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Abstract Background Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships. Results We present results on a study where we use a large set of phenotype data – in textual form – to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations. Conclusion The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.

Functional RNAi based screening is affected by large numbers of false positive and negative hits due to prevalent sequence based off-target effects. We performed a druggable genome targeting siRNA screen intended to identify novel regulators of E-cadherin (CDH1) expression, a known key player in epithelial mesenchymal transition (EMT). Analysis of primary screening results indicated a large number of false-positive hits. To address these crucial difficulties we developed an analysis method, SENSORS, which, similar to published methods, is a seed enrichment strategy for analyzing siRNA off-targets in RNAi screens. Using our approach, we were able to demonstrate that accounting for seed based off-target effects stratifies primary screening results and enables the discovery of additional screening hits. While traditional hit detection methods are prone to false positive results which are undetected, we were able to identify false positive hits robustly. Transcription factor MYBL1 was identified as a putative novel target required for CDH1 expression and verified experimentally. No siRNA pool targeting MYBL1 was present in the used siRNA library. Instead, MYBL1 was identified as a putative CDH1 regulating target solely based on the SENSORS off-target score, i.e. as a gene that is a cause for off-target effects down regulating E-cadherin expression. [ABSTRACT FROM AUTHOR]

Background: Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships. Results: We present results on a study where we use a large set of phenotype data -- in textual form -- to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations. Conclusion: The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process. [ABSTRACT FROM AUTHOR]

THR1, the gene from Saccharomyces cerevisiae, encoding homoserine kinase, one of the threonine biosynthetic enzymes, has been cloned by complementation. The nucleotide sequence of a 3.1-kb region carrying this gene reveals an open reading frame of 356 codons, corresponding to about 40 kDa for the encoded protein. The presence of three canonical GCN4 regulatory sequences in the upstream flanking region suggests that the expression of THR1 is under the general amino acid control. In parallel, the enzyme was purified by four consecutive column chromatographies, monitoring homoserine kinase activity. In SDS gel electrophoresis, homoserine kinase migrates like a 40-kDa protein; the native enzyme appears to be a homodimer. Thc sequence of the first 15 NH2-terminal amino acids, as determined by automated Edman degradation, is in accordance with the amino acid sequence deduced from the nucleotide sequence. Computer-assisted comparison of the yeast enzyme with the corresponding activities from bacterial sources showed that several segments among these proteins are highly conserved. Furthermore, the observed homology patterns suggest that the ancestral sequences might have been composed from separate (functional) domains. A block of very similar amino acids is found in the homoserine kinases towards the carboxy terminus that is also present in many other proteins involved in threonine (or serine) metabolism: this motif, therefore, may represent the binding site for the hydroxyamino acids. Limited similarity was detected between a motif conserved among thc homoserine kinases and consensus sequences found in other mono- or dinucleotide-binding proteins. [ABSTRACT FROM AUTHOR]

Abstract: Human leucine-rich repeat kinase 1 (LRRK1) is a multi-domain protein of unknown function belonging to the ROCO family of complex proteins. Here, we report the molecular characterization of human LRRK1 and show, for the first time, that LRRK1 is both a functional protein kinase and a GDP/GTP-binding protein. Binding of GTP to LRRK1 is specific, requires the GTPase-like Roc domain, and leads to a stimulation of LRRK1 kinase activity. LRRK1 is the first example of a GTP-regulated protein kinase harboring both the kinase effector domain and the GTP-binding regulatory domain. Hence, we propose a model in which LRRK1 cycles between a GTP-bound active and a GDP-bound inactive state. Moreover, we mutated LRRK1 to mimic mutations previously identified in LRRK2/dardarin, the only human paralogue of LRRK1, that have been linked to autosomal-dominant parkinsonism. We demonstrate that three of four mutations analyzed significantly downregulate LRRK1 kinase activity. Ultimately, the results presented for LRRK1 may contribute to the elucidation of LRRK2''s role in the pathogenesis of Parkinson''s disease. [Copyright &y& Elsevier]

In order to minimise attrition rates in drug development projects, a target discovery process is implemented to select and characterise the most suitable candidate kinase targets, before lead identification and lead optimisation are embarked upon. The process consists of 1) target selection, 2) target assessment, and 3) target validation. This rational approach to target discovery, as a prerequisite for lead discovery, ensures that new therapeutic targets fulfil a set of general criteria, as well as indication-specific, descriptive and functional ones. The approach should ultimately maximise the likelihood of achieving target-selective inhibition by small-molecule inhibitors with minimal in vivo side effects and a therapeutic effect based on a sound biological hypothesis.

The reasons why human mammary tumors become resistant to tamoxifen therapy are mainly unknown. Changes in gene expression may occur as cells acquire resistance to antiestrogens. We therefore undertook a comparative gene expression analysis of tamoxifen-sensitive and tamoxifen-resistant human breast cancer in vivo models using Affymetrix oligonucleotide arrays to analyze differential gene expression. Total RNAs from the tamoxifen-sensitive patient-derived mammary carcinoma xenograft MaCa 3366 and the tamoxifen-resistant model MaCa 3366/TAM were hybridized to Affymetrix HuGeneFL and to Hu95Av2 arrays. Pairwise comparisons and clustering algorithms were applied to identify differentially expressed genes and patterns of gene expression. As revealed by cluster analysis, the tamoxifen-sensitive and the tamoxifen-resistant breast carcinomas differed regarding their gene expression pattern. More than 100 transcripts are changed in abundance in MaCa 3366/TAM as compared with MaCa 3366. Among the genes that are differentially expressed in the tamoxifen-resistant tumors, there are several IFN-inducible and estrogen-responsive genes, and genes known to be involved in breast carcinogenesis. The genes neuronatin (NNAT) and bone marrow stem cell antigen 2 (BST2) were sharply up-regulated in MaCa 3366/TAM. The differential expression of four genes (NNAT, BST2, IGFBP5, and BCAS1) was confirmed by Taqman PCR. Our results provide the starting point for deriving markers for tamoxifen resistance by differential gene expression profiling in a human breast cancer model of acquired tamoxifen resistance. Finally, genes whose expression profiles are distinctly changed between the two xenograft lines will be further evaluated as potential targets for diagnostic or therapeutic approaches of tamoxifen-resistant breast cancer.

In search of novel mechanisms leading to the development of antiestrogen-resistance in human breast tumors, we analyzed differences in the gene and protein expression pattern of the human breast carcinoma cell line T47D and its derivative T47D-r, which is resistant toward the pure antiestrogen ZM 182780 (Faslodex trade mark, fulvestrant). Affymetrix DNA chip hybridizations on the commercially available HuGeneFL and Hu95A arrays were carried out in parallel to the proteomics analysis where the total cellular protein content of T47D or T47D-r was separated on two-dimensional gels. Thirty-eight proteins were found to be reproducibly up- or down-regulated more than 2-fold in T47D-r versus T47D in the proteomics analysis. Comparison with differential mRNA analysis revealed that 19 of these were up- or down-regulated in parallel with the corresponding mRNA molecules, among which are the protease cathepsin D, the GTPases Rab11a and MxA, and the secreted protein hAG-2. For 11 proteins, the corresponding mRNA was not found to be differentially expressed, and for eight proteins an inverse regulation was found at the mRNA level. In summary, mRNA expression data, when combined with proteomic information, provide a more detailed picture of how breast cancer cells are altered in their antiestrogen-resistant compared with the antiestrogen-sensitive state.