Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels.

Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.

Protein quantitative trait loci identify novel candidates modulating cellular response to chemotherapy.

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p<0.001). Interestingly, GWAS SNPs from various regions of the genome implicated the same target protein (p<0.0001) that correlated with drug induced cytotoxicity or apoptosis (p ≤ 0.05). Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response. This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets. This approach, linking targeted proteomic data to variation in pharmacologic response, can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms.

Cholestane-3ß, 5α, 6ß-triol suppresses proliferation, migration, and invasion of human prostate cancer cells.

Oxysterols are oxidation products of cholesterol. Cholestane-3ß, 5α, 6ß-triol (abbreviated as triol) is one of the most abundant and active oxysterols. Here, we report that triol exhibits anti-cancer activity against human prostate cancer cells. Treatment of cells with triol dose-dependently suppressed proliferation of LNCaP CDXR-3, DU-145, and PC-3 human prostate cancer cells and reduced colony formation in soft agar. Oral administration of triol at 20 mg/kg daily for three weeks significantly retarded the growth of PC-3 xenografts in nude mice. Flow cytometric analysis revealed that triol treatment at 10-40 µM caused G1 cell cycle arrest while the TUNEL assay indicated that triol treatment at 20-40 µM induced apoptosis in all three cell lines. Micro-Western Arrays and traditional Western blotting methods indicated that triol treatment resulted in reduced expression of Akt1, phospho-Akt Ser473, phospho-Akt Thr308, PDK1, c-Myc, and Skp2 protein levels as well as accumulation of the cell cycle inhibitor p27(Kip). Triol treatment also resulted in reduced Akt1 protein expression in PC-3 xenografts. Overexpression of Skp2 in PC-3 cells partially rescued the growth inhibition caused by triol. Triol treatment suppressed migration and invasion of DU-145, PC-3, and CDXR-3 cells. The expression levels of proteins associated with epithelial-mesenchymal transition as well as focal adhesion kinase were affected by triol treatment in these cells. Triol treatment caused increased expression of E-cadherin protein levels but decreased expression of N-cadherin, vimentin, Slug, FAK, phospho-FAK Ser722, and phospho-FAK Tyr861 protein levels. Confocal laser microscopy revealed redistribution of ß-actin and α-tubulin at the periphery of the CDXR-3 and DU-145 cells. Our observations suggest that triol may represent a promising therapeutic agent for advanced metastatic prostate cancer.

Relating human genetic variation to variation in drug responses.

Although sequencing a single human genome was a monumental effort a decade ago, more than 1000 genomes have now been sequenced. The task ahead lies in transforming this information into personalized treatment strategies that are tailored to the unique genetics of each individual. One important aspect of personalized medicine is patient-to-patient variation in drug response. Pharmacogenomics addresses this issue by seeking to identify genetic contributors to human variation in drug efficacy and toxicity. Here, we present a summary of the current status of this field, which has evolved from studies of single candidate genes to comprehensive genome-wide analyses. Additionally, we discuss the major challenges in translating this knowledge into a systems-level understanding of drug physiology, with the ultimate goal of developing more effective personalized clinical treatment strategies.

Caffeic acid phenethyl ester suppresses the proliferation of human prostate cancer cells through inhibition of p70S6K and Akt signaling networks.

Caffeic acid phenethyl ester (CAPE) is a bioactive component derived from honeybee hive propolis. CAPE has been shown to have antimitogenic, anticarcinogenic, and other beneficial medicinal properties. Many of its effects have been shown to be mediated through its inhibition of NF-κB signaling pathways. We took a systematic approach to uncover the effects of CAPE from hours to days on the signaling networks in human prostate cancer cells. We observed that CAPE dosage dependently suppressed the proliferation of LNCaP, DU-145, and PC-3 human prostate cancer cells. Administration of CAPE by gavage significantly inhibited the tumor growth of LNCaP xenografts in nude mice. Using LNCaP cells as a model system, we examined the effect of CAPE on gene expression, protein signaling, and transcriptional regulatory networks using micro-Western arrays and PCR arrays. We built a model of the impact of CAPE on cell signaling which suggested that it acted through inhibition of Akt-related protein signaling networks. Overexpression of Akt1 or c-Myc, a downstream target of Akt signaling, significantly blocked the antiproliferative effects of CAPE. In summary, our results suggest that CAPE administration may be useful as an adjuvant therapy for prostate and potentially other types of cancers that are driven by the p70S6K and Akt signaling networks.

Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches.

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments. However, traditional antibody-based approaches are not optimal for analyzing large numbers of protein abundances and modifications across many samples. In this article, we will review recent advances in methodologies and philosophies behind several microarray-based, intermediate-level, 'protein-omic' methods, including a focus on reverse-phase lysate arrays and micro-western arrays, which have been helpful for bridging gaps between large- and small-scale protein analysis approaches and have provided insight into the roles that protein systems play in several biological processes.