High resolution genomic analysis of sporadic breast cancer using array-based comparative genomic hybridization

INTRODUCTION: Genomic aberrations in the form of subchromosomal DNA copy number changes are a hallmark of epithelial cancers, including breast cancer. The goal of the present study was to analyze such aberrations in breast cancer at high resolution. METHODS: We employed high-resolution array comparative genomic hybridization with 4,134 bacterial artificial chromosomes that cover the genome at 0.9 megabase resolution to analyze 47 primary breast tumors and 18 breast cancer cell lines. RESULTS: Common amplicons included 8q24.3 (amplified in 79% of tumors, with 5/47 exhibiting high level amplification), 1q32.1 and 16p13.3 (amplified in 66% and 57% of tumors, respectively). Moreover, we found several positive correlations between specific amplicons from different chromosomes, suggesting the existence of cooperating genetic loci. Queried by gene, the most frequently amplified kinase was PTK2 (79% of tumors), whereas the most frequently lost kinase was PTK2B (hemizygous loss in 34% of tumors). Amplification of ERBB2 as measured by comparative genomic hybridization (CGH) correlated closely with ERBB2 DNA and RNA levels measured by quantitative PCR as well as with ERBB2 protein levels. The overall frequency of recurrent losses was lower, with no region lost in more than 50% of tumors; the most frequently lost tumor suppressor gene was RB1 (hemizygous loss in 26% of tumors). Finally, we find that specific copy number changes in cell lines closely mimicked those in primary tumors, with an overall Pearson correlation coefficient of 0.843 for gains and 0.734 for losses. CONCLUSION: High resolution CGH analysis of breast cancer reveals several regions where DNA copy number is commonly gained or lost, that non-random correlations between specific amplicons exist, and that specific genetic alterations are maintained in breast cancer cell lines despite repeat passage in tissue culture. These observations suggest that genes within these regions are critical to the malignant phenotype and may thus serve as future therapeutic targets

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

Predicting protein-protein interface residues using local surface structural similarity

<p>Abstract</p> <p>Background</p> <p>Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce <it>PrISE</it>, a family of local structural similarity-based computational methods for predicting protein-protein interface residues.</p> <p>Results</p> <p>We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The <it>PrISE </it>family of interface prediction methods uses a representation of structural elements that captures the atomic composition and accessible surface area of the residues that make up each structural element. Each of the members of the <it>PrISE </it>methods identifies for each structural element in the query protein, a collection of <it>similar </it>structural elements in its repository of structural elements and weights them according to their similarity with the structural element of the query protein. <it>PrISE_L</it>relies on the similarity between structural elements (i.e. local structural similarity). <it>PrISE_G</it>relies on the similarity between protein surfaces (i.e. general structural similarity). <it>PrISE_C</it>, combines local structural similarity and general structural similarity to predict interface residues. These predictors label the central residue of a structural element in a query protein as an interface residue if a weighted majority of the structural elements that are similar to it are interface residues, and as a non-interface residue otherwise. The results of our experiments using three representative benchmark datasets show that the <it>PrISE_C</it>outperforms <it>PrISE_L</it>and <it>PrISE_G</it>; and that <it>PrISE_C</it>is highly competitive with state-of-the-art structure-based methods for predicting protein-protein interface residues. Our comparison of <it>PrISE_C</it>with <it>PredUs</it>, a recently developed method for predicting interface residues of a query protein based on the known interface residues of its (global) structural homologs, shows that performance superior or comparable to that of <it>PredUs </it>can be obtained using only local surface structural similarity. <it>PrISE_C</it>is available as a Web server at <url>http://prise.cs.iastate.edu/</url></p> <p>Conclusions</p> <p>Local surface structural similarity based methods offer a simple, efficient, and effective approach to predict protein-protein interface residues.</p

Downgrading MELD Improves the Outcomes after Liver Transplantation in Patients with Acute-on-Chronic Hepatitis B Liver Failure

Background: High score of model for end-stage liver diseases (MELD) before liver transplantation (LT) indicates poor prognosis. Artificial liver support system (ALSS) has been proved to effectively improve liver and kidney functions, and thus reduce the MELD score. We aim to evaluate whether downgrading MELD score could improve patient survival after LT. Methodology/Principal Findings: One hundred and twenty-six LT candidates with acute-on-chronic hepatitis B liver failure and MELD score $30 were included in this prospective study. Of the 126 patients, 42 received emergency LT within 72 h (ELT group) and the other 84 were given ALSS as salvage treatment. Of the 84 patients, 33 were found to have reduced MELD score (,30) on the day of LT (DGM group), 51 underwent LT with persistent high MELD score (N-DGM group). The median waiting time for a donor was 10 for DGM group and 9.5 days for N-DGM group. In N-DGM group there is a significantly higher overall mortality (43.1$30 was effective i

Transcriptome Sequencing and De Novo Analysis for Yesso Scallop (Patinopecten yessoensis) Using 454 GS FLX

BACKGROUND: Bivalves comprise 30,000 extant species, constituting the second largest group of mollusks. However, limited genetic research has focused on this group of animals so far, which is, in part, due to the lack of genomic resources. The advent of high-throughput sequencing technologies enables generation of genomic resources in a short time and at a minimal cost, and therefore provides a turning point for bivalve research. In the present study, we performed de novo transcriptome sequencing to first produce a comprehensive expressed sequence tag (EST) dataset for the Yesso scallop (Patinopecten yessoensis). RESULTS: In a single 454 sequencing run, 805,330 reads were produced and then assembled into 32,590 contigs, with about six-fold sequencing coverage. A total of 25,237 unique protein-coding genes were identified from a variety of developmental stages and adult tissues based on sequence similarities with known proteins. As determined by GO annotation and KEGG pathway mapping, functional annotation of the unigenes recovered diverse biological functions and processes. Transcripts putatively involved in growth, reproduction and stress/immune-response were identified. More than 49,000 single nucleotide polymorphisms (SNPs) and 2,700 simple sequence repeats (SSRs) were also detected. CONCLUSION: Our data provide the most comprehensive transcriptomic resource currently available for P. yessoensis. Candidate genes potentially involved in growth, reproduction, and stress/immunity-response were identified, and are worthy of further investigation. A large number of SNPs and SSRs were also identified and ready for marker development. This resource should lay an important foundation for future genetic or genomic studies on this species

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference