Song exposure regulates known and novel microRNAs in the zebra finch auditory forebrain.

BACKGROUND: In an important model for neuroscience, songbirds learn to discriminate songs they hear during tape-recorded playbacks, as demonstrated by song-specific habituation of both behavioral and neurogenomic responses in the auditory forebrain. We hypothesized that microRNAs (miRNAs or miRs) may participate in the changing pattern of gene expression induced by song exposure. To test this, we used massively parallel Illumina sequencing to analyse small RNAs from auditory forebrain of adult zebra finches exposed to tape-recorded birdsong or silence. RESULTS: In the auditory forebrain, we identified 121 known miRNAs conserved in other vertebrates. We also identified 34 novel miRNAs that do not align to human or chicken genomes. Five conserved miRNAs showed significant and consistent changes in copy number after song exposure across three biological replications of the song-silence comparison, with two increasing (tgu-miR-25, tgu-miR-192) and three decreasing (tgu-miR-92, tgu-miR-124, tgu-miR-129-5p). We also detected a locus on the Z sex chromosome that produces three different novel miRNAs, with supporting evidence from Northern blot and TaqMan qPCR assays for differential expression in males and females and in response to song playbacks. One of these, tgu-miR-2954-3p, is predicted (by TargetScan) to regulate eight song-responsive mRNAs that all have functions in cellular proliferation and neuronal differentiation. CONCLUSIONS: The experience of hearing another bird singing alters the profile of miRNAs in the auditory forebrain of zebra finches. The response involves both known conserved miRNAs and novel miRNAs described so far only in the zebra finch, including a novel sex-linked, song-responsive miRNA. These results indicate that miRNAs are likely to contribute to the unique behavioural biology of learned song communication in songbirds.

Chromatin changes in dicer-deficient mouse embryonic stem cells in response to retinoic acid induced differentiation.

Loss of Dicer, an enzyme critical for microRNA biogenesis, results in lethality due to a block in mouse embryonic stem cell (mES) differentiation. Using ChIP-Seq we found increased H3K9me2 at over 900 CpG islands in the Dicer(-/-)ES epigenome. Gene ontology analysis revealed that promoters of chromatin regulators to be among the most impacted by increased CpG island H3K9me2 in ES (Dicer(-/-)). We therefore, extended the study to include H3K4me3 and H3K27me3 marks for selected genes. We found that the ES (Dicer(-/-)) mutant epigenome was characterized by a shift in the overall balance between transcriptionally favorable (H3K4me3) and unfavorable (H3K27me3) marks at key genes regulating ES cell differentiation. Pluripotency genes Oct4, Sox2 and Nanog were not impacted in relation to patterns of H3K27me3 and H3K4me3 and showed no changes in the rates of transcript down-regulation in response to RA. The most striking changes were observed in regards to genes regulating differentiation and the transition from self-renewal to differentiation. An increase in H3K4me3 at the promoter of Lin28b was associated with the down-regulation of this gene at a lower rate in Dicer(-/-)ES as compared to wild type ES. An increase in H3K27me3 in the promoters of differentiation genes Hoxa1 and Cdx2 in Dicer(-/-)ES cells was coincident with an inability to up-regulate these genes at the same rate as ES upon retinoic acid (RA)-induced differentiation. We found that siRNAs Ezh2 and post-transcriptional silencing of Ezh2 by let-7 g rescued this effect suggesting that Ezh2 up-regulation is in part responsible for increased H3K27me3 and decreased rates of up-regulation of differentiation genes in Dicer(-/-)ES.

Androgens promote prostate cancer cell growth through induction of autophagy.

Androgens regulate both the physiological development of the prostate and the pathology of prostatic diseases. However, the mechanisms by which androgens exert their regulatory activities on these processes are poorly understood. In this study, we have determined that androgens regulate overall cell metabolism and cell growth, in part, by increasing autophagy in prostate cancer cells. Importantly, inhibition of autophagy using either pharmacological or molecular inhibitors significantly abrogated androgen-induced prostate cancer cell growth. Mechanistically, androgen-mediated autophagy appears to promote cell growth by augmenting intracellular lipid accumulation, an effect previously demonstrated to be necessary for prostate cancer cell growth. Further, autophagy and subsequent cell growth is potentiated, in part, by androgen-mediated increases in reactive oxygen species. These findings demonstrate a role for increased fat metabolism and autophagy in prostatic neoplasias and highlight the potential of targeting underexplored metabolic pathways for the development of novel therapeutics.

Large-scale integration of MicroRNA and gene expression data for identification of enriched microRNA-mRNA associations in biological systems.

The discovery of microRNAs (miRNAs) revealed a hidden layer of gene regulation that is able to integrate multiple genes into biologically meaningful networks. A number of computational prediction programs have been developed to identify putative miRNA targets. Collectively, the miRNAs that have been discovered so far have the potential to target over 60% of genes in our genome. A minimum of six consecutive nucleotides in the 5'-seed (nucleotides 2-8) in the miRNA must bind through complimentary base pairing to the 3'-untranslated (3'-UTRs) of target genes. Given the small sequence match required, a given miRNA has the potential to target hundreds of genes and a given mRNA can have 0-50 miRNA binding sites. The low-throughput nature of the query design (gene by gene or miRNA by miRNA) and a fairly high rate of false positives and negatives uncovered by the limited number of functional studies remain as the major limitations. Programs that integrate genome-wide gene and miRNA expression data determined by microarray and/or next-generation sequencing (NGS) technologies with the publicly available target prediction algorithms are extremely valuable on two fronts. First, they allow the investigator to fully capitalize on all the data generated to reveal new genes and pathways underlying the biological process under study. Second, these programs allow the investigator to lift a small network of genes they are currently following into a larger network through the integrative properties of miRNAs. In this chapter, we discuss the latest methodologies for determining genome-wide miRNA and gene expression changes and three programs (Sigterms, CORNA, and MMIA) that allow the investigator to generate short lists of enriched miRNA:target mRNA candidates for large-scale miRNA:target mRNA validation. These efforts are essential for determining false positive and negative rates of existing algorithms and refining our knowledge on the rules of miRNA-mRNA relationships.