML at diagnosis and at the time of complete remission with induction chemotherapy revealed that patients with downregulated CD53 and CD47 expression appeared to relapse less frequently. Network model analysis of EV proteins revealed several upregulated kinases, including LYN, CSNK2A1, SYK, CSK, and PTK2B. The potential cytotoxicity of several clinically applicable drugs that inhibit these kinases was tested in AML cell lines. The drugs lowered the viability of AML cells. The collective data suggest that AML cell-derived EVs could reflect essential leukemia biology.Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multimember protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network.  https://www.selleckchem.com/products/beta-aminopropionitrile.html  We show that clust.perturb results are predictive of real-world cluster stability poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry-based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17, and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared with interferon gamma (IFNɣ) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes.