Integrative subcellular proteomic analysis allows accurate prediction of human disease causing genes
- Li Zhao,
- Yiyun Chen,
- Amol Onkar Bajaj,
- Aiden Eblimit,
- Mingchu Xu,
- Zachry T Soens,
- Feng Wang,
- Zhongqi Ge,
- Sung Yun Jung,
- Feng He,
- Yumei Li,
- Theodore G Wensel,
- Jun Qin and
- Rui Chen1
- ↵* Corresponding author; email: ruichen{at}bcm.edu
Abstract
Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other datasets, it can greatly enhance our ability to predict gene function genome wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization and 84% accuracy is achieved comparing to experiment data. By integrating the protein OS enrichment score, the protein abundance and the retina transcriptome, the probability whether a gene play essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrated the synergy of combining subcellular fractionation proteomics with other omics datasets, and is generally applicable to other tissues and diseases.
- Received September 13, 2015.
- Accepted February 19, 2016.
- Published by Cold Spring Harbor Laboratory Press
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