Overexpression of ribosomal RNA in prostate cancer is common but not linked to rDNA promoter hypomethylation

Alterations in nucleoli, including increased numbers, increased size, altered architecture and increased function are hallmarks of prostate cancer cells. The mechanisms that result in increased nucleolar size, number and function in prostate cancer have not been fully elucidated. The nucleolus is formed around repeats of a transcriptional unit encoding a 45S ribosomal RNA (rRNA) precursor that is then processed to yield the mature 18S, 5.8S and 28S RNA species. Although it has been generally accepted that tumor cells overexpress rRNA species, this has not been examined in clinical prostate cancer. We find that indeed levels of the 45S rRNA, 28S, 18S and 5.8S are overexpressed in the majority of human primary prostate cancer specimens as compared with matched benign tissues. One mechanism that can alter nucleolar function and structure in cancer cells is hypomethylation of CpG dinucleotides of the upstream rDNA promoter region. However, this mechanism has not been examined in prostate cancer. To determine whether rRNA overexpression could be explained by hypomethylation of these CpG sites, we also evaluated the DNA methylation status of the rDNA promoter in prostate cancer cell lines and the clinical specimens. Bisulfite sequencing of genomic DNA revealed two roughly equal populations of loci in cell lines consisting of those that contained densely methylated deoxycytidine residues within CpGs and those that were largely unmethylated. All clinical specimens also contained two populations with no marked changes in methylation of this region in cancer as compared with normal. We recently reported that MYC can regulate rRNA levels in human prostate cancer; here we show that MYC mRNA levels are correlated with 45S, 18S and 5.8S rRNA levels. Further, as a surrogate for nucleolar size and number, we examined the expression of fibrillarin, which did not correlate with rRNA levels. We conclude that rRNA levels are increased in human prostate cancer, but that hypomethylation of the rDNA promoter does not explain this increase, nor does hypomethylation explain alterations in nucleolar number and structure in prostate cancer cells. Rather, rRNA levels and nucleolar size and number relate more closely to MYC overexpression

Nucleolus: the fascinating nuclear body

Nucleoli are the prominent contrasted structures of the cell nucleus. In the nucleolus, ribosomal RNAs are synthesized, processed and assembled with ribosomal proteins. RNA polymerase I synthesizes the ribosomal RNAs and this activity is cell cycle regulated. The nucleolus reveals the functional organization of the nucleus in which the compartmentation of the different steps of ribosome biogenesis is observed whereas the nucleolar machineries are in permanent exchange with the nucleoplasm and other nuclear bodies. After mitosis, nucleolar assembly is a time and space regulated process controlled by the cell cycle. In addition, by generating a large volume in the nucleus with apparently no RNA polymerase II activity, the nucleolus creates a domain of retention/sequestration of molecules normally active outside the nucleolus. Viruses interact with the nucleolus and recruit nucleolar proteins to facilitate virus replication. The nucleolus is also a sensor of stress due to the redistribution of the ribosomal proteins in the nucleoplasm by nucleolus disruption. The nucleolus plays several crucial functions in the nucleus: in addition to its function as ribosome factory of the cells it is a multifunctional nuclear domain, and nucleolar activity is linked with several pathologies. Perspectives on the evolution of this research area are proposed

Bayesian analysis of the scatterometer wind retrieval inverse problem: some new approaches

The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer. We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution. We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes. This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets. We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm. Copyright 2004 Royal Statistical Society.