Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

<p>Abstract</p> <p>Background</p> <p>Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.</p> <p>Methods</p> <p>Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.</p> <p>Results</p> <p>A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).</p> <p>Conclusion</p> <p>Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.</p

Profiling and Relative Quantitation of Phosphoinositides by Multiple Precursor Ion Scanning Based on Phosphate Methylation and Isotopic Labeling

Phosphoinositides, the phosphorylated derivatives of phosphatidylinositol (PtdIns), are key regulators of many fundamental biological processes, including cell growth, proliferation, and motility. Here, we present a novel method for rapid, sensitive, and simultaneous profiling of phosphatidylinositol trisphosphate (PtdInsP₃), phosphatidylinositol bisphosphate (PtdInsP₂), and phosphatidylinositol phosphate (PtdInsP) of different fatty acid compositions. This method is based on a technique called “charged diacylglycerol fragment ion-specific multiple precursor ion scanning” (DAG⁺-specific MPIS), coupled with prior phosphate methylation. Using DAG⁺-specific MPIS, we were able to identify 32 PtdIns, 28 PtdInsP, 30 PtdInsP₂, and 3 PtdInsP₃ molecular species from bovine brain extracts or prostatic cancer cell lines in an efficient and time-saving manner. Our analysis revealed a large range of fatty acyl compositions in phosphoinositides not obtained previously from mammalian samples. We also developed a method that involves isotopic labeling of endogenous phosphoinositides with deuterated diazomethane (CD₂N₂) for quantitation of phosphoinositides. CD₂N₂ was generated in situ through acid-catalyzed H/D exchange and methanolysis of trimethylsilyl diazomethane (TMS-diazomethane). Phosphoinositides, extracted from a PC3 prostatic cancer cell line, were labeled either with CH₂N₂ or CD₂N₂ and mixed in known proportions for DAG⁺-specific MPIS-based mass spectrometry (MS) analysis. The results indicate that isotopic labeling is capable of providing accurate quantitation of PtdInsP₃, PtdInsP₂, and PtdInsP with adequate linearity as well as high reproducibility with an average coefficient variation of 18.9%. More importantly, this new methods excluded the need for multiple phosphoinositide internal standards. DAG⁺-specific MPIS and isotopic labeling based MS analysis of phosphoinositides offers unique advantages over existing approaches and presents a powerful tool for research of phosphoinositide metabolism

Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomic methods have been widely used to identify lysine acylation proteins. However, these experimental approaches often fail to detect proteins that are in low abundance or absent in specific biological samples. To circumvent these problems, we developed a computational method to predict lysine acylation, including acetylation, malonylation, succinylation, and glutarylation. The prediction algorithm integrated flanking primary sequence determinants and evolutionary conservation of acylated lysine as well as multiple protein functional annotation features including gene ontology, conserved domains, and protein–protein interactions. The inclusion of functional annotation features increases predictive power oversimple sequence considerations for four of the acylation species evaluated. For example, the Matthews correlation coefficient (MCC) for the prediction of malonylation increased from 0.26 to 0.73. The performance of prediction was validated against an independent data set for malonylation. Likewise, when tested with independent data sets, the algorithm displayed improved sensitivity and specificity over existing methods. Experimental validation by Western blot experiments and LC–MS/MS detection further attested to the performance of prediction. We then applied our algorithm on to the mouse proteome and reported the global-scale prediction of lysine acetylation, malonylation, succinylation, and glutarylation, which should serve as a valuable resource for future functional studies

Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomic methods have been widely used to identify lysine acylation proteins. However, these experimental approaches often fail to detect proteins that are in low abundance or absent in specific biological samples. To circumvent these problems, we developed a computational method to predict lysine acylation, including acetylation, malonylation, succinylation, and glutarylation. The prediction algorithm integrated flanking primary sequence determinants and evolutionary conservation of acylated lysine as well as multiple protein functional annotation features including gene ontology, conserved domains, and protein–protein interactions. The inclusion of functional annotation features increases predictive power oversimple sequence considerations for four of the acylation species evaluated. For example, the Matthews correlation coefficient (MCC) for the prediction of malonylation increased from 0.26 to 0.73. The performance of prediction was validated against an independent data set for malonylation. Likewise, when tested with independent data sets, the algorithm displayed improved sensitivity and specificity over existing methods. Experimental validation by Western blot experiments and LC–MS/MS detection further attested to the performance of prediction. We then applied our algorithm on to the mouse proteome and reported the global-scale prediction of lysine acetylation, malonylation, succinylation, and glutarylation, which should serve as a valuable resource for future functional studies

Proteomic Comparison and MRM-Based Comparative Analysis of Metabolites Reveal Metabolic Shift in Human Prostate Cancer Cell Lines

One of the major challenges in prostate cancer therapy remains the development of effective treatments for castration-resistant prostate cancer (CRPC), as the underlying mechanisms for its progression remain elusive. Previous studies showed that androgen receptor (AR) is crucially involved in regulation of metabolism in prostate cancer (PCa) cells throughout the transition from early stage, androgen-sensitive PCa to androgen-independent CRPC. AR achieves such metabolic rewiring directively either via its transcriptional activity or via interactions with AMP-activated protein kinase (AMPK). However, due to the heterogeneous expression and activity status of AR in PCa cells, it remains a challenge to investigate the links between AR status and metabolic alterations. To this end, we compared the proteomes of three pairs of androgen-sensitive (AS) and androgen-independent (AI) PCa cell lines, namely, PC3-AR⁺/PC3, 22Rv1/Du145, and LNCaP/C42B, using an iTRAQ labeling approach. Our results revealed that most of the differentially expressed proteins between each pair function in metabolism, indicating a metabolic shift between AS and AI cells, as further validated by multiple reaction monitoring (MRM)-based quantification of nucleotides and relative comparison of fatty acids between these cell lines. Furthermore, increased adenylate kinase isoenzyme 1 (AK1) in AS relative to AI cells may result in activation of AMPK, representing a major regulatory factor involved in the observed metabolic shift in PCa cells