Effects of RAL signal transduction in KRAS- and BRAF-mutated cells and prognostic potential of the RAL signature in colorectal cancer

Our understanding of oncogenic signaling pathways has strongly fostered current concepts for targeted therapies in metastatic colorectal cancer. The RALA pathway is novel candidate due to its independent role in controlling expression of genes downstream of RAS. We compared RALA GTPase activities in three colorectal cancer cell lines by GTPase pull-down assay and analyzed the transcriptional and phenotypic effects of transient RALA silencing. Knocking-down RALA expression strongly diminished the active GTP-bound form of the protein. Proliferation of KRAS mutated cell lines was significantly reduced, while BRAF mutated cells were mostly unaffected. By microarray analysis we identified common genes showing altered expression upon RALA silencing in all cell lines. None of these genes were affected when the RAF/MAPK or PI3K pathways were blocked. To investigate the potential clinical relevance of the RALA pathway and its associated transcriptome, we performed a meta-analysis interrogating progression-free survival of colorectal cancer patients of five independent data sets using Cox regression. In each dataset, the RALA-responsive signature correlated with worse outcome. In summary, we uncovered the impact of the RAL signal transduction on genetic program and growth control in KRAS- and BRAF-mutated colorectal cells and demonstrated prognostic potential of the pathway-responsive gene signature in cancer patients

The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner

All cells and organisms exhibit stress-coping mechanisms to ensure survival. Cytoplasmic protein-RNA assemblies termed stress granules are increasingly recognized to promote cellular survival under stress. Thus, they might represent tumor vulnerabilities that are currently poorly explored. The translation-inhibitory eIF2α kinases are established as main drivers of stress granule assembly. Using a systems approach, we identify the translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regulator mammalian target of rapamycin complex 1 (mTORC1) to promote stress granule assembly. When highly active, PI3K is the main driver of stress granules; however, the impact of p38 becomes apparent as PI3K activity declines. PI3K and p38 thus act in a hierarchical manner to drive mTORC1 activity and stress granule assembly. Of note, this signaling hierarchy is also present in human breast cancer tissue. Importantly, only the recognition of the PI3K-p38 hierarchy under stress enabled the discovery of p38’s role in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as they hierarchically promote stress granule formation

Parallel sequencing of extrachromosomal circular DNAs and transcriptomes in single cancer cells

Extrachromosomal DNAs (ecDNAs) are common in cancer, but many questions about their origin, structural dynamics and impact on intratumor heterogeneity are still unresolved. Here we describe single-cell extrachromosomal circular DNA and transcriptome sequencing (scEC&T-seq), a method for parallel sequencing of circular DNAs and full-length mRNA from single cells. By applying scEC&T-seq to cancer cells, we describe intercellular differences in ecDNA content while investigating their structural heterogeneity and transcriptional impact. Oncogene-containing ecDNAs were clonally present in cancer cells and drove intercellular oncogene expression differences. In contrast, other small circular DNAs were exclusive to individual cells, indicating differences in their selection and propagation. Intercellular differences in ecDNA structure pointed to circular recombination as a mechanism of ecDNA evolution. These results demonstrate scEC&T-seq as an approach to systematically characterize both small and large circular DNA in cancer cells, which will facilitate the analysis of these DNA elements in cancer and beyond

3D-Druck von QR-Codes mit Formgedächtniseigenschaften

Die Additive Fertigung, auch bekannt als 3D-Druck, gewinnt aufgrund der schnellen Verfügbarkeit und der großen Gestaltungsvielfalt von Druckobjekten zunehmend an Bedeutung. Innerhalb der kommerziellen Additiven Fertigung von Bauteilen ist die Schmelzschichtung, in Englisch »Fused Filament Fabrication«, kurz FFF-Verfahren, am weitesten verbreitet. Wesentliche Vorteile liegen hier in einem hohen Maß an Materialeffizienz, in der schier unendlichen Zahl an Möglichkeiten zur Steuerung der Funktionalität über die Auswahl des verwendeten Werkstoffs sowie in der zielgerichteten Additivierung zum Aufbau spezifischer Eigenschaftsprofile. All dies eröffnet vielfältige Optionen in der Entwicklung innovativer Produkte. Der Einsatz Additiver Fertigungsverfahren zur Herstellung filigraner Objekte liegt zunehmend im Trend. Eine gute Auflösung additiv gefertigter Quick Response (QR)-Codes ist für ein fehlerfreies Lesen des Codes mithilfe eines Smartphones unerlässlich. Verwendet man Formgedächtnispolymere (FGPs) zur Herstellung des Codes und führt im Nachgang eine thermomechanische Behandlung durch, gelingt es, den Code in einer maschinell nicht lesbaren Form zu stabilisieren. Eine Temperaturerhöhung über die Schalttemperatur des FGP führt dann dazu, dass der QR-Code von nicht lesbar nach maschinell lesbar schaltet. Die Einsatzmöglichkeiten der Technologie sind vielfältig und umfassen die Kennzeichnung plagiatsgefährdeter Güter zur Bekämpfung von Produktpiraterie und die Kennzeichnung tiefgekühlter Waren zum Einhalten von (Tief-)Kühlketten

Long non-coding RNAs differentially expressed between normal versus primary breast tumor tissues disclose converse changes to breast cancer-related protein-coding genes

Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes

Cell cycle, oncogenic and tumor suppressor pathways regulate numerous long and macro non-protein-coding RNAs

Background: The genome is pervasively transcribed but most transcripts do not code for proteins, constituting non-protein-coding RNAs. Despite increasing numbers of functional reports of individual long non-coding RNAs (lncRNAs), assessing the extent of functionality among the non-coding transcriptional output of mammalian cells remains intricate. In the protein-coding world, transcripts differentially expressed in the context of processes essential for the survival of multicellular organisms have been instrumental in the discovery of functionally relevant proteins and their deregulation is frequently associated with diseases. We therefore systematically identified lncRNAs expressed differentially in response to oncologically relevant processes and cell-cycle, p53 and STAT3 pathways, using tiling arrays. Results: We found that up to 80% of the pathway-triggered transcriptional responses are non-coding. Among these we identified very large macroRNAs with pathway-specific expression patterns and demonstrated that these are likely continuous transcripts. MacroRNAs contain elements conserved in mammals and sauropsids, which in part exhibit conserved RNA secondary structure. Comparing evolutionary rates of a macroRNA to adjacent protein-coding genes suggests a local action of the transcript. Finally, in different grades of astrocytoma, a tumor disease unrelated to the initially used cell lines, macroRNAs are differentially expressed. Conclusions: It has been shown previously that the majority of expressed non-ribosomal transcripts are non-coding. We now conclude that differential expression triggered by signaling pathways gives rise to a similar abundance of non-coding content. It is thus unlikely that the prevalence of non-coding transcripts in the cell is a trivial consequence of leaky or random transcription events

Proximal lncRNA – mRNA pairs.

<p>For non-coding DE- probes significantly differentially expressed between normal and tumor samples (FDR) the protein-coding gene (Gencode release v12) with closest genome coordinates was identified, and the pair retained if the protein-coding gene was differentially expressed at the same FDR cutoff. Log2 fold change of the non-coding probe (<i>x</i>-axis) and the maximal log2 fold change of probes located in exons of the protein-coding gene (<i>y</i>-axis) is depicted as a bivariate histogram using hexagonal binning (R package hexbin). Pairs with converse fold changes are shown in the left upper and right lower quadrant. Pairs with consistent fold changes but opposite reading direction are shown in the left lower and right upper quadrant (see also panel describing direction of expression changes for each quadrant). Numbers in quadrant correspond to number of unique genes depicted. (<b>A.</b>) Proximal pairs, where the non-coding probe is intergenic. (<b>B.</b>) Pairs where the non-coding probes is in an intron of the protein-coding gene. (<b>C.</b>) Pairs where the non-coding probe and the protein-coding gene are on opposite strands and overlap at least partially.</p

DE-probe overlap with genomic annotation (Basal-like versus Luminal A and B tumors).

<p><b>A.–D.</b>: Number of DE-probes significantly differentially expressed between Basal-like and Luminal A and B tumors () and mapping to different genomic annotations. Log2 transformed odds ratios and their 95% confidence interval for the respective annotation dataset are shown. Odds ratios of observed versus expected probe overlaps were calculated and tested by Fisher's exact test for significant enrichment or depletion, with *** indicating , ** , and * , respectively. Missing error bars denote no DE-probes overlapped with according annotation. Results are shown (<b>A.</b>) for DE-probes located in annotated protein coding genes versus intergenic space based on Gencode release v12, (<b>B.–D.</b>) for intergenic or intronic non-coding DE-probes either located in several classes of known and predicted ncRNAs (B.), in non-coding transcripts regulated during cell cycle (CC), upon TP53 or Stat3 induction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Hackermller1" target="_blank">[43]</a> (C.), or in regulatory sites (D.). (<b>E.</b>) Fraction of unique non-coding DE-loci in exons of known short and long ncRNAs, in genomic sites with conserved secondary structures, in antisense-direction to known non-coding exons (Gencode v12), or in novel sites. Numbers denote absolute number of DE-loci located in novel sites. For detailed output of Fisher's exact tests see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076.s011" target="_blank">Table S4</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076.s014" target="_blank">Table S7</a> for detailed description of annotation datasets.</p

Differential expression analysis.

<p>The expression patterns of mRNA-probes and non-coding probes of 26 breast tumors and 5 normal breast tissues were investigated using the custom microarray. (<b>A.</b>) Fraction of unique genomic loci significantly differentially expressed () between normal and tumor samples located completely in exons of protein-coding genes (Gencode v12), in exons of known lncRNAs (lincRNAs, Gencode v12 lncRNAs, lncRNAs as annotated in lncRNAdb <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Amaral1" target="_blank">[51]</a>, and lncRNAs contained in chromatin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Mondal1" target="_blank">[27]</a>), in exons of transcripts of uncertain coding potential (TUCPs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Cabili1" target="_blank">[23]</a>), in exons of short RNAs (UCSC sno/miRNA track), in genomic loci with conserved secondary structure motifs (Evofold <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Pedersen1" target="_blank">[59]</a>, RNAz <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Gruber1" target="_blank">[58]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Washietl3" target="_blank">[97]</a> and SISSIz <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Gesell1" target="_blank">[53]</a>), in antisense-direction to known exons (Gencode v12), or in novel genomic regions. (<b>B.</b>) Fraction of unique genomic loci significantly differentially expressed () between Basal-like and Luminal tumors and located in genomic annotations as described for panel A. Numbers beside bars denote absolute number of unique DE-loci.</p

HDAC3 (histone deacetylase 3) mRNA and its putative regulatory antisense lncRNA.

<p>(<b>A.</b>) Genomic locus of HDAC3 on chromosome 5 and the antisense transcript downstream of HDAC3 with genomic positions of strand-specific RT-qPCR primers/products. Annotation track DE-TAR corresponds to genomic loci significantly downregulated upon TP53 induction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Hackermller1" target="_blank">[43]</a>. Both transcripts appear to be significantly differentially expressed on the custom microarray (), exhibiting a non-synonymous expression pattern (<b>B.</b>). The transcription start site of the annotated antisense RNA overlaps with the transcription start site of DIAPH1. Genome-wide predictions of functional open reading frames (RNAcode, ) correspond mainly to exons of HDAC3 mRNA, while some short putative open reading frames overlap the antisense transcript. (<b>C.</b>) Strand-specific RT-qPCR validations relative to normal sample “RP38” for both, the HDAC3 mRNA and the antisense transcript.</p