The RNA-binding protein HuR regulates DNA methylation through stabilization of DNMT3b mRNA

The molecular basis underlying the aberrant DNA-methylation patterns in human cancer is largely unknown. Altered DNA methyltransferase (DNMT) activity is believed to contribute, as DNMT expression levels increase during tumorigenesis. Here, we present evidence that the expression of DNMT3b is post-transcriptionally regulated by HuR, an RNA-binding protein that stabilizes and/or modulates the translation of target mRNAs. The presence of a putative HuR-recognition motif in the DNMT3b 3'UTR prompted studies to investigate if this transcript associated with HuR. The interaction between HuR and DNMT3b mRNA was studied by immunoprecipitation of endogenous HuR ribonucleoprotein complexes followed by RT-qPCR detection of DNMT3b mRNA, and by in vitro pulldown of biotinylated DNMT3b RNAs followed by western blotting detection of HuR. These studies revealed that binding of HuR stabilized the DNMT3b mRNA and increased DNMT3b expression. Unexpectedly, cisplatin treatment triggered the dissociation of the [HuR-DNMT3b mRNA] complex, in turn promoting DNMT3b mRNA decay, decreasing DNMT3b abundance, and lowering the methylation of repeated sequences and global DNA methylation. In summary, our data identify DNMT3b mRNA as a novel HuR target, present evidence that HuR affects DNMT3b expression levels post-transcriptionally, and reveal the functional consequences of the HuR-regulated DNMT3b upon DNA methylation patterns.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.