Genome-Wide Analysis of Small RNA and Novel MicroRNA Discovery in Human Acute Lymphoblastic Leukemia Based on Extensive Sequencing Approach

BACKGROUND:MicroRNAs (miRNAs) have been proved to play an important role in various cellular processes and function as tumor suppressors or oncogenes in cancers including leukemia. The identification of a large number of novel miRNAs and other small regulatory RNAs will provide valuable insights into the roles they play in tumorgenesis. METHODOLOGY/PRINCIPAL FINDINGS:To gain further understanding of the role of miRNAs relevant to acute lymphoblastic leukemia (ALL), we employed the sequencing-by-synthesis (SBS) strategy to sequence small RNA libraries prepared from ALL patients and normal donors. In total we identified 159 novel miRNAs and 116 novel miRNA*s from both libraries. Among the 159 novel miRNAs, 42 were identified with high stringency in our data set. Furthermore, we demonstrated the different expression patterns of 20 newly identified and several known miRNAs between ALL patients and normal donors, suggesting these miRNAs may be associated with ALL and could constitute an ALL-specific miRNA signature. Interestingly, GO "biological process" classifications revealed that a set of significantly abnormally expressed miRNAs are associated with disease relapse, which implies that these dysregulated miRNAs might promote the progression of ALL by regulating genes involved in the pathway of the disease development. CONCLUSION/SIGNIFICANCE:The study presents a comprehensive picture of the expression of small RNAs in human acute lymphoblastic leukemia and highlights novel and known miRNAs differentially expressed between ALL patients and normal donors. To our knowledge, this is the first study to look at genome-wide known and novel miRNA expression patterns in in human acute lymphoblastic leukemia. Our data revealed that these deregulated miRNAs may be associated with ALL or the onset of relapse

Disease-Associated Mutations That Alter the RNA Structural Ensemble

Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble

Accurate classification of RNA structures using topological fingerprints

While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC \u3e 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint

Caring for critically ill children Standards

Includes bibliographical references. Added t.p. in Welsh: Gofalu am blant difrifol wael; safonau. Parallel text in English and Welsh, printed tete-beche,Text in English WelshAvailable from British Library Document Supply Centre- DSC:m03/21287 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo