Pathguide: a Pathway Resource List

Bader, Gary D.; Cary, Michael P.; Sander, Chris

doi:10.1093/nar/gkj126

Cited by 393 publications

(248 citation statements)

References 5 publications

(6 reference statements)

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“…Subsequently, the top 750 ProTA hits were subjected to pathway analyses using BioCarta and KEGG databases that are specialized in signaling and metabolic pathways, respectively [40]. Results from BioCarta analysis suggest that BTZ stabilized CD36, MAPK14 and FOS in the pathway of "TSP-1 induced apoptosis in microvascular endothelial cell (mvEC)" (Table 1).…”

Section: Gene Ontology and Pathway Analysesmentioning

confidence: 99%

Profiling human protein degradome delineates cellular responses to proteasomal inhibition and reveals a feedback mechanism in regulating proteasome homeostasis

Tao

Yang

et al. 2014

Cell Res

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Global change in protein turnover (protein degradome) constitutes a central part of cellular responses to intrinsic or extrinsic stimuli. However, profiling protein degradome remains technically challenging. Recently, inhibition of the proteasome, e.g., by using bortezomib (BTZ), has emerged as a major chemotherapeutic strategy for treating multiple myeloma and other human malignancies, but systematic understanding of the mechanisms for BTZ drug action and tumor drug resistance is yet to be achieved. Here we developed and applied a dual-fluorescence-based Protein Turnover Assay (ProTA) to quantitatively profile global changes in human protein degradome upon BTZ-induced proteasomal inhibition. ProTA and subsequent network analyses delineate potential molecular basis for BTZ action and tumor drug resistance in BTZ chemotherapy. Finally, combined use of BTZ with drugs targeting the ProTA-identified key genes or pathways in BTZ action reduced BTZ resistance in multiple myeloma cells. Remarkably, BTZ stabilizes proteasome subunit PSMC1 and proteasome assembly factor PSMD10, suggesting a previously under-appreciated mechanism for regulating proteasome homeostasis. Therefore, ProTA is a novel tool for profiling human protein degradome to elucidate potential mechanisms of drug action and resistance, which might facilitate therapeutic development targeting proteostasis to treat human disorders.

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Section: Gene Ontology and Pathway Analysesmentioning

confidence: 99%

Profiling human protein degradome delineates cellular responses to proteasomal inhibition and reveals a feedback mechanism in regulating proteasome homeostasis

Tao

Yang

et al. 2014

Cell Res

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“…For example, Bradley et al uses pathway information as a prior, which can be beneficial for identification, but pathway knowledge can also be incomplete. Very few pathway databases combine interactions between multiple types of molecular features (Bader, 2006) except for KEGG (Kanehisa, 2000). Dawson et al implemented EBcoexpress, which uses Empirical Bayes estimation and returns a posterior probability of differential coexpression for each pair of molecular features.…”

Section: Introductionmentioning

confidence: 99%

The discordant method: a novel approach for differential correlation

2015

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Motivation: Current differential correlation methods are designed to determine molecular feature pairs that have the largest magnitude of difference between correlation coefficients. These methods do not easily capture molecular feature pairs that experience no correlation in one group but correlation in another, which may reflect certain types of biological interactions. We have developed a tool, the Discordant method, which categorizes the correlation types for each group to make this possible. Results: We compare the Discordant method to existing approaches using simulations and two biological datasets with different types of -omics data. In contrast to other methods, Discordant identifies phenotype-related features at a similar or higher rate while maintaining reasonable computational tractability and usability. Availability and implementation: R code and sample data are available at https://github.com/siskac/

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“…Currently, there are 132 PPI databases indexed by the Pathguide (Bader et al, 2006;accessed 23 Dec 2011). The quantity of physical interactions to date is 386,495 across all species when integrated among major 11 databases by the iRefWeb accessed 23 Dec 2011).…”

Section: Ppi Databasesmentioning

confidence: 99%