Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies. © 2012 Coronnello et al

Quantification of miRNA-mRNA Interactions

miRNAs are small RNA molecules (′ 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO)

Determination of mercury in estuarine sediments by flow injection-cold vapour atomic absorption spectrometry after microwave extraction

A flow injection-cold vapour atomic absorption spectrometric (CVAAS) method was developed for the determination of mercury at trace level in estuarine sediments using sodium tetrahydroborate (III) as reductant. The mercury was solubilized with nitric acid in closed vessels and microwave oven heating. Instrumental and operational conditions (volume and concentration of reagents, reaction time, etc.) were optimized. The effect of several ions on the analytical signal was also studied; no interferences were recorded excepting for copper and nickel which caused a serious depressing effect. The detection limit obtained was 0.01 $\mu$g g$^{-1}$. The validation of the method was performed analyzing a certified reference sediment, BCR CRM 277 Estuarine Sediment. Good recovery (c.a. 98%) and precision ($< 3\%$, RSD) were achieved. The proposed method was successfully applied to the determination of mercury in sediment samples from Ares-Betanzos Estuary (Galicia, NW Spain)

Modeling of inner filter effect in synchronous spectrofluorimetry by using partial least squares

This paper deals with a very common problem on spectrofluorimetry: the inner-filter effect. This limitation has made spectrofluorimetry into a disadvantageous technique for the quantification of individual compounds in complex mixtures, however, a lot of papers have quantified compounds as fluorene in mixtures where other Polycyclic Aromatic Hydrocarbons (PAHs) absorb part of the energy emitted by fluorene without taking account this limitation. The inner-effect filter for fluorene is easily detectable in spectrofluorimetric measurements in mixtures where there are compounds, such as benzo(k)fluoranthene, indeno(1,2,3-cd)pyrene and pyrene. The application of methods, such as Multiple Linear Regression (MLR) to the quantification of fluorene in mixtures containing compounds capable to quench its signal, provides high errors in the analytical results. This point is carefully treated in our paper. The precise and accurate quantification of fluorene in presence of benzo(k)fluoranthene, indene(1,2,3-cd)pyrene and pyrene, was achieved by constant-wavelength synchronous fluorimetry in combination with Partial Least Squares (PLS) calibration