Splicing factors stimulate polyadenylation via USEs at non-canonical 3' end formation signals.
Danckwardt S, Kaufmann I, Gentzel M, Foerstner KU
, Gantzert AS, Gehring NH, Neu-Yilik G, Bork P
, Keller W, Wilm M, Hentze MW, Kulozik AE
2007 Jun 6; 26(11): 2658-69. Epub 2007 Apr 26; PubMed: 17464285.
Abstract + PDF
The prothrombin (F2) 3' end formation signal is highly susceptible to thrombophilia-associated gain-of-function mutations. In its unusual architecture, the F2 3' UTR contains an upstream sequence element (USE) that compensates for weak activities of the non-canonical cleavage site and the downstream U-rich element. Here, we address the mechanism of USE function. We show that the F2 USE contains a highly conserved nonameric core sequence, which promotes 3' end formation in a position- and sequence-dependent manner. We identify proteins that specifically interact with the USE, and demonstrate their function as trans-acting factors that promote 3' end formation. Interestingly, these include the splicing factors U2AF35, U2AF65 and hnRNPI. We show that these splicing factors not only modulate 3' end formation via the USEs contained in the F2 and the complement C2 mRNAs, but also in the biocomputationally identified BCL2L2, IVNS and ACTR mRNAs, suggesting a broader functional role. These data uncover a novel mechanism that functionally links the splicing and 3' end formation machineries of multiple cellular mRNAs in an USE-dependent manner.
Get the most out of your metagenome: computational analysis of environmental sequence data.
2007 Oct 11; 10(5): 490-8. Epub 2007 Oct 23; PubMed: 17936679.
Abstract + PDF
New advances in sequencing technologies bring random shotgun sequencing of ecosystems within reach of smaller labs, but the complexity of metagenomics data can be overwhelming. Recently, many novel computational tools have been developed to unravel ecosystem properties starting from fragmented sequences. In addition, the so-called 'comparative metagenomics' approaches have allowed the discovery of specific genomic and community adaptations to environmental factors. However, many of the parameters extracted from these data to describe the environment at hand (e.g. genomic features, functional complement, phylogenetic composition) are interdependent and influenced by technical aspects of sample preparation and data treatment, leading to various pitfalls during analysis. To avoid this and complement existing initiatives in data standards, we propose a minimal standard for metagenomics data analysis ('MINIMESS') to be able to take full advantage of the power of comparative metagenomics in understanding microbial life on earth.