Anti-Proliferative and Cytoprotective Activity of Aryl Carbamate and Aryl Urea Derivatives with Alkyl Groups and Chlorine as Substituents.

Natural cytokinines are a promising group of cytoprotective and anti-tumor agents. In this research, we synthesized a set of aryl carbamate, pyridyl urea, and aryl urea cytokinine analogs with alkyl and chlorine substitutions and tested their antiproliferative activity in MDA-MB-231, A-375, and U-87 MG cell lines, and cytoprotective properties in H2O2 and CoCl2 models. Aryl carbamates with the oxamate moiety were selectively anti-proliferative for the cancer cell lines tested, while the aryl ureas were inactive. In the cytoprotection studies, the same aryl carbamates were able to counteract the CoCl2 cytotoxicity by 3-8%. The possible molecular targets of the aryl carbamates during the anti-proliferative action were the adenosine A2 receptor and CDK2. The obtained results are promising for the development of novel anti-cancer therapeutics.

ZNF281 inhibits neuronal differentiation and is a prognostic marker for neuroblastoma.

Derangement of cellular differentiation because of mutation or inappropriate expression of specific genes is a common feature in tumors. Here, we show that the expression of ZNF281, a zinc finger factor involved in several cellular processes, decreases during terminal differentiation of murine cortical neurons and in retinoic acid-induced differentiation of neuroblastoma (NB) cells. The ectopic expression of ZNF281 inhibits the neuronal differentiation of murine cortical neurons and NB cells, whereas its silencing causes the opposite effect. Furthermore, TAp73 inhibits the expression of ZNF281 through miR34a. Conversely, MYCN promotes the expression of ZNF281 at least in part by inhibiting miR34a. These findings imply a functional network that includes p73, MYCN, and ZNF281 in NB cells, where ZNF281 acts by negatively affecting neuronal differentiation. Array analysis of NB cells silenced for ZNF281 expression identified GDNF and NRP2 as two transcriptional targets inhibited by ZNF281. Binding of ZNF281 to the promoters of these genes suggests a direct mechanism of repression. Bioinformatic analysis of NB datasets indicates that ZNF281 expression is higher in aggressive, undifferentiated stage 4 than in localized stage 1 tumors supporting a central role of ZNF281 in affecting the differentiation of NB. Furthermore, patients with NB with high expression of ZNF281 have a poor clinical outcome compared with low-expressors. These observations suggest that ZNF281 is a controller of neuronal differentiation that should be evaluated as a prognostic marker in NB.

p53 mutants cooperate with HIF-1 in transcriptional regulation of extracellular matrix components to promote tumor progression.

Mutations in the TP53 gene and microenvironmentally driven activation of hypoxia-inducible factor-1 (HIF-1) typically occur in later stages of tumorigenesis. An ongoing challenge is the identification of molecular determinants of advanced cancer pathogenesis to design alternative last-line therapeutic options. Here, we report that p53 mutants influence the tumor microenvironment by cooperating with HIF-1 to promote cancer progression. We demonstrate that in non-small cell lung cancer (NSCLC), p53 mutants exert a gain-of-function (GOF) effect on HIF-1, thus regulating a selective gene expression signature involved in protumorigenic functions. Hypoxia-mediated activation of HIF-1 leads to the formation of a p53 mutant/HIF-1 complex that physically binds the SWI/SNF chromatin remodeling complex, promoting expression of a selective subset of hypoxia-responsive genes. Depletion of p53 mutants impairs the HIF-mediated up-regulation of extracellular matrix (ECM) components, including type VIIa1 collagen and laminin-γ2, thus affecting tumorigenic potential of NSCLC cells in vitro and in mouse models in vivo. Analysis of surgically resected human NSCLC revealed that expression of this ECM gene signature was highly correlated with hypoxic tumors exclusively in patients carrying p53 mutations and was associated with poor prognosis. Our data reveal a GOF effect of p53 mutants in hypoxic tumors and suggest synergistic activities of p53 and HIF-1. These findings have important implications for cancer progression and might provide innovative last-line treatment options for advanced NSCLC.

Serine and glycine metabolism in cancer.

Serine and glycine are biosynthetically linked, and together provide the essential precursors for the synthesis of proteins, nucleic acids, and lipids that are crucial to cancer cell growth. Moreover, serine/glycine biosynthesis also affects cellular antioxidative capacity, thus supporting tumour homeostasis. A crucial contribution of serine/glycine to cellular metabolism is through the glycine cleavage system, which refuels one-carbon metabolism; a complex cyclic metabolic network based on chemical reactions of folate compounds. The importance of serine/glycine metabolism is further highlighted by genetic and functional evidence indicating that hyperactivation of the serine/glycine biosynthetic pathway drives oncogenesis. Recent developments in our understanding of these pathways provide novel translational opportunities for drug development, dietary intervention, and biomarker identification of human cancers.

p63 Sustains self-renewal of mammary cancer stem cells through regulation of Sonic Hedgehog signaling.

The predominant p63 isoform, ΔNp63, is a master regulator of normal epithelial stem cell (SC) maintenance. However, in vivo evidence of the regulation of cancer stem cell (CSC) properties by p63 is still limited. Here, we exploit the transgenic MMTV-ErbB2 (v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2) mouse model of carcinogenesis to dissect the role of p63 in the regulation of mammary CSC self-renewal and breast tumorigenesis. ErbB2 tumor cells enriched for SC-like properties display increased levels of ΔNp63 expression compared with normal mammary progenitors. Down-regulation of p63 in ErbB2 mammospheres markedly restricts self-renewal and expansion of CSCs, and this action is fully independent of p53. Furthermore, transplantation of ErbB2 progenitors expressing shRNAs against p63 into the mammary fat pads of syngeneic mice delays tumor growth in vivo. p63 knockdown in ErbB2 progenitors diminishes the expression of genes encoding components of the Sonic Hedgehog (Hh) signaling pathway, a driver of mammary SC self-renewal. Remarkably, p63 regulates the expression of Sonic Hedgehog (Shh), GLI family zinc finger 2 (Gli2), and Patched1 (Ptch1) genes by directly binding to their gene regulatory regions, and eventually contributes to pathway activation. Collectively, these studies highlight the importance of p63 in maintaining the self-renewal potential of mammary CSCs via a positive modulation of the Hh signaling pathway.

AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System.

BACKGROUND: Exploration of the Adverse Event Reporting System (AERS) data by a wide scientific community is limited due to several factors. First, AERS data must be intensively preprocessed to be converted into analyzable format. Second, application of the currently accepted disproportional reporting measures results in false positive signals. METHODS: We proposed a data mining strategy to improve hypothesis generation with respect to potential associations. RESULTS: By numerous examples, we illustrate that our strategy controls the false positive signals. We implemented a free online tool, AERS spider (www.chemoprofiling.org/AERS). CONCLUSIONS: We believe that AERS spider would be a valuable tool for drug safety experts.

BioProfiling.de: analytical web portal for high-throughput cell biology.

BioProfiling.de provides a comprehensive analytical toolkit for the interpretation gene/protein lists. As input, BioProfiling.de accepts a gene/protein list. As output, in one submission, the gene list is analyzed by a collection of tools which employs advanced enrichment or network-based statistical frameworks. The gene list is profiled with respect to the most information available regarding gene function, protein interactions, pathway relationships, in silico predicted microRNA to gene associations, as well as, information collected by text mining. BioProfiling.de provides a user friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. The web portal is freely available at http://www.BioProfiling.de/gene_list.

MIPS: curated databases and comprehensive secondary data resources in 2010.

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38,000,000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

CCancer: a bird's eye view on gene lists reported in cancer-related studies.

CCancer is an automatically collected database of gene lists, which were reported mostly by experimental studies in various biological and clinical contexts. At the moment, the database covers 3369 gene lists extracted from 2644 papers published in approximately 80 peer-reviewed journals. As input, CCancer accepts a gene list. An enrichment analyses is implemented to generate, as output, a highly informative survey over recently published studies that report gene lists, which significantly intersect with the query gene list. A report on gene pairs from the input list which were frequently reported together by other biological studies is also provided. CCancer is freely available at http://mips.helmholtz-muenchen.de/proj/ccancer.

R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases.

R spider is a web-based tool for the analysis of a gene list using the systematic knowledge of core pathways and reactions in human biology accumulated in the Reactome and KEGG databases. R spider implements a network-based statistical framework, which provides a global understanding of gene relations in the supplied gene list, and fully exploits the Reactome and KEGG knowledge bases. R spider provides a user-friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. R spider is freely available at http://mips.helmholtz-muenchen.de/proj/rspider.

GeneSet2miRNA: finding the signature of cooperative miRNA activities in the gene lists.

GeneSet2miRNA is the first web-based tool which is able to identify whether or not a gene list has a signature of miRNA-regulatory activity. As input, GeneSet2miRNA accepts a list of genes. As output, a list of miRNA-regulatory models is provided. A miRNA-regulatory model is a group of miRNAs (single, pair, triplet or quadruplet) that is predicted to regulate a significant subset of genes from the submitted list. GeneSet2miRNA provides a user friendly dialog-driven web page submission available for several model organisms. GeneSet2miRNA is freely available at http://mips.helmholtz-muenchen.de/proj/gene2mir/.

New immunological potential markers for triple negative breast cancer: IL18R1, CD53, TRIM, Jaw1, LTB, PTPRCAP.

Breast cancer (BC) is the second leading cause of cancer death in women worldwide, and settings of specific prognostic factors and efficacious therapies are made difficult by phenotypic heterogeneity of BC subtypes. Therefore, there is a current urgent need to define novel predictive genetic predictors that may be useful for stratifying patients with distinct prognostic outcomes. Here, we looked for novel molecular signatures for triple negative breast cancers (TNBCs). By a bioinformatic approach, we identified a panel of genes, whose expression was positively correlated with disease-free survival in TNBC patients, namely IL18R1, CD53, TRIM, Jaw1, LTB, and PTPRCAP, showing specific immune expression profiles linked to survival prediction; most of these genes are indeed expressed in immune cells and are required for productive lymphocyte activation. According to our hypothesis, these genes were not, or poorly, expressed in different TNBC cell lines, derived from either primary breast tumours or metastatic pleural effusions. This conclusion was further supported in vivo, as immuno-histochemical analysis on biopsies of TNBC invasive ductal carcinomas highlighted differential expression of these six genes in cancer cells, as well as in intra- and peri-tumoral infiltrating lymphocytes. Our data open to the possibility that inter-tumour heterogeneity of immune markers might have predictive value; further investigations are recommended in order to establish the real power of cancer-related immune profiles as prognostic factors.

The DICS repository: module-assisted analysis of disease-related gene lists.

SUMMARY: The DICS database is a dynamic web repository of computationally predicted functional modules from the human protein-protein interaction network. It provides references to the CORUM, DrugBank, KEGG and Reactome pathway databases. DICS can be accessed for retrieving sets of overlapping modules and protein complexes that are significantly enriched in a gene list, thereby providing valuable information about the functional context. AVAILABILITY: Supplementary information on datasets and methods is available on the web server http://mips.gsf.de/proj/dics.

ProfCom: a web tool for profiling the complex functionality of gene groups identified from high-throughput data.

ProfCom is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of 'complex functions'. A 'Complex function' is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. ProfCom provides a user friendly dialog-driven web page submission available for several model organisms and supports most available gene identifiers. In addition, the web service interface allows the submission of any kind of annotation data. ProfCom is freely available at http://webclu.bio.wzw.tum.de/profcom/.

PPI spider: a tool for the interpretation of proteomics data in the context of protein-protein interaction networks.

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein-protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web-based tool, PPI spider (http://mips.helmholtz-muenchen.de/proj/ppispider). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.

Complex phylogenetic profiling reveals fundamental genotype-phenotype associations.

We have developed a computational technique refereed to as complex phylogenetic profiling. Our approach combines logic analyses of gene phylogenetic profiles and phenotype data. Logic analysis of phylogenetic profiles identifies sets of proteins whose presence or absence follows certain logic relationships. Our approach identifies phenotype specific logic, i.e. it identifies sets of proteins simultaneously present or absent only in genomes with a given phenotype. For example, for most genomes expressing phenotype A, the presence of protein C presumes the presence of protein B, while for other genomes (not expressing phenotype A) the presence of protein C presumes the absence of protein B. Application of complex phylogenetic profiling to bacterial data and several well studied phenotypes reveals genotype-phenotype associations on the level of fundamental biochemical pathways.

N-Glycosylation in Chrysosporium lucknowense enzymes.

Twenty-eight enzymes, encoded by different genes and secreted by different mutant strains of Chrysosporium lucknowense, were subjected to MALDI-TOF MS peptide fingerprinting followed by analysis of the MS data using the GlycoMod tool from the ExPASy proteomic site. Various N-linked glycan structures were discriminated in the C. lucknowense proteins as a result of the analysis. N-Glycosylated peptides with modifications matching the oligosaccharide compositions contained in the GlycoSuiteDB were found in 12 proteins. The most frequently encountered N-linked glycan, found in 9 peptides from 7 proteins, was (Man)(3)(GlcNAc)(2), that is, the core pentasaccharide structure forming mammalian-type high-mannose and hybrid/complex glycans in glycoproteins from different organisms. Nine out of 12 enzymes represented variably N-glycosylated proteins carrying common (Hex)(0-4)(HexNAc)(0-6)+(Man)(3)(GlcNAc)(2) structures, most of them being hybrid/complex glycans. Various glycan structures were likely formed as a result of the enzymatic trimming of a 'parent' oligosaccharide with different glycosidases. The N-glycosylation patterns found in C. lucknowense proteins differ from those reported for the extensively studied enzymes from Aspergilli and Trichoderma species, where high-mannose glycans of variable structure have been detected.

A systematic approach to infer biological relevance and biases of gene network structures.

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Alterations in neuronal metabolism contribute to the pathogenesis of prion disease.

Neurodegenerative conditions are characterised by a progressive loss of neurons, which is believed to be initiated by misfolded protein aggregations. During this time period, many physiological and metabolomic alterations and changes in gene expression contribute to the decline in neuronal function. However, these pathological effects have not been fully characterised. In this study, we utilised a metabolomic approach to investigate the metabolic changes occurring in the hippocampus and cortex of mice infected with misfolded prion protein. In order to identify these changes, the samples were analysed by ultrahigh-performance liquid chromatography-tandem mass spectroscopy. The present dataset comprises a total of 498 compounds of known identity, named biochemicals, which have undergone principal component analysis and supervised machine learning. The results generated are consistent with the prion-inoculated mice having significantly altered metabolic profiles. In particular, we highlight the alterations associated with the metabolism of glucose, neuropeptides, fatty acids, L-arginine/nitric oxide and prostaglandins, all of which undergo significant changes during the disease. These data provide possibilities for future studies targeting and investigating specific pathways to better understand the processes involved in neuronal dysfunction in neurodegenerative diseases.

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.