Cyclin D1 promotes secretion of pro-oncogenic immuno-miRNAs and piRNAs.

The molecular mechanisms governing the secretion of the non-coding genome are poorly understood. We show herein that cyclin D1, the regulatory subunit of the cyclin-dependent kinase that drives cell-cycle progression, governs the secretion and relative proportion of secreted non-coding RNA subtypes (miRNA, rRNA, tRNA, CDBox, scRNA, HAcaBox. scaRNA, piRNA) in human breast cancer. Cyclin D1 induced the secretion of miRNA governing the tumor immune response and oncogenic miRNAs. miR-21 and miR-93, which bind Toll-Like Receptor 8 to trigger a pro-metastatic inflammatory response, represented >85% of the cyclin D1-induced secreted miRNA transcripts. Furthermore, cyclin D1 regulated secretion of the P-element Induced WImpy testis (PIWI)-interacting RNAs (piRNAs) including piR-016658 and piR-016975 that governed stem cell expansion, and increased the abundance of the PIWI member of the Argonaute family, piwil2 in ERα positive breast cancer. The cyclin D1-mediated secretion of pro-tumorigenic immuno-miRs and piRNAs may contribute to tumor initiation and progression.

The complex transcriptional landscape of the anucleate human platelet.

BACKGROUND: Human blood platelets are essential to maintaining normal hemostasis, and platelet dysfunction often causes bleeding or thrombosis. Estimates of genome-wide platelet RNA expression using microarrays have provided insights to the platelet transcriptome but were limited by the number of known transcripts. The goal of this effort was to deep-sequence RNA from leukocyte-depleted platelets to capture the complex profile of all expressed transcripts. RESULTS: From each of four healthy individuals we generated long RNA (≥40 nucleotides) profiles from total and ribosomal-RNA depleted RNA preparations, as well as short RNA (<40 nucleotides) profiles. Analysis of ~1 billion reads revealed that coding and non-coding platelet transcripts span a very wide dynamic range (≥16 PCR cycles beyond ß-actin), a result we validated through qRT-PCR on many dozens of platelet messenger RNAs. Surprisingly, ribosomal-RNA depletion significantly and adversely affected estimates of the relative abundance of transcripts. Of the known protein-coding loci, ~9,500 are present in human platelets. We observed a strong correlation between mRNAs identified by RNA-seq and microarray for well-expressed mRNAs, but RNASeq identified many more transcripts of lower abundance and permitted discovery of novel transcripts. CONCLUSIONS: Our analyses revealed diverse classes of non-coding RNAs, including: pervasive antisense transcripts to protein-coding loci; numerous, previously unreported and abundant microRNAs; retrotransposons; and thousands of novel un-annotated long and short intronic transcripts, an intriguing finding considering the anucleate nature of platelets. The data are available through a local mirror of the UCSC genome browser and can be accessed at: http://cm.jefferson.edu/platelets_2012/.

Predicting genome-wide redundancy using machine learning.

BACKGROUND: Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. RESULTS: Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods. CONCLUSIONS: Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

Molecular dynamics simulations of Ago silencing complexes reveal a large repertoire of admissible 'seed-less' targets.

To better understand the recognition mechanism of RISC and the repertoire of guide-target interactions we introduced G:U wobbles and mismatches at various positions of the microRNA (miRNA) 'seed' region and performed all-atom molecular dynamics simulations of the resulting Ago-miRNA:mRNA ternary complexes. Our simulations reveal that many modifications, including combinations of multiple G:U wobbles and mismatches in the seed region, are admissible and result in only minor structural fluctuations that do not affect overall complex stability. These results are further supported by analyses of HITS-CLIP data. Lastly, introduction of disruptive mutations revealed a bending motion of the PAZ domain along the L1/L2 'hinge' and a subsequent opening of the nucleic-acid-binding channel. Our findings suggest that the spectrum of a miRNA's admissible targets is different from what is currently anticipated by the canonical seed-model. Moreover, they provide a likely explanation for the previously reported sequence-dependent regulation of unintended targeting by siRNAs.