Identification of novel non-coding RNAs using profiles of short sequence reads from next generation sequencing data

<p>Abstract</p> <p>Background</p> <p>The increasing interest in small non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), small interfering RNAs (siRNAs) and Piwi-interacting RNAs (piRNAs) and recent advances in sequencing technology have yielded large numbers of short (18-32 nt) RNA sequences from different organisms, some of which are derived from small nucleolar RNAs (snoRNAs) and transfer RNAs (tRNAs). We observed that these short ncRNAs frequently cover the entire length of annotated snoRNAs or tRNAs, which suggests that other loci specifying similar ncRNAs can be identified by clusters of short RNA sequences.</p> <p>Results</p> <p>We combined publicly available datasets of tens of millions of short RNA sequence tags from <it>Drosophila melanogaster</it>, and mapped them to the <it>Drosophila </it>genome. Approximately 6 million perfectly mapping sequence tags were then assembled into 521,302 tag-contigs (TCs) based on tag overlap. Most transposon-derived sequences, exons and annotated miRNAs, tRNAs and snoRNAs are detected by TCs, which show distinct patterns of length and tag-depth for different categories. The typical length and tag-depth of snoRNA-derived TCs was used to predict 7 previously unrecognized box H/ACA and 26 box C/D snoRNA candidates. We also identified one snRNA candidate and 86 loci with a high number of tags that are yet to be annotated, 7 of which have a particular 18mer motif and are located in introns of genes involved in development. A subset of new snoRNA candidates and putative ncRNA candidates was verified by Northern blot.</p> <p>Conclusions</p> <p>In this study, we have introduced a new approach to identify new members of known classes of ncRNAs based on the features of TCs corresponding to known ncRNAs. A large number of the identified TCs are yet to be examined experimentally suggesting that many more novel ncRNAs remain to be discovered.</p

Differential Gene Expression Profiling of Orbital Adipose Tissue in Thyroid Orbitopathy

Published under a Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)PURPOSE. We aimed to determine differentially expressed genes relevant to orbital inflammation and orbital fat expansion in thyroid orbitopathy (TO) using microarray gene profiling in a case-control study. METHODS. Human orbital adipose samples were obtained from individuals with active TO (n ¼ 12), inactive TO (n ¼ 21), and normal controls (n ¼ 21). Gene expression profiles were examined using microarray analysis and were compared between active and inactive TO, and between active TO and normal controls. Top ranked differentially expressed genes were validated by real-time RT-PCR in an additional eight active TO, 13 inactive TO, and 11 normal controls and correlated with gene set enrichment analysis (GSEA) and molecular pathways analysis. RESULTS. Seven hundred twenty-one probes (683 genes) and 806 probes (735 genes) were significantly differentially expressed in comparing active to inactive TO and in comparing active TO to healthy controls, respectively. All selected genes were confirmed to be differentially expressed by real-time RT-PCR. Multiple top ranked genes in active versus inactive TO comparison are overrepresented by immune and inflammatory response genes. They include defensins (DEFA1, DEFA1B, DEFA3), which were overexpressed by 3.05- to 4.14-fold and TIMD4 by 4.20-fold. Markers for adipogenesis were overexpressed including SCD, FADS1, and SCDP1. Gene set enrichment analysis revealed dysregulation of epigenetic signatures, T-cell activation, Th1 differentiation, defensin pathway, cell adhesion, cytoskeleton organization, apoptosis, cell cycling, and lipid metabolism in active TO. CONCLUSIONS. Active TO is characterized by upregulation of genes involved in cell-mediated immune, innate immune, and inflammatory response and enhanced orbital adipogenesis. TIMD4, DEFA1, DEFA1B, and DEFA3 genes may be involved in the innate immune-mediated orbital inflammation in TO. Epigenetic mechanisms may play a role in the pathogenesis of TO

The First Very Long Baseline Interferometry Image of 44 GHz Methanol Maser with the KVN and VERA Array (KaVA)

We have carried out the first very long baseline interferometry (VLBI) imaging of 44 GHz class I methanol maser (7_{0}-6_{1}A^{+}) associated with a millimeter core MM2 in a massive star-forming region IRAS 18151-1208 with KaVA (KVN and VERA Array), which is a newly combined array of KVN (Korean VLBI Network) and VERA (VLBI Exploration of Radio Astrometry). We have succeeded in imaging compact maser features with a synthesized beam size of 2.7 milliarcseconds x 1.5 milliarcseconds (mas). These features are detected at a limited number of baselines within the length of shorter than approximately 650 km corresponding to 100 Mlambda in the uv-coverage. The central velocity and the velocity width of the 44 GHz methanol maser are consistent with those of the quiescent gas rather than the outflow traced by the SiO thermal line. The minimum component size among the maser features is ~ 5 mas x 2 mas, which corresponds to the linear size of ~ 15 AU x 6 AU assuming a distance of 3 kpc. The brightness temperatures of these features range from ~ 3.5 x 10^{8} to 1.0 x 10^{10} K, which are higher than estimated lower limit from a previous Very Large Array observation with the highest spatial resolution of ~ 50 mas. The 44 GHz class I methanol maser in IRAS 18151-1208 is found to be associated with the MM2 core, which is thought to be less evolved than another millimeter core MM1 associated with the 6.7 GHz class II methanol maser.Comment: 19 pages, 3 figure

The genome sequence of Xanthomonas oryzae pathovar oryzae KACC10331, the bacterial blight pathogen of rice

The nucleotide sequence was determined for the genome of Xanthomonas oryzae pathovar oryzae (Xoo) KACC10331, a bacterium that causes bacterial blight in rice (Oryza sativa L.). The genome is comprised of a single, 4 941 439 bp, circular chromosome that is G + C rich (63.7%). The genome includes 4637 open reading frames (ORFs) of which 3340 (72.0%) could be assigned putative function. Orthologs for 80% of the predicted Xoo genes were found in the previously reported X.axonopodis pv. citri (Xac) and X.campestris pv. campestris (Xcc) genomes, but 245 genes apparently specific to Xoo were identified. Xoo genes likely to be associated with pathogenesis include eight with similarity to Xanthomonas avirulence (avr) genes, a set of hypersensitive reaction and pathogenicity (hrp) genes, genes for exopolysaccharide production, and genes encoding extracellular plant cell wall-degrading enzymes. The presence of these genes provides insights into the interactions of this pathogen with its gramineous host

Ultra-sensitive detection of circulating tumour DNA enriches for patients with greater risk of recurrence in clinically localised prostate cancer

Funding: C.E.M. and H.D. were supported by the Cancer Research UK Cambridge Centre, John Black Charitable Foundation and Prostate Cancer Foundation. H.D. and V.J.G. acknowledge infrastructure support from the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215- 20014).Peer reviewe

International Journal of Cancer / DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled =4 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled =2 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled =7 10-16 and 1 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled =2 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled =5 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.(VLID)222024

Genomic evolution shapes prostate cancer disease type

H.R.F. was supported by a Cancer Research UK Programme Grant to Simon Tavaré (C14303/A17197), as, partially, was A.G.L. A.G.L. acknowledges the support of the University of St Andrews. A.G.L. and J.H.R.F. also acknowledge the support of the Cambridge Cancer Research Fund.The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Aalternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.Peer reviewe