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

Development of brain infarct volume as assessed by magnetic resonance imaging (MRI): follow-up of diffusion-weighted MRI lesions

To investigate the development of ischemic brain lesions, as present in the acute stroke phase, by diffusion-weighted magnetic resonance imaging (DWI), and in the subacute and chronic phases until up to four months after stroke, in fluid-attenuated inversion recovery (FLAIR)- and T2-weighted (T2W) magnetic resonance (MR) images.Twelve consecutive patients with their first middle cerebral artery (MCA) infarction were included. Lesion volumes were assessed on T2W images recorded with a turbo spin echo (TSE) and on images recorded with the FLAIR sequence on average on day 8 and after about four months. They were compared with acute lesion volumes in perfusion and DWI images taken within 24 hours of stroke onset.On day 8, lesion volumes in images obtained with FLAIR exceeded the acute infarct volumes in DWI. The chronic lesion volumes were almost identical in T2W and FLAIR images but significantly reduced compared with the acute DWI lesions. The lesion volumes assessed on DWI images correlated highly with the lesions in the images obtained with TSE or FLAIR, as did the lesions in the images obtained with FLAIR and TSE. The secondary lesion shrinkage was accompanied by ventricular enlargement and perilesional sulcal widening, as most clearly visible in the images obtained with FLAIR.Our results show that the acute DWI lesions are highly predictive for the infarct lesion in the chronic stage after stroke despite a dynamic lesion evolution most evident in MR images obtained with FLAIR