Title Information
Title
A Phosphoproteomic Study of Insulin Signaling Pathway Using A Novel High-Throughput Pipeline
Name: Personal
Name Part
Yu, Kebing
Role
Role Term: Text
creator
Origin Information
Copyright Date (keyDate="yes", encoding="w3cdtf")
2009
Physical Description
Extent
xvi, 131 p.
digitalOrigin
born digital
Note
Thesis (Ph.D.) -- Brown University (2010)
Name: Personal
Name Part
Salomon, Arthur
Role
Role Term: Text
director
Name: Personal
Name Part
Sun, Shouheng
Role
Role Term: Text
reader
Name: Personal
Name Part
Bazemore-Walker, Carthene
Role
Role Term: Text
reader
Name: Corporate
Name Part
Brown University. Chemistry
Role
Role Term: Text
sponsor
Genre (aat)
theses
Abstract
Recent advances in the speed and sensitivity of mass spectrometers and analytical methods, the exponential acceleration of computer powers, and the availability of genomic databases from an array of species have led to a deluge of proteomic data. Unfortunately, this enhancement has not been accompanied by a concomitant increase in the availability of tools allowing users to efficiently analyze these data. Often the manual aggregation and analysis of proteomic data in current software distract investigators from the biological meaning of their data, leading to the all-too-frequent deposition of data into scientific literature with little biological interpretation. We seek to fill the gap by providing a high-throughput autonomous proteomic analysis pipeline with the following critical components: liquid chromatography/mass spectrometry (LC/MS) acquisition control, peptide validation, quantitative data exploration, and protein network analysis. The automated LC/MS control tool provides reproducible and sensitive multi-dimensional sample analysis. Instrument acquired data are streamlined to a customized proteomic pipeline for database searching and post-acquisition calculation. The logistic spectral score we developed for high-throughput statistical validation of database assignment outperforms SEQUEST XCorr (3.4-fold more peptides) and X!Tandem E-Value (1.9-fold more peptides) at a 1% false discovery rate estimated by decoy database. All calculation results are directed into a relational database for organization of proteomic results, collation of experimental data with available protein information resources, and visual comparison of multiple proteomic experiments. This platform provides flexible adaptation to diverse workflows for individual proteomics labs and enables proteomic scientists to modify the presentation of the proteomic data, implement extra data-dependent analysis tasks and process additional input formats. The utility of this system is illustrated through analysis of insulin signaling pathway important to liver cancers. We explored changes in phosphorylation quantitatively in hIRS1-transfected NIH3T3 cells in response to insulin stimulation using a label-free/SILAC hybrid quantitation approach. In the NIH3T3-hIRS1/NIH3T3-hIRS1 Y1180F timecourse, we discovered 2201 phosphorylation sites at 1% false discovery rate, among which 84.6% were on Serine, 13.6% were on Threonine and 1.8% were on Tyrosine.
Subject (Local)
Topic
Signaling Pathway
Subject (Local)
Topic
Database
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1079785")
Topic
Proteomics
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/832181")
Topic
Bioinformatics
Subject (FAST) (authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1061482")
Topic
Phosphorylation
Record Information
Record Content Source (marcorg)
RPB
Record Creation Date (encoding="iso8601")
20091218
Language
Language Term: Code (ISO639-2B)
eng
Language Term: Text
English
Identifier: DOI
10.7301/Z0BP012C
Access Condition: rights statement (href="http://rightsstatements.org/vocab/InC/1.0/")
In Copyright
Access Condition: restriction on access
Collection is open for research.
Type of Resource (primo)
dissertations