Molecular Classification of Colorectal Cancer by microRNA Profiling: Correlation with the Consensus Molecular Subtypes (CMS) and Validation of miR-30b Targets.

Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p < 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p < 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3'-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3'-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response.

Transcriptome-wide analysis links the short-term expression of the b isoforms of TIA proteins to protective proteostasis-mediated cell quiescence response.

Control of gene expression depends on genetics and environmental factors. The T-cell intracellular antigens T-cell intracellular antigen 1 (TIA1), TIA1-like/related protein (TIAL1/TIAR) and human antigen R (HuR/ELAVL1) are RNA-binding proteins that play crucial roles in regulating gene expression in both situations. This study used massive sequencing analysis to uncover molecular and functional mechanisms resulting from the short-time expression of the b isoforms of TIA1 and TIAR, and of HuR in HEK293 cells. Our gene profiling analysis identified several hundred differentially expressed genes (DEGs) and tens of alternative splicing events associated with TIA1b, TIARb and HuR overexpression. Gene ontology analysis revealed that the controlled expression of these proteins strongly influences the patterns of DEGs and RNA variants preferentially associated with development, reproduction, cell cycle, metabolism, autophagy and apoptosis. Mechanistically, TIA1b and TIARb isoforms display both common and differential effects on the regulation of gene expression, involving systematic perturbations of cell biosynthetic machineries (splicing and translation). The transcriptome outputs were validated using functional assays of the targeted cellular processes as well as expression analysis for selected genes. Collectively, our observations suggest that early TIA1b and TIARb expression operates to connect the regulatory crossroads to protective proteostasis responses associated with a survival quiescence phenotype.

3DBIONOTES v2.0: a web server for the automatic annotation of macromolecular structures.

MOTIVATION: Complementing structural information with biochemical and biomedical annotations is a powerful approach to explore the biological function of macromolecular complexes. However, currently the compilation of annotations and structural data is a feature only available for those structures that have been released as entries to the Protein Data Bank. RESULTS: To help researchers in assessing the consistency between structures and biological annotations for structural models not deposited in databases, we present 3DBIONOTES v2.0, a web application designed for the automatic annotation of biochemical and biomedical information onto macromolecular structural models determined by any experimental or computational technique. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://3dbionotes-ws.cnb.csic.es. CONTACT: jsegura@cnb.csic.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct Molecular Signature of Murine Fetal Liver and Adult Hematopoietic Stem Cells Identify Novel Regulators of Hematopoietic Stem Cell Function.

During ontogeny, fetal liver (FL) acts as a major site for hematopoietic stem cell (HSC) maturation and expansion, whereas HSCs in the adult bone marrow (ABM) are largely quiescent. HSCs in the FL possess faster repopulation capacity as compared with ABM HSCs. However, the molecular mechanism regulating the greater self-renewal potential of FL HSCs has not yet extensively been assessed. Recently, we published RNA sequencing-based gene expression analysis on FL HSCs from 14.5-day mouse embryo (E14.5) in comparison to the ABM HSCs. We reanalyzed these data to identify key transcriptional regulators that play important roles in the expansion of HSCs during development. The comparison of FL E14.5 with ABM HSCs identified more than 1,400 differentially expressed genes. More than 200 genes were shortlisted based on the gene ontology (GO) annotation term "transcription." By morpholino-based knockdown studies in zebrafish, we assessed the function of 18 of these regulators, previously not associated with HSC proliferation. Our studies identified a previously unknown role for tdg, uhrf1, uchl5, and ncoa1 in the emergence of definitive hematopoiesis in zebrafish. In conclusion, we demonstrate that identification of genes involved in transcriptional regulation differentially expressed between expanding FL HSCs and quiescent ABM HSCs, uncovers novel regulators of HSC function.

NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning.

Drug repositioning, using known drugs for treating conditions different from those the drug was originally designed to treat, is an important drug discovery tool that allows for a faster and cheaper development process by using drugs that are already approved or in an advanced trial stage for another purpose. This is especially relevant for orphan diseases because they affect too few people to make drug research de novo economically viable. In this paper we present NFFinder, a bioinformatics tool for identifying potential useful drugs in the context of orphan diseases. NFFinder uses transcriptomic data to find relationships between drugs, diseases and a phenotype of interest, as well as identifying experts having published on that domain. The application shows in a dashboard a series of graphics and tables designed to help researchers formulate repositioning hypotheses and identify potential biological relationships between drugs and diseases. NFFinder is freely available at http://nffinder.cnb.csic.es.

NMF-mGPU: non-negative matrix factorization on multi-GPU systems.

BACKGROUND: In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU. RESULTS: NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup). CONCLUSIONS: Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used "out of the box" by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu .