ABSTRACT
RBM46 is a germ cell-specific RNA-binding protein required for gametogenesis, but the targets and molecular functions of RBM46 remain unknown. Here, we demonstrate that RBM46 binds at specific motifs in the 3'UTRs of mRNAs encoding multiple meiotic cohesin subunits and show that RBM46 is required for normal synaptonemal complex formation during meiosis initiation. Using a recently reported, high-resolution technique known as LACE-seq and working with low-input cells, we profiled the targets of RBM46 at single-nucleotide resolution in leptotene and zygotene stage gametes. We found that RBM46 preferentially binds target mRNAs containing GCCUAU/GUUCGA motifs in their 3'UTRs regions. In Rbm46 knockout mice, the RBM46-target cohesin subunits displayed unaltered mRNA levels but had reduced translation, resulting in the failed assembly of axial elements, synapsis disruption, and meiotic arrest. Our study thus provides mechanistic insights into the molecular functions of RBM46 in gametogenesis and illustrates the power of LACE-seq for investigations of RNA-binding protein functions when working with low-abundance input materials.
Subject(s)
Animals , Mice , 3' Untranslated Regions/genetics , Cell Cycle Proteins/metabolism , Gametogenesis/genetics , Meiosis/genetics , Nuclear Proteins/genetics , RNA-Binding Proteins/geneticsABSTRACT
@#In central venous obstruction, vertebral marrow enhancement (VME) may be seen secondary to collateral venous flow via the vertebral venous plexus. 1 There are only sporadic case reports on pseudolesions due to collateral enhancement mimicking sclerotic osseous metastasis. This abnormal vertebral enhancement may lead to erroneous diagnosis of sclerotic metastases or suspicious bone lesion which affect the management and prognosis. We describe a case of brachiocephalic vein obstruction-related vertebral body pseudolesions as identified in contrast-enhanced computed tomography (CECT) scan
ABSTRACT
Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.