1. The expanding universe of microRNAs
MicroRNAs (miRNAs) are short RNA molecules encoded by plant and animal genomes that have garnered significant interest for their ability to regulate gene expression. A number of miRNAs have been discovered in recent years, however it is likely that many miRNAs have gone undetected. Two papers published in Genome Research utilize the twelve fly genomes to identify novel miRNAs, further refine the set of known miRNAs, and investigate the biology and origins of miRNA genes.
In a study led by Dr. David Bartel, a combination of computational methods and high-throughput sequencing techniques identified new miRNAs conserved across the Drosophila species. “The new fly genomes enabled us to predict new miRNAs, 20 of which we experimentally confirmed, and the genome alignments enabled us to more accurately predict the evolutionarily conserved targets of these and other miRNAs,” explains Bartel.
While computational methods are important for identifying novel miRNAs, large-scale sequencing of small RNAs indicates that many miRNAs continue to evade prediction. “Most of the 59 novel miRNAs that we found were not predicted by us or by others,” describes Bartel. “This illustrates the advantages of high-throughput sequencing of small RNAs, and the limitations of comparative sequence analysis for miRNA gene identification.”
In a related paper, a study led by Dr. Manolis Kellis utilized the twelve Drosophila genomes to computationally predict and experimentally validate novel miRNAs by defining the structural and evolutionary properties of known miRNAs. Classification of newly identified miRNAs has revealed greater diversity in the regulation gene expression by miRNAs, with increased potential for combinatorial regulation, and provided new insights on miRNA biogenesis and function. “We learned that both arms of a miRNA hairpin can produce functional miRNAs, which sometimes work cooperatively to target a common pathway,” explains Kellis.
The combination of comparative and experimental analyses by both groups also provided novel evidence for emergent gene function, deriving from the portion of the miRNA hairpin previously believed to be discarded, and the strand of the DNA previously not thought to produce a miRNA.
David Bartel, Ph.D., Whitehead Institute/MIT/HHMI, Cambridge, MA, USA firstname.lastname@example.org, +1-617-258-5287 or
Eric Lai, Ph.D., Sloan-Kettering Institute, New York, NY, USA email@example.com, +1-212-639-5578 or
J. Graham Ruby, Whitehead Institute/MIT/HHMI, Cambridge, MA, USA firstname.lastname@example.org, +1-617-324-1651Reference:
Manolis Kellis, Ph.D., MIT/Broad Institute, Cambridge, MA, USA email@example.com, +1-617-253-2419 or
Alexander Stark, Ph.D., MIT/Broad Institute, Cambridge, MA, USA firstname.lastname@example.org, +1-617-253-6079Reference:
2. Revisiting D. melanogaster
Drosophila melanogaster is one of the most intensely studied model organisms in biology. Numerous studies over the years have defined nearly 14,000 protein-coding genes by experimental and computational methods, however these methods are likely to have produced erroneous annotations or may be missing other annotations. In order to assess the D. melanogaster protein-coding gene catalog, a group of researchers led by Dr. Manolis Kellis identified evolutionarily signatures of protein-coding genes by comparative analysis of the twelve fly genomes. This strategy was then applied to evaluation of the current catalog and identification of genes that have escaped annotation.
The study led to the discovery of hundreds of new genes, refined existing genes, and concluded that greater than 10% of the protein-coding gene annotations requires refinement.
Additionally, the work revealed abundant unusual gene structures. “We have learned that many brain-expressed proteins may be undergoing post-transcriptional changes by stop-codon read-through,” explains Kellis. “We found 149 genes for which a conserved stop codon is followed by strong evidence of protein-coding selection for up to hundreds of amino acids, suggesting a new mechanism for post-transcriptional regulation in animal genomes.” The researchers also report additional widespread evidence suggesting several diverse mechanisms of post-transcriptional regulation for protein-coding genes.
Manolis Kellis, Ph.D., MIT/Broad Institute, Cambridge, MA, USA email@example.com, +1-617-262-6121Reference:
3. Keeping genes in order
In humans and other vertebrate genomes, long-range regulatory DNA sequences known as highly conserved noncoding elements (HCNEs) have been found to cluster around genes involved in developmental processes, forming genomic regulatory blocks (GRBs). The GRBs are conserved in vertebrates, maintaining the order, or microsynteny, of associated genes on the chromosome. In this study, researchers utilize mosquito genome sequences and sequences available from the twelve fly genome project to investigate the microsynteny underlying GRBs across a wider range of evolution than previously possible.
“By using insect (Drosophila and mosquito) genome comparisons, we show that long-range regulation of developmental genes by arrays of highly conserved regulatory elements is an ancient feature that has shaped the evolution of metazoan genomes,” says Dr. Boris Lenhard, senior investigator of the study.
“Additionally, we present genome-wide evidence that the responsiveness of genes to long-range regulation is partially determined by the type of their core promoter,” explains Lenhard, addressing the issue of how some genes that are conserved in GRBs are not regulated by HCNEs.
Boris Lenhard, Ph.D., University of Bergen, Bergen, Norway firstname.lastname@example.org, +47-555-84362Reference:
4. Tracing the origins of relocated genes
Investigations into the evolution of genomes have revealed significant upheaval in genome organization: insertions, deletions, rearrangement or duplication of large regions, and even duplication of entire genomes. In addition, individual genes have undergone genomic relocation. Sequencing of the twelve Drosophila genomes now allows deeper investigations into single gene relocation and its origins.
“The availability of twelve fly genomes provides a unique opportunity to investigate fine-scale events, such as relocation of individual genes, using whole genome comparative analysis across various levels of evolutionary divergence,” explains primary author Arjun Bhutkar. Bhutkar and colleagues identify and characterize positionally relocated genes (PRGs) in the Drosophila genus, and provide evidence for two distinct origins of PRGs: transposition of genes at the level of DNA, and retrotransposition of RNAs into the genome.
The researchers extended their study to comparisons of Drosophila and other insect genomes. “Such analyses demonstrate the role of PRGs in evolutionary chromosomal organization,” says Bhutkar, as this study highlights the role of PRGs in creation of genomic diversity.
Arjun Bhutkar, Harvard University, Cambridge, MA, USA/Boston University, Boston, MA, USA email@example.com, +1-617- 495-2906 or
William M. Gelbart, Ph.D., Harvard University, Cambridge, MA, USA firstname.lastname@example.org, +1-617-495-2906Reference:
Please direct requests for pre-print copies of the manuscripts to Peggy Calicchia, the Editorial Secretary for Genome Research (email@example.com; +1-516-422-4012). In addition to the five articles highlighted above, the following will also appear in the issue:
5. Heger, A. and Ponting, C. 2007. Evolutionary rate analyses of orthologues and paralogues from twelve Drosophila Genomes. Genome Res. doi:10.1101/gr.6249707
6. Villasante, A. et al. 2007. Drosophila telomeric retrotransposons derived from an ancestral element that was recruited to replace telomerase. Genome Res. doi:10.1101/gr.6365107
7. Stage, D.E. and Eickbush, T.H. 2007. Sequence variation within the rRNA gene loci of twelve Drosophila species. Genome Res. doi:10.1101/gr.6376807
8. Stark, A. et al. 2007. Reliable prediction of regulator targets using 12 Drosophila genomes. Genome Res. doi:10.1101/gr.7090407
9. Rasmussen, M.D. and Kellis, M. 2007. Accurate gene-tree reconstruction by learning gene- and species-specific substitution rates across multiple complete genomes. Genome Res. doi:10.1101/gr.7105007
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