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A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

ABSTRACT: Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to systematically map the drug-protein interactions between Daclatasvir and NS5A and profile genetic barriers to Daclatasvir resistance. We implemented saturation mutagenesis in combination with next-generation sequencing technology to systematically quantify the effect of every possible amino acid substitution in the drug-targeted region (domain IA of NS5A) on replication fitness and sensitivity to Daclatasvir. This enabled determination of the residues governing drug-protein interactions. The relative fitness and drug sensitivity profiles also provide a comprehensive reference of the genetic barriers for all possible single amino acid changes during viral evolution, which we utilized to predict clinical outcomes using mathematical models. We envision that this high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.

SUBMITTER: Qi H

PROVIDER: S-EPMC3983061 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

Qi Hangfei H Olson C Anders CA Wu Nicholas C NC Ke Ruian R Loverdo Claude C Chu Virginia V Truong Shawna S Remenyi Roland R Chen Zugen Z Du Yushen Y Su Sheng-Yao SY Al-Mawsawi Laith Q LQ Wu Ting-Ting TT Chen Shu-Hua SH Lin Chung-Yen CY Zhong Weidong W Lloyd-Smith James O JO Sun Ren R

PLoS pathogens 20140410 4

Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to ...[more]

PMID: 24722365

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Project description:BackgroundThe P21 activated kinases (PAK) are frequently dysregulated in cancer and have central roles in oncogenic signalling, prompting the development of PAK inhibitors (PAKi) as anticancer agents. However, such compounds have not reached clinical use because, at least partially, there is a limited mechanistic understanding of their mode of action. Here, we aimed to characterize functional and molecular responses to PAKi (PF-3758309, FRAX-486 and IPA-3) in multiple acute myeloid leukaemia (AML) models to gain insights on the biochemical pathways affected by these inhibitors in this disease and identify determinants of response in patient samples.MethodsWe mined phosphoproteomic datasets of primary AML, and used proteomics and phosphoproteomics to profile PAKi impact in immortalized (P31/Fuj and MV4-11), and primary AML cells from 8 AML patients. These omics datasets were integrated with gene dependency data to identify which proteins targeted by PAKi are necessary for the proliferation of AML. We studied the effect PAKi on cell cycle progression, proliferation, differentiation and apoptosis. Finally, we used phosphoproteomics data as input for machine learning models that predicted ex vivo response in two independent datasets of primary AML cells (with 36 and 50 cases, respectively) to PF-3758309 and identify markers of response.ResultsWe found that PAK1 activation- measured from phosphoproteomics data- was predictive of poor prognosis in primary AML cases. PF-3758309 was the most effective PAKi in reducing proliferation and inducing apoptosis in AML cell lines. In cell lines and primary cells, PF-3758309 inhibited PAK, AMPK and PKCA activities, reduced c-MYC transcriptional activity and the expression of ribosomal proteins, and targeted the FLT3 pathway in FLT3-ITD mutated cells. In primary cells, PF-3758309 reduced STAT5 phosphorylation at Tyr699. Functionally, PF-3758309 reduced cell-growth, induced apoptosis, blocked cell cycle progression and promoted differentiation in a model-dependent manner. ML modelling accurately classified primary AML samples as sensitive or resistant to PF-3758309 ex vivo treatment, and highlighted PHF2 phosphorylation at Ser705 as a robust response biomarker.ConclusionsIn summary, our data define the proteomic, molecular and functional responses of primary and immortalised AML cells to PF-3758309 and suggest a route to personalise AML treatments based on PAK inhibitors.

Project description:Background The p21 activated kinases (PAK) are frequently dysregulated in cancer and have central roles in oncogenic signalling, prompting the development of PAK inhibitors (PAKi) as anticancer agents. However, such compounds have not reached clinical use because, at least partially, there is a limited mechanistic understanding of their mode of action. Here, we aimed to characterize functional and molecular responses to PAK inhibitors (PF-3758309, FRAX-486 and IPA-3) in multiple AML models to gain mechanistic insights on how these inhibitors work in this disease and identify determinants of response in patient samples. Methods We used proteomics and phosphoproteomics to profile PAKi impact on protein phosphorylation and expression and kinase activity in P31/Fuj and MV4-11 AML cells, and primary cells from 8 AML patients. We also integrated gene dependency data with phosphoproteomics and proteomics to identify which proteins targeted by PAKi are necessary for the proliferation of AML. We studied the effect PAKi on cell cycle progression, proliferation, differentiation and apoptosis. Finally, we used phosphoproteomics data as input for machine learning models that predicted “ex vivo” response of primary AML cells to PF-3758309 and identify markers of response. Results PF-3758309 was the most effective PAKi in reducing proliferation and inducing apoptosis in AML. PF-3758309 inhibited PAK, AMPK and PKCA activities in all AML cell lines and primary cells tested. This compound reduced the phosphorylation and expression of c-MYC and multiple ribosomal proteins in both cell lines and targeted the FLT3 pathway in FLT3-ITD mutated cells. In primary cells, PF-3758309 reduced STAT5 phosphorylation at Y699 in models with high phosphorylation on this site. Functionally, PF-3758309 reduced cell-growth, induced apoptosis, blocked the cell cycle progression and promoted differentiation in a model-dependent manner. ML modelling accurately classified primary AML samples as sensitive or resistant to PF-3758309 “ex vivo” treatment. Analysis of the models highlighted phosphorylation of PFH2 at Ser705 as a potential biomarker of response to PF-3758309. Conclusions In summary, our data define the proteomic, molecular and functional response of AML cells to PF-3758309 and suggest a route to personalise treatments in AML based on PAK inhibitors.

Dataset Information

A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

Publications

A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

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