Phenoscape: Identifying Candidate Genes for Evolutionary Phenotypes

Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance to mutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes). We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology

Clinical utilization of genomics data produced by the international Pseudomonas aeruginosa consortium

The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aeruginosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P. aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care

Super wide regression network for unsupervised cross-database facial expression recognition

AbstractUnsupervised cross-database facial expression recognition (FER) is a challenging problem, in which the training and testing samples belong to different facial expression databases. For this reason, the training (source) and testing (target) facial expression samples would have different feature distributions and hence the performance of lots of existing FER methods may decrease. To solve this problem, in this paper we propose a novel super wide regression network (SWiRN) model, which serves as the regression parameter to bridge the original feature space and the label space and herein in each layer the maximum mean discrepancy (MMD) criterion is used to enforce the source and target facial expression samples to share the same or similar feature distributions. Consequently, the learned SWiRN is able to predict the expression categories of the target samples although we have no access to any label information of target samples. We conduct extensive cross-database FER experiments on CK+, eNTERFACE, and Oulu-CASIA VIS facial expression databases to evaluate the proposed SWiRN. Experimental results show that our SWiRN model achieves more promising performance than recent proposed cross-database emotion recognition methods

Cell Surface Delivery of TRAIL Strongly Augments the Tumoricidal Activity of T Cells

Purpose: Adoptive T-cell therapy generally fails to induce meaningful anticancer responses in patients with solid tumors. Here, we present a novel strategy designed to selectively enhance the tumoricidal activity of T cells by targeted delivery of TNF-related apoptosis-inducing ligand (TRAIL) to the T-cell surface. Experimental Design: We constructed two recombinant fusion proteins, anti-CD3: TRAIL and K12: TRAIL. Tumoricidal activity of T cells in the presence of these fusion proteins was assessed in solid tumor cell lines, primary patient-derived malignant cells, and in a murine xenograft model. Results: When added to T cells, K12:TRAIL and anti-CD3:TRAIL selectively bind to the T-cell surface antigens CD3 and CD7, respectively, leading to cell surface accretion of TRAIL. Subsequently, anti-CD3:TRAIL and K12:TRAIL increased the tumoricidal activity of T cells toward cancer cell lines and primary patient-derived malignant cells by more than 500-fold. Furthermore, T-cell surface delivery of TRAIL strongly inhibited tumor growth and increased survival time of xenografted mice more than 6-fold. Conclusions: Targeted delivery of TRAIL to cell surface antigens of T cells potently enhances the tumoricidal activity of T cells. This approach may be generally applicable to enhance the efficacy of adoptive T-cell therapy. Clin Cancer Res; 17(17); 5626-37. (C)2011 AACR