Aberrant promoter hypermethylation of PBRM1, BAP1, SETD2, KDM6A and other chromatin-modifying genes is absent or rare in clear cell RCC

Recent sequencing studies of clear cell (conventional) renal cell carcinoma (ccRCC) have identified inactivating point mutations in the chromatin-modifying genes PBRM1, KDM6A/UTX, KDM5C/JARID1C, SETD2, MLL2 and BAP1. To investigate whether aberrant hypermethylation is a mechanism of inactivation of these tumor suppressor genes in ccRCC, we sequenced the promoter region within a bona fide CpG island of PBRM1, KDM6A, SETD2 and BAP1 in bisulfite-modified DNA of a representative series of 50 primary ccRCC, 4 normal renal parenchyma specimens and 5 RCC cell lines. We also interrogated the promoter methylation status of KDM5C and ARID1A in the Cancer Genome Atlas (TCGA) ccRCC Infinium data set. PBRM1, KDM6A, SETD2 and BAP1 were unmethylated in all tumor and normal specimens. KDM5C and ARID1A were unmethylated in the TCGA 219 ccRCC and 119 adjacent normal specimens. Aberrant promoter hypermethylation of PBRM1, BAP1 and the other chromatin-modifying genes examined here is therefore absent or rare in ccRCC.     

Rule extraction in gene–disease relationship discovery

Background

Biomedical data available to researchers and clinicians have increased dramatically over the past years because of the exponential growth of knowledge in medical biology. It is difficult for curators to go through all of the unstructured documents so as to curate the information to the database. Associating genes with diseases is important because it is a fundamental challenge in human health with applications to understanding disease properties and developing new techniques for prevention, diagnosis and therapy.

Methods

Our study uses the automatic rule-learning approach to gene–disease relationship extraction. We first prepare the experimental corpus from MEDLINE and OMIM. A parser is applied to produce some grammatical information. We then learn all possible rules that discriminate relevant from irrelevant sentences. After that, we compute the scores of the learned rules in order to select rules of interest. As a result, a set of rules is generated.

Results

We produce the learned rules automatically from the 1000 positive and 1000 negative sentences. The test set includes 400 sentences composed of 200 positives and 200 negatives. Precision, recall and F-score served as our evaluation metrics. The results reveal that the maximal precision rate is 77.8% and the maximal recall rate is 63.5%. The maximal F-score is 66.9% where the precision rate is 70.6% and the recall rate is 63.5%.

Conclusions

We employ the rule-learning approach to extract gene–disease relationships. Our main contributions are to build rules automatically and to support a more complete set of rules than a manually generated one. The experiments show exhilarating results and some improving efforts will be made in the future.     

Effect of organ site on nuclear matrix protein composition

The nuclear matrix has been linked to several important cellular functions within cells, such as DNA organization and replication, as well as regulation of gene expression. It has been reported that the nuclear matrix protein composition is altered in cells grown on different extracellular matrices in vitro. This study examined the nuclear matrix protein composition of tumors produced by MAT-LyLu (MLL) rat prostate tumor cells implanted at different organ sites within the rat. When high resolution two-dimensional gels were utilized to compare nuclear matrix protein composition to the prostate orthotopic tumor, it was found that there were distinct protein differences depending upon where the tumor grew. In particular, there were 14 proteins found in the lung, six proteins found in intramuscular, 17 proteins is the heart, and five proteins in the tail vein tumor tissue that were not present in the prostate orthotopic tumor tissue. Therefore, this study adds evidence to support that the nuclear matrix composition of a cell is dependent, at least in part, by the extracellular matrix and/or different cellular environments and may have a role in site-specific differences in tumor properties. © 1996 Wiley-Liss, Inc.     

The importin-alpha/nucleophosmin switch controls taspase1 protease function

Taspase1 is a threonine protease suspected to process (patho)biologically relevant nuclear and cytoplasmic substrates, such as the mixed lineage leukemia protein. However, neither the mechanisms regulating Taspase1's intracellular localization nor their functional consequences are known. Analysis of endogenous and ectopically expressed Taspase1 detected the protease predominantly in the nucleus accumulating at the nucleolus. Microinjection and ectopic expression studies identified an evolutionarily conserved bipartite nuclear import signal (NLS) (amino acids (197) KRNKRKLELA ERVDTDFMQLKKRR(220) ) interacting with importin-α. Notably, an NLS-mutated, import-deficient Taspase1 was biologically inactive. Although the NLS conferred nuclear transport already of the proenzyme, Taspase1's nucleolar localization required its autoproteolytic processing, triggering its interaction with the nucleolar shuttle protein nucleophosmin. In contrast, (auto)catalytically inactive Taspase1 mutants neither accumulated at the nucleolus nor bound nucleophosmin. Active nuclear import and interaction with nucleophosmin was found to be required for the formation of proteolytically active Taspase1 ensuring to efficiently process its nuclear targets. Intriguingly, coexpression of pathological nucleophosmin variants increased the amount of cytoplasmic Taspase1. Hence, Taspase1 appears to exploit the nuclear export activity of nucleophosmin to gain transient access to the cytoplasm required to also cleave its cytoplasmic substrates. Collectively, we here describe a hitherto unknown mechanism regulating the biological activity of this protease.     

Local gene density predicts the spatial position of genetic loci in the interphase nucleus

Specific chromosomal translocations are hallmarks of many human leukemias. The basis for these translocation events is poorly understood, but it has been assumed that spatial positioning of genes in the nucleus of hematopoietic cells is a contributing factor. Analysis of the nuclear 3D position of the gene MLL, frequently involved in chromosomal translocations and five of its translocation partners (AF4, AF6, AF9, ENL and ELL), and two control loci revealed a characteristic radial distribution pattern in all hematopoietic cells studied. Genes in areas of high local gene density were found positioned towards the nuclear center, whereas genes in regions of low gene density were detected closer to the nuclear periphery. The gene density within a 2 Mbp window was found to be a better predictor for the relative positioning of a genomic locus within the cell nucleus than the gene density of entire chromosomes. Analysis of the position of MLL, AF4, AF6 and AF9 in cell lines carrying chromosomal translocations involving these genes revealed that the position of the normal genes was different from that of the fusion genes, and this was again consistent with the changes in local gene density within a 2 Mbp window. Thus, alterations in gene density directly at translocation junctions could explain the change in the position of affected genes in leukemia cells.