RNA interference in adult mice

RNA interference is an evolutionarily conserved surveillance mechanism that responds to double-stranded RNA by sequence-specific silencing of homologous genes. Here we show that transgene expression can be suppressed in adult mice by synthetic small interfering RNAs and by small-hairpin RNAs transcribed in vivo from DNA templates. We also show the therapeutic potential of this technique by demonstrating effective targeting of a sequence from hepatitis C virus by RNA interference in vivo.     

Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA

Two small RNAs regulate the timing of Caenorhabditis elegans development. Transition from the first to the second larval stage fates requires the 22-nucleotide lin-4 RNA, and transition from late larval to adult cell fates requires the 21-nucleotide let-7 RNA. The lin-4 and let-7 RNA genes are not homologous to each other, but are each complementary to sequences in the 3' untranslated regions of a set of protein-coding target genes that are normally negatively regulated by the RNAs. Here we have detected let-7 RNAs of approximately 21 nucleotides in samples from a wide range of animal species, including vertebrate, ascidian, hemichordate, mollusc, annelid and arthropod, but not in RNAs from several cnidarian and poriferan species, Saccharomyces cerevisiae, Escherichia coli or Arabidopsis. We did not detect lin-4 RNA in these species. We found that let-7 temporal regulation is also conserved: let-7 RNA expression is first detected at late larval stages in C. elegans and Drosophila, at 48 hours after fertilization in zebrafish, and in adult stages of annelids and molluscs. The let-7 regulatory RNA may control late temporal transitions during development across animal phylogeny.     

Identifying Micro RNA and Gene Expression Networks Using Graph Communities

Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies.Although it becomes clear that micro RNAs,one type of epigenetic factors,have direct effect on target genes,it is unclear how micro RNAs perturb downstream genetic neighborhood.Hence,we propose a network community approach to integrate micro RNA and gene expression profiles,to construct an integrative genetic network perturbed by micro RNAs.We apply this approach to an ovarian cancer dataset from The Cancer Genome Atlas project to identify the fluctuation of micro RNA expression and its effects on gene expression.First,we perform expression quantitative loci analysis between micro RNA and gene expression profiles via both a classical regression framework and a sparse learning model.Then,we apply the spin glass community detection algorithm to find genetic neighborhoods of the micro RNAs and their associated genes.Finally,we construct an integrated network between micro RNA and gene expression based on their community structure.Various disease related micro RNAs and genes,particularly related to ovarian cancer,are identified in this network.Such an integrative network allows us to investigate the genetic neighborhood affected by micro RNA expression that may lead to disease manifestation and progression.