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.

Cellular senescence is a terminal differentiation state that has been proposed to have a role in both tumour suppression and ageing. This view is supported by the fact that accumulation of senescent cells can be observed in response to oncogenic stress as well as a result of normal organismal ageing. Thus, identifying senescent cells in in vivo and in vitro has an important diagnostic and therapeutic potential. The molecular pathways involved in triggering and/or maintaining the senescent phenotype are not fully understood. As a consequence, the markers currently utilized to detect senescent cells are limited and lack specificity. In order to address this issue, we screened for plasma membrane-associated proteins that are preferentially expressed in senescent cells. We identified 107 proteins that could be potential markers of senescence and validated 10 of them (DEP1, NTAL, EBP50, STX4, VAMP3, ARMX3, B2MG, LANCL1, VPS26A and PLD3). We demonstrated that a combination of these proteins can be used to specifically recognize senescent cells in culture and in tissue samples and we developed a straightforward fluorescence-activated cell sorting-based detection approach using two of them (DEP1 and B2MG). Of note, we found that expression of several of these markers correlated with increased survival in different tumours, especially in breast cancer. Thus, our results could facilitate the study of senescence, define potential new effectors and modulators of this cellular mechanism and provide potential diagnostic and prognostic tools to be used clinically.

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. mammary stem cells | p53 family | breast cancer

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.

Activation of serine biosynthesis supports growth and proliferation of cancer cells. Human cancers often exhibit overexpression of phosphoglycerate dehydrogenase (PHGDH), the metabolic enzyme that catalyses the reaction that diverts serine biosynthesis from the glycolytic pathway. By refueling serine biosynthetic pathways, cancer cells sustain their metabolic requirements, promoting macromolecule synthesis, anaplerotic flux and ATP. Serine biosynthesis intersects glutaminolysis and together with this pathway provides substrates for production of antioxidant GSH. In human lung adenocarcinomas we identified a correlation between serine biosynthetic pathway and p73 expression. Metabolic profiling of human cancer cell line revealed that TAp73 activates serine biosynthesis, resulting in increased intracellular levels of serine and glycine, associated to accumulation of glutamate, tricarboxylic acid (TCA) anaplerotic intermediates and GSH. However, at molecular level p73 does not directly regulate serine metabolic enzymes, but transcriptionally controls a key enzyme of glutaminolysis, glutaminase-2 (GLS-2). p73, through GLS-2, favors conversion of glutamine in glutamate, which in turn drives the serine biosynthetic pathway. Serine and glutamate can be then employed for GSH synthesis, thus the p73-dependent metabolic switch enables potential response against oxidative stress. In knockdown experiment, indeed, TAp73 depletion completely abrogates cancer cell proliferation capacity in serine/glycine-deprivation, supporting the role of p73 to help cancer cells under metabolic stress. These findings implicate p73 in regulation of cancer metabolism and suggest that TAp73 influences glutamine and serine metabolism, affecting GSH synthesis and determining cancer pathogenesis.

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.

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.

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/.