Targeted expression of tumor necrosis factor-related apoptosis-inducing ligand TRAIL in skin protects mice against chemical carcinogenesis

<p>Abstract</p> <p>Background</p> <p>Gene ablation studies have revealed that tumor necrosis factor-related apoptosis-inducing ligand (TRAIL, Apo2L, TNFSF10) plays a crucial role in tumor surveillance, as TRAIL-deficient mice exhibit an increased sensitivity to different types of tumorigenesis. In contrast, possible tumor-protective effect of increased levels of endogenous TRAIL expression <it>in vivo </it>has not been assessed yet. Such models will provide important information about the efficacy of TRAIL-based therapies and potential toxicity in specific tissues.</p> <p>Methods</p> <p>To this aim, we engineered transgenic mice selectively expressing TRAIL in the skin and subjected these mice to a two-step chemical carcinogenesis protocol that generated benign and preneoplastic lesions. We were therefore able to study the effect of increased TRAIL expression at the early steps of skin tumorigenesis.</p> <p>Results</p> <p>Our results showed a delay of tumor appearance in TRAIL expressing mice compared to their wild-type littermates. More importantly, the number of tumors observed in transgenic animals was significantly lower than in the control animals, and the lesions observed were mostly benign. Interestingly, Wnt/β-catenin signaling differed between tumors of wild-type and TRAIL transgenics.</p> <p>Conclusion</p> <p>Altogether, these data reveal that, at least in this model, TRAIL is able on its own to act on pre-transformed cells, and reduce their tumorigenic potential.</p

Retinoic acid protects human breast cancer cells against etoposide-induced apoptosis by NF-kappaB-dependent but cIAP2-independent mechanisms

<p>Abstract</p> <p>Background</p> <p>Retinoids, through their cognate nuclear receptors, exert potent effects on cell growth, differentiation and apoptosis, and have significant promise for cancer therapy and chemoprevention. These ligands can determine the ultimate fate of target cells by stimulating or repressing gene expression directly, or indirectly through crosstalking with other signal transducers.</p> <p>Results</p> <p>Using different breast cancer cell models, we show here that depending on the cellular context retinoids can signal either towards cell death or cell survival. Indeed, retinoids can induce the expression of pro-apoptotic (i.e. TRAIL, TNF-Related Apoptosis-Inducing Ligand, Apo2L/TNFSF10) and anti-apoptotic (i.e. cIAP2, inhibitor of apoptosis protein-2) genes. Promoter mapping, gel retardation and chromatin immunoprecipitation assays revealed that retinoids induce the expression of this gene mainly through crosstalk with NF-kappaB. Supporting this crosstalk, the activation of NF-kappaB by retinoids in T47D cells antagonizes the apoptosis triggered by the chemotherapeutic drugs etoposide, camptothecin or doxorubicin. Notably apoptosis induced by death ligands (i.e. TRAIL or antiFAS) is not antagonized by retinoids. That knockdown of cIAP2 expression by small interfering RNA does not alter the inhibition of etoposide-induced apoptosis by retinoids in T47D cells reveals that stimulation of cIAP2 expression is not the cause of their anti-apoptotic action. However, ectopic overexpression of a NF-kappaB repressor increases apoptosis by retinoids moderately and abrogates almost completely the retinoid-dependent inhibition of etoposide-induced apoptosis. Our data exclude cIAP2 and suggest that retinoids target other regulator(s) of the NF-kappaB signaling pathway to induce resistance to etoposide on certain breast cancer cells.</p> <p>Conclusions</p> <p>This study shows an important role for the NF-kappaB pathway in retinoic acid signaling and retinoic acid-mediated resistance to cancer therapy-mediated apoptosis in breast cancer cells, independently of cIAP2. Our data support the use of NF-kappaB pathway activation as a marker for screening that will help to develop novel retinoids, or retinoid-based combination therapies with improved efficacy.</p

A comprehensive resource for retrieving, visualizing, and integrating functional genomics data

The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.\ua0\ua9 2019 Blum et al

Activation Function 2 in the Human Androgen Receptor Ligand Binding Domain Mediates Interdomain Communication with the NH 2 -terminal Domain

Activation function 2 in the ligand binding domain of nuclear receptors forms a hydrophobic cleft that binds the LXXLL motif of p160 transcriptional coactivators. Here we provide evidence that activation function 2 in the androgen receptor serves as the contact site for the androgen dependent NH(2)- and carboxyl-terminal interaction of the androgen receptor and only weakly interacts with p160 coactivators in an LXXLL-dependent manner. Mutagenesis studies indicate that it is the NH(2)-/carboxyl-terminal interaction that is required by activation function 2 to stabilize helix 12 and slow androgen dissociation critical for androgen receptor activity in vivo. The androgen receptor recruits p160 coactivators through its NH(2)-terminal and DNA binding domains in an LXXLL motif-independent manner. The results suggest a novel function for activation function 2 and a unique mechanism of nuclear receptor transactivation

Epigenetic profiling of the antitumor natural product psammaplin A and its analogues

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Neofunctionalization in Vertebrates: The Example of Retinoic Acid Receptors

Understanding the role of gene duplications in establishing vertebrate innovations is one of the main challenges of Evo-Devo (evolution of development) studies. Data on evolutionary changes in gene expression (i.e., evolution of transcription factor-cis-regulatory elements relationships) tell only part of the story; protein function, best studied by biochemical and functional assays, can also change. In this study, we have investigated how gene duplication has affected both the expression and the ligand-binding specificity of retinoic acid receptors (RARs), which play a major role in chordate embryonic development. Mammals have three paralogous RAR genes—RARα, β, and γ—which resulted from genome duplications at the origin of vertebrates. By using pharmacological ligands selective for specific paralogues, we have studied the ligand-binding capacities of RARs from diverse chordates species. We have found that RARβ-like binding selectivity is a synapomorphy of all chordate RARs, including a reconstructed synthetic RAR representing the receptor present in the ancestor of chordates. Moreover, comparison of expression patterns of the cephalochordate amphioxus and the vertebrates suggests that, of all the RARs, RARβ expression has remained most similar to that of the ancestral RAR. On the basis of these results together, we suggest that while RARβ kept the ancestral RAR role, RARα and RARγ diverged both in ligand-binding capacity and in expression patterns. We thus suggest that neofunctionalization occurred at both the expression and the functional levels to shape RAR roles during development in vertebrates

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution

BACKGROUND: Chromatin immunoprecipitation combined with massive parallel sequencing (ChIP-seq) is widely used to study protein-chromatin interactions or chromatin modifications at genome-wide level. Sequence reads that accumulate locally at the genome (peaks) reveal loci of selectively modified chromatin or specific sites of chromatin-binding factors. Computational approaches (peak callers) have been developed to identify the global pattern of these sites, most of which assess the deviation from background by applying distribution statistics. RESULTS: We have implemented MeDiChISeq, a regression-based approach, which--by following a learning process--defines a representative binding pattern from the investigated ChIP-seq dataset. Using this model MeDiChISeq identifies significant genome-wide patterns of chromatin-bound factors or chromatin modification. MeDiChISeq has been validated for various publicly available ChIP-seq datasets and extensively compared with other peak callers. CONCLUSIONS: MeDiChI-Seq has a high resolution when identifying binding events, a high degree of peak-assessment reproducibility in biological replicates, a low level of false calls and a high true discovery rate when evaluated in the context of gold-standard benchmark datasets. Importantly, this approach can be applied not only to 'sharp' binding patterns--like those retrieved for transcription factors (TFs)--but also to the broad binding patterns seen for several histone modifications. Notably, we show that at high sequencing depths, MeDiChISeq outperforms other algorithms due to its powerful peak shape recognition capacity which facilitates discerning significant binding events from spurious background enrichment patterns that are enhanced with increased sequencing depths

Nucleic Acids Res

The absence of a quality control (QC) system is a major weakness for the comparative analysis of genome-wide profiles generated by next-generation sequencing (NGS). This concerns particularly genome binding/occupancy profiling assays like chromatin immunoprecipitation (ChIP-seq) but also related enrichment-based studies like methylated DNA immunoprecipitation/methylated DNA binding domain sequencing, global run on sequencing or RNA-seq. Importantly, QC assessment may significantly improve multidimensional comparisons that have great promise for extracting information from combinatorial analyses of the global profiles established for chromatin modifications, the bindings of epigenetic and chromatin-modifying enzymes/machineries, RNA polymerases and transcription factors and total, nascent or ribosome-bound RNAs. Here we present an approach that associates global and local QC indicators to ChIP-seq data sets as well as to a variety of enrichment-based studies by NGS. This QC system was used to certify >5600 publicly available data sets, hosted in a database for data mining and comparative QC analyses

POLYPHEMUS: R package for comparative analysis of RNA polymerase II ChIP-seq profiles by non-linear normalization

Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP-seq) is increasingly used to map protein–chromatin interactions at global scale. The comparison of ChIP-seq profiles for RNA polymerase II (PolII) established in different biological contexts, such as specific developmental stages or specific time-points during cell differentiation, provides not only information about the presence/accumulation of PolII at transcription start sites (TSSs) but also about functional features of transcription, including PolII stalling, pausing and transcript elongation. However, annotation and normalization tools for comparative studies of multiple samples are currently missing. Here, we describe the R-package POLYPHEMUS, which integrates TSS annotation with PolII enrichment over TSSs and coding regions, and normalizes signal intensity profiles. Thereby POLYPHEMUS facilitates to extract information about global PolII action to reveal changes in the functional state of genes. We validated POLYPHEMUS using a kinetic study on retinoic acid-induced differentiation and a publicly available data set from a comparative PolII ChIP-seq profiling in Caenorhabditis elegans. We demonstrate that POLYPHEMUS corrects the data sets by normalizing for technical variation between samples and reveal the potential of the algorithm in comparing multiple data sets to infer features of transcription regulation from dynamic PolII binding profiles