Androgen receptor phosphorylation at serine 515 by Cdk1 predicts biochemical relapse in prostate cancer patients

<br>Background:Prostate cancer cell growth is dependent upon androgen receptor (AR) activation, which is regulated by specific kinases. The aim of the current study is to establish if AR phosphorylation by Cdk1 or ERK1/2 is of prognostic significance.</br> <br>Methods: Scansite 2.0 was utilised to predict which AR sites are phosphorylated by Cdk1 and ERK1/2. Immunohistochemistry for these sites was then performed on 90 hormone-naive prostate cancer specimens. The interaction between Cdk1/ERK1/2 and AR phosphorylation was investigated in vitro using LNCaP cells.</br><br>Results:Phosphorylation of AR at serine 515 (pAR(S515)) and PSA at diagnosis were independently associated with decreased time to biochemical relapse. Cdk1 and pCdk1(161), but not ERK1/2, correlated with pAR(S515). High expression of pAR(S515) in patients with a PSA at diagnosis of ≤20 ng ml(-1) was associated with shorter time to biochemical relapse (P=0.019). This translated into a reduction in disease-specific survival (10-year survival, 38.1% vs 100%, P<0.001). In vitro studies demonstrated that treatment with Roscovitine (a Cdk inhibitor) caused a reduction in pCdk1(161) expression, pAR(S515)expression and cellular proliferation.</br> <br>Conclusion: In prostate cancer patients with PSA at diagnosis of ≤20 ng ml(-1), phosphorylation of AR at serine 515 by Cdk1 may be an independent prognostic marker.</br&gt

Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy

Background: Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. Methodology/Principal Findings: Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. Conclusions/Significance: Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is,4.6 times that of random minimotifs. For the molecular function filter this ratio is,2.9. These results, together with the comparison with the published frequency score filter, strongly suggest tha

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Background: The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. Results: Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from ≥88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections. Conclusion: To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field

Rapid and Highly Informative Diagnostic Assay for H5N1 Influenza Viruses

A highly discriminative and information-rich diagnostic assay for H5N1 avian influenza would meet immediate patient care needs and provide valuable information for public health interventions, e.g., tracking of new and more dangerous variants by geographic area as well as avian-to-human or human-to-human transmission. In the present study, we have designed a rapid assay based on multilocus nucleic acid sequencing that focuses on the biologically significant regions of the H5N1 hemagglutinin gene. This allows the prediction of viral strain, clade, receptor binding properties, low- or high-pathogenicity cleavage site and glycosylation status. H5 HA genes were selected from nine known high-pathogenicity avian influenza subtype H5N1 viruses, based on their diversity in biologically significant regions of hemagglutinin and/or their ability to cause infection in humans. We devised a consensus pre-programmed pyrosequencing strategy, which may be used as a faster, more accurate alternative to de novo sequencing. The available data suggest that the assay described here is a reliable, rapid, information-rich and cost-effective approach for definitive diagnosis of H5N1 avian influenza. Knowledge of the predicted functional sequences of the HA will enhance H5N1 avian influenza surveillance efforts

A Comparative Study of Human TLR 7/8 Stimulatory Trimer Compositions in Influenza A Viral Genomes

Background: Variation in the genomes of single-stranded RNA viruses affects their infectivity and pathogenicity in two ways. First, viral genome sequence variations lead to changes in viral protein sequences and activities. Second, viral genome sequence variation produces diversity at the level of nucleotide composition and diversity in the interactions between viral RNAs and host toll-like receptors (TLRs). A viral genome-typing method based on this type of diversity has not yet been established. Methodology/Principal Findings: In this study, we propose a novel genomic trait called the ‘‘TLR stimulatory trimer composition’ ’ (TSTC) and two quantitative indicators, Score S and Score N, named ‘‘TLR stimulatory scores’ ’ (TSS). Using the complete genome sequences of 10,994 influenza A viruses (IAV) and 251 influenza B viruses, we show that TSTC analysis reveals the diversity of Score S and Score N among the IAVs isolated from various hosts. In addition, we show that low values of Score S are correlated with high pathogenicity and pandemic potential in IAVs. Finally, we use Score S and Score N to construct a logistic regression model to recognize IAV strains that are highly pathogenic or have high pandemic potential. Conclusions/Significance: Results from the TSTC analysis indicate that there are large differences between human and avian IAV genomes (except for segment 3), as illustrated by Score S. Moreover, segments 1, 2, 3 and 4 may be majo

Metastasis of Tumor Cells Is Enhanced by Downregulation of Bit1

Resistance to anoikis, which is defined as apoptosis induced by loss of integrin-mediated cell attachment to the extracellular matrix, is a determinant of tumor progression and metastasis. We have previously identified the mitochondrial Bit1 (Bcl-2 inhibitor of transcription) protein as a novel anoikis effector whose apoptotic function is independent from caspases and is uniquely controlled by integrins. In this report, we examined the possibility that Bit1 is suppressed during tumor progression and that Bit1 downregulation may play a role in tumor metastasis.Using a human breast tumor tissue array, we found that Bit1 expression is suppressed in a significant fraction of advanced stages of breast cancer. Targeted disruption of Bit1 via shRNA technology in lowly aggressive MCF7 cells conferred enhanced anoikis resistance, adhesive and migratory potential, which correlated with an increase in active Extracellular kinase regulated (Erk) levels and a decrease in Erk-directed phosphatase activity. These pro-metastasis phenotypes were also observed following downregulation of endogenous Bit1 in Hela and B16F1 cancer cell lines. The enhanced migratory and adhesive potential of Bit1 knockdown cells is in part dependent on their high level of Erk activation since down-regulating Erk in these cells attenuated their enhanced motility and adhesive properties. The Bit1 knockdown pools also showed a statistically highly significant increase in experimental lung metastasis, with no differences in tumor growth relative to control clones in vivo using a BALB/c nude mouse model system. Importantly, the pulmonary metastases of Bit1 knockdown cells exhibited increased phospho-Erk staining.These findings indicate that downregulation of Bit1 conferred cancer cells with enhanced anoikis resistance, adhesive and migratory properties in vitro and specifically potentiated tumor metastasis in vivo. These results underscore the therapeutic importance of restoring Bit1 expression in cancer cells to circumvent metastasis at least in part through inhibition of the Erk pathway

BID-F1 and BID-F2 Domains of Bartonella henselae Effector Protein BepF Trigger Together with BepC the Formation of Invasome Structures

The gram-negative, zoonotic pathogen Bartonella henselae (Bhe) translocates seven distinct Bartonella effector proteins (Beps) via the VirB/VirD4 type IV secretion system (T4SS) into human cells, thereby interfering with host cell signaling [1], [2]. In particular, the effector protein BepG alone or the combination of effector proteins BepC and BepF trigger massive F-actin rearrangements that lead to the establishment of invasome structures eventually resulting in the internalization of entire Bhe aggregates [2], [3]. In this report, we investigate the molecular function of the effector protein BepF in the eukaryotic host cell. We show that the N-terminal [E/T]PLYAT tyrosine phosphorylation motifs of BepF get phosphorylated upon translocation but do not contribute to invasome-mediated Bhe uptake. In contrast, we found that two of the three BID domains of BepF are capable to trigger invasome formation together with BepC, while a mutation of the WxxxE motif of the BID-F1 domain inhibited its ability to contribute to the formation of invasome structures. Next, we show that BepF function during invasome formation can be replaced by the over-expression of constitutive-active Rho GTPases Rac1 or Cdc42. Finally we demonstrate that BID-F1 and BID-F2 domains promote the formation of filopodia-like extensions in NIH 3T3 and HeLa cells as well as membrane protrusions in HeLa cells, suggesting a role for BepF in Rac1 and Cdc42 activation during the process of invasome formation

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community