Integrating site-specific peptide reporters and targeted mass spectrometry enables rapid substrate-specific kinase assay at the nanogram cell level.

Dysregulation of phosphorylation-mediated signaling drives the initiation and progression of many diseases. A substrate-specific kinase assay capable of quantifying the altered site-specific phosphorylation of its phenotype-dependent substrates provides better specificity to monitor a disease state. We report a sensitive and rapid substrate-specific kinase assay by integrating site-specific peptide reporter and multiple reaction monitoring (MRM)-MS platform for relative and absolute quantification of substrate-specific kinase activity at the sensitivity of nanomolar kinase and nanogram cell lysate. Using non-small cell lung cancer as a proof-of-concept, three substrate peptides selected from constitutive phosphorylation in tumors (HDGF-S165, RALY-S135, and NRD1-S94) were designed to demonstrate the feasibility. The assay showed good accuracy (<15% nominal deviation) and reproducibility (<15% CV). In PC9 cells, the measured activity for HDGF-S165 was 3.2 ± 0.2 fmol µg-1 min-1, while RALY-S135 and NRD1-S94 showed 4- and 20-fold higher activity at the sensitivity of 25 ng and 5 ng lysate, respectively, suggesting different endogenous kinases for each substrate peptide. Without the conventional shotgun phosphoproteomics workflow, the overall pipeline from cell lysate to MS data acquisition only takes 3 h. The multiplexed analysis revealed differences in the phenotype-dependent substrate phosphorylation profiles across six NSCLC cell lines and suggested a potential association of HDGF-S165 and NRD1-S94 with TKI resistance. With the ease of design, sensitivity, accuracy, and reproducibility, this approach may offer rapid and sensitive assays for targeted quantification of the multiplexed substrate-specific kinase activity of small amounts of sample.

Integrating proteomics with electrochemistry for identifying kinase biomarkers.

We present an integrated approach for highly sensitive identification and validation of substrate-specific kinases as cancer biomarkers. Our approach combines phosphoproteomics for high throughput cancer-related biomarker discovery from patient tissues and an impedimetric kinase activity biosensor for sensitive validation. Using non-small-cell lung cancer (NSCLC) as a proof-of-concept study, label-free quantitative phosphoproteomic analysis of a pair of cancerous and its adjacent normal tissues revealed 198 phosphoproteins that are over-phosphorylated in NSCLC. Among the differentially regulated phosphorylation sites, the most significant alteration was in residue S165 in the Hepatoma Derived Growth Factor (HDGF) protein. Hence, HDGF was selected as a model system for the electrochemical studies. Further motif-based analysis of this altered phosphorylation site revealed that extracellular-signal-regulated kinase 1/2 (ERK1/2) are most likely to be the corresponding kinases. For validation of the kinase-substrate pair, densely packed peptide monolayers corresponding to the HDGF phosphorylation site were coupled to a gold electrode. Phosphorylation of the monolayer by ERK2 and dephosphorylation by alkaline phosphatase (AP) were detected by electrochemical impedance spectroscopy (EIS) and surface roughness analysis. Compared to other methods for quantification of kinase concentration, this label-free electrochemical assay offers the advantages of ultra-sensitivity as well as higher specificity for the detection of cancer-related kinase-substrate pair. With implementation of multiple kinase-substrate biomarker pairs, we expect this integrated approach to become a high throughput platform for discovery and validation of phosphorylation-mediated biomarkers.

LiverCancerMarkerRIF: a liver cancer biomarker interactive curation system combining text mining and expert annotations.

Biomarkers are biomolecules in the human body that can indicate disease states and abnormal biological processes. Biomarkers are often used during clinical trials to identify patients with cancers. Although biomedical research related to biomarkers has increased over the years and substantial effort has been expended to obtain results in these studies, the specific results obtained often contain ambiguities, and the results might contradict each other. Therefore, the information gathered from these studies must be appropriately integrated and organized to facilitate experimentation on biomarkers. In this study, we used liver cancer as the target and developed a text-mining-based curation system named LiverCancerMarkerRIF, which allows users to retrieve biomarker-related narrations and curators to curate supporting evidence on liver cancer biomarkers directly while browsing PubMed. In contrast to most of the other curation tools that require curators to navigate away from PubMed and accommodate distinct user interfaces or Web sites to complete the curation process, our system provides a user-friendly method for accessing text-mining-aided information and a concise interface to assist curators while they remain at the PubMed Web site. Biomedical text-mining techniques are applied to automatically recognize biomedical concepts such as genes, microRNA, diseases and investigative technologies, which can be used to evaluate the potential of a certain gene as a biomarker. Through the participation in the BioCreative IV user-interactive task, we examined the feasibility of using this novel type of augmented browsing-based curation method, and collaborated with curators to curate biomarker evidential sentences related to liver cancer. The positive feedback received from curators indicates that the proposed method can be effectively used for curation. A publicly available online database containing all the aforementioned information has been constructed at http://btm.tmu.edu.tw/livercancermarkerrif in an attempt to facilitate biomarker-related studies. DATABASE URL: http://btm.tmu.edu.tw/LiverCancerMarkerRIF/