Browsing by Subject "phenotypes"

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Approaches to discover new human disease models through Boolean relationships of orthologous phenotypes
(2012-04-26) Tien, Matthew; Marcotte, Edward
In the development of genome-wide databases of model organisms, non-traditional approaches to study genomic networks have emerged to model molecular interactions. Past approaches to model such systems have used the homology of genes in model organisms to study human diseases and conditions. Combining the homology of genes between human model organisms with information in genomic databases, the Marcotte laboratory has discovered a systematic approach to predict new candidate genes for human diseases. In characterizing phenotypes of model organisms with homologous genes, it is possible to reveal similar genetic interactions of homologous genes in human diseases. These phenotypes are characterized by the presence and absence of all orthologous genes between species, these binary data structures are called phenologs. The project below examined the potential of Boolean relationships of phenologs within one or multiple species to optimize the identified set of genes for a human disease and to predict more candidate genes involved in human disease.
Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
(PLOS One, 2013-05-01) Singh-Blom, U. Martin; Natarajan, Nagarajan; Tewari, Ambuj; Woods, John O.; Dhillon, Inderjit S.; Marcotte, Edward M.
Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall. The authors want to thank Jon Laurent and Kris McGary for some of the data used, and Li and Patra for making their code available. Most of Ambuj Tewari's contribution to this work happened while he was a postdoctoral fellow at the University of Texas at Austin.
Prediction of gene–phenotype associations in humans, mice, and plants using phenologs
(BMC Bioinformatics, 2013-06-21) Woods, John O.; Singh-Blom, Ulf Martin; Laurent, Jon M.; McGary, Kriston L.; Marcotte, Edward M.
Background: Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such “orthologous phenotypes,” or “phenologs,” are examples of deep homology, and may be used to predict additional candidate disease genes. Results: In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data — from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans — establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene–phenotype associations, as for the Arabidopsis response to vernalization phenotype. Conclusions: We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.
Syndecan-1 Regulates Vasculat Smooth Muscle Cell Phenotype
(PLOS One, 2014-02-25) Chaterji, Somali; Lam, Christoffer H.; Ho, Derek S.; Proske, Daniel C.; Baker, Aaron B.
Objective: We examined the role of syndecan-1 in modulating the phenotype of vascular smooth muscle cells in the context of endogenous inflammatory factors and altered microenvironments that occur in disease or injury-induced vascular remodeling. Methods and Results: Vascular smooth muscle cells (vSMCs) display a continuum of phenotypes that can be altered during vascular remodeling. While the syndecans have emerged as powerful and complex regulators of cell function, their role in controlling vSMC phenotype is unknown. Here, we isolated vSMCs from wild type (WT) and syndecan-1 knockout (S1KO) mice. Gene expression and western blotting studies indicated decreased levels of α-smooth muscle actin (α-SMA), calponin, and other vSMC-specific differentiation markers in S1KO relative to WT cells. The spread area of the S1KO cells was found to be greater than WT cells, with a corresponding increase in focal adhesion formation, Src phosphorylation, and alterations in actin cytoskeletal arrangement. In addition, S1KO led to increased S6RP phosphorylation and decreased AKT and PKC-α phosphorylation. To examine whether these changes were present in vivo, isolated aortae from aged WT and S1KO mice were stained for calponin. Consistent with our in-vitro findings, the WT mice aortae stained higher for calponin relative to S1KO. When exposed to the inflammatory cytokine TNF-α, WT vSMCs had an 80% reduction in syndecan-1 expression. Further, with TNF-α, S1KO vSMCs produced increased pro-inflammatory cytokines relative to WT. Finally, inhibition of interactions between syndecan-1 and integrins αvβ3 and αvβ5 using the inhibitory peptide synstatin appeared to have similar effects on vSMCs as knocking out syndecan-1, with decreased expression of vSMC differentiation markers and increased expression of inflammatory cytokines, receptors, and osteopontin. Conclusions: Taken together, our results support that syndecan-1 promotes vSMC differentiation and quiescence. Thus, the presence of syndecan-1 would have a protective effect against vSMC dedifferentiation and this activity is linked to interactions with integrins αvβ3 and αvβ5.