In a multicenter study, we determined the expression profiles of 863 microRNAs by array analysis of 454 blood samples from human individuals with different cancers or noncancer diseases, and validated this 'miRNome' by quantitative real-time PCR. We detected consistently deregulated profiles for all tested diseases; pathway analysis confirmed disease association of the respective microRNAs. We observed significant correlations (P = 0.004) between the genomic location of disease-associated genetic variants and deregulated microRNAs.

BackgroundMicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool.MethodsUsing a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set.ResultsA hypothesis test based approch detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR.ConclusionsOur study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma.

RNA molecules play important and diverse regulatory roles in the cell. Inspired by this natural versatility, RNA devices are increasingly important for many synthetic biology applications, e.g. optimizing engineered metabolic pathways, gene therapeutics or building up complex logical units. A major advantage of RNA is the possibility of de novo design of RNA-based sensing domains via an in vitro selection process (SELEX). Here, we describe development of a novel ciprofloxacin-responsive riboswitch by in vitro selection and next-generation sequencing-guided cellular screening. The riboswitch recognizes the small molecule drug ciprofloxacin with a KD in the low nanomolar range and adopts a pseudoknot fold stabilized by ligand binding. It efficiently interferes with gene expression both in lower and higher eukaryotes. By controlling an auxotrophy marker and a resistance gene, respectively, we demonstrate efficient, scalable and programmable control of cellular survival in yeast. The applied strategy for the development of the ciprofloxacin riboswitch is easily transferrable to any small molecule target of choice and will thus broaden the spectrum of RNA regulators considerably.

Riboswitch development for clinical, technological, and synthetic biology applications constantly seeks to optimize regulatory behavior. Here, we present a machine learning approach to improve the regulation of a tetracycline (tc)dependent riboswitch device composed of two individual tc aptamers. We developed a bioinformatics model that combines random forest analysis with a convolutional neural network to predict the switching behavior of such tandem riboswitches. We found that both biophysical parameters and the hydrogen bond pattern influence regulation. Our new design pipeline led to significant improvement of the tc riboswitch device with a dynamic range extension from 8.5 to 40-fold. We are confident that our novel method not only results in an excellent tc-dependent riboswitch device but further holds great promise and potential for the optimization of other riboswitches.

ω-Transaminases (ω-TAs) are important biocatalysts for the synthesis of active, chiral pharmaceutical ingredients containing amino groups, such as β-amino acids, which are important in peptidomimetics and as building blocks for drugs. However, the application of ω-TAs is limited by the availability and stability of enzymes with high conversion rates. One strategy for the synthesis and optical resolution of β-phenylalanine and other important aromatic β-amino acids is biotransformation by utilizing an ω-transaminase from Variovorax paradoxus. We designed variants of this ω-TA to gain higher process stability on the basis of predictions calculated by using the FoldX software. We herein report the first thermostabilization of a nonthermostable S-selective ω-TA by FoldX-guided site-directed mutagenesis. The melting point (T ) of our best-performing mutant was increased to 59.3 °C, an increase of 4.0 °C relative to the T value of the wild-type enzyme, whereas the mutant fully retained its specific activity.