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Institute
Mechanisms underlying therapy resistance of tumor cells include protein kinase Akt. Putative Akt targets include store-operated \(Ca^{2+}\)-entry (SOCE) accomplished by pore forming ion channel unit Orai1 and its regulator STIM1. We explored whether therapy resistant (A2780cis) differ from therapy sensitive (A2780) ovary carcinoma cells in Akt, Orai1, and STIM1 expression, \(Ca^{2+}\)-signaling and cell survival following cisplatin (100µM) treatment. Transcript levels were quantified with RT-PCR, protein abundance with Western blotting, cytosolic \(Ca^{2+}\)-activity ([\(Ca^{2+}\)]i) with Fura-2-fluorescence, SOCE from increase of [\(Ca^{2+}\)]i following \(Ca^{2+}\)-readdition after Ca2+-store depletion, and apoptosis utilizing flow cytometry. Transcript levels of Orai1 and STIM1, protein expression of Orai1, STIM1, and phosphorylated Akt, as well as SOCE were significantly higher in A2780cis than A2780 cells. SOCE was decreased by Akt inhibitor III (SH-6, 10µM) in A2780cis but not A2780 cells and decreased in both cell lines by Orai1 inhibitor 2-aminoethoxydiphenyl borate (2-ABP, 50µM). Phosphatidylserine exposure and late apoptosis following cisplatin treatment were significantly lower in A2780cis than A2780 cells, a difference virtually abolished by SH-6 or 2-ABP. In conclusion, Orai1/STIM1 expression and function are increased in therapy resistant ovary carcinoma cells, a property at least in part due to enhanced Akt activity and contributing to therapy resistance in those cells.
Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.