Articles Online (Volume 11, Issue 4)


Leveraging Metabolomics to Assess the Next Generation of Temozolomide-based Therapeutic Approaches for Glioblastomas

Patrick-Denis St-Coeur, Mohamed Touaibia, Miroslava Cuperlovic-Culf, Pier Jr Morin

Glioblastoma multiforme (GBM) is the most common adult primary tumor of the central nervous system. The current standard of care for glioblastoma patients involves a combination of surgery, radiotherapy and chemotherapy with the alkylating agent temozolomide. Several mechanisms underlying the inherent and acquired temozolomide resistance have been identified and contribute to treatment failure. Early identification of temozolomide-resistant GBM patients and improvement of the therapeutic strategies available to treat this malignancy are of uttermost importance. This review initially looks at the molecular pathways underlying GBM formation and development with a particular emphasis placed on recent therapeutic advances made in the field. Our focus will next be directed toward the molecular mechanisms modulating temozolomide resistance in GBM patients and the strategies envisioned to circumvent this resistance. Finally, we highlight the diagnostic and prognostic value of metabolomics in cancers and assess its potential usefulness in improving the current standard of care for GBM patients.

Page 199–206

Original Research

Exploring the Nicotinic Acetylcholine Receptor-associated Proteome with iTRAQ and Transgenic Mice

Tristan D. McClure-Begley, Kathy L. Stone, Michael J. Marks, Sharon R. Grady, Christopher M. Colangelo, Jon M. Lindstrom, Marina R. Picciotto

Neuronal nicotinic acetylcholine receptors (nAChRs) containing α4 and β2 subunits are the principal receptors in the mammalian central nervous system that bind nicotine with high affinity. These nAChRs are involved in nicotine dependence, mood disorders, neurodegeneration and neuroprotection. However, our understanding of the interactions between α4β2-containing (α4β2∗) nAChRs and other proteins remains limited. In this study, we identified proteins that interact with α4β2∗ nAChRs in a genedose dependent pattern by immunopurifying β2∗ nAChRs from mice that differ in α4 and β2 subunit expression and performing proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ). Reduced expression of either the α4 or the β2 subunit results in a correlated decline in the expression of a number of putative interacting proteins. We identified 208 proteins co-immunoprecipitated with these nAChRs. Furthermore, stratified linear regression analysis indicated that levels of 17 proteins was correlated significantly with expression of α4β2 nAChRs, including proteins involved in cytoskeletal rearrangement and calcium signaling. These findings represent the first application of quantitative proteomics to produce a β2∗ nAChR interactome and describe a novel technique used to discover potential targets for pharmacological manipulation of α4β2 nAChRs and their downstream signaling mechanisms.

Page 207–218

Original Research

In silico Proteome-wide Amino aCid and Elemental Composition (PACE) Analysis of Expression Proteomics Data Provides A Fingerprint of Dominant Metabolic Processes

David M. Good, Anwer Mamdoh, Harshavardhan Budamgunta, Roman A. Zubarev

Proteome-wide Amino aCid and Elemental composition (PACE) analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decomposition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements (C, H, N, O and S), with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differentiation between very similar proteomes. In addition, PACE provides novel insights into proteome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down-regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.

Page 219–229

Original Research

Quantitative Evaluation of Aldo–keto Reductase Expression in Hepatocellular Carcinoma (HCC) Cell Lines

Lei Yang, Ju Zhang, Shenyan Zhang, Weiwei Dong, Xiaomin Lou, Siqi Liu

The involvement of aldo–keto reductases (AKRs) in tumorigenesis is widely reported, but their roles in the pathological process are not generally recognized due to inconsistent measurements of their expression. To overcome this problem, we simultaneously employed real-time PCR to examine gene expression and multiple reaction monitoring (MRM) of mass spectrometry (MS) to examine the protein expression of AKRs in five different hepatic cell lines. These include one relatively normal hepatic cell line, L-02, and four hepatocellular carcinoma (HCC) cell lines, HepG2, HuH7, BEL7402 and SMMC7721. The results of real-time PCR showed that expression of genes encoding the AKR1C family members rather than AKR1A and AKR1B was associated with tumor, and most of genes encoding AKRs were highly expressed in HuH7. Similar observations were obtained through MRM. Different from HuH7, the protein abundance of AKR1A and AKR1B was relatively consistent among the other four hepatic cell lines, while protein expression of AKR1C varied significantly compared to L-02. Therefore, we conclude that the abundant distribution of AKR1C proteins is likely to be associated with liver tumorigenesis, and the AKR expression status in HuH7 is completely different from other liver cancer cell lines. This study, for the first time, provided both overall and quantitative information regarding the expression of AKRs at both mRNA and protein levels in hepatic cell lines. Our observations put the previous use of AKRs as a biomarker into question since it is only consistent with our data from HuH7. Furthermore, the data presented herein demonstrated that quantitative evaluation and comparisons within a protein family at both mRNA and protein levels were feasible using current techniques.

Page 230–240

Original Research

PepBind: A Comprehensive Database and Computational Tool for Analysis of Protein–peptide Interactions

Arindam Atanu Das, Om Prakash Sharma, Muthuvel Suresh Kumar, Ramadas Krishna, Premendu P. Mathur

Protein–peptide interactions, where one partner is a globular protein (domain) and the other is a flexible linear peptide, are key components of cellular processes predominantly in signaling and regulatory networks, hence are prime targets for drug design. To derive the details of the protein–peptide interaction mechanism is often a cumbersome task, though it can be made easier with the availability of specific databases and tools. The Peptide Binding Protein Database (PepBind) is a curated and searchable repository of the structures, sequences and experimental observations of 3100 protein–peptide complexes. The web interface contains a computational tool, protein inter-chain interaction (PICI), for computing several types of weak or strong interactions at the protein–peptide interaction interface and visualizing the identified interactions between residues in Jmol viewer. This initial database release focuses on providing protein–peptide interface information along with structure and sequence information for protein–peptide complexes deposited in the Protein Data Bank (PDB). Structures in PepBind are classified based on their cellular activity. More than 40% of the structures in the database are found to be involved in different regulatory pathways and nearly 20% in the immune system. These data indicate the importance of protein–peptide complexes in the regulation of cellular processes. PepBind is freely accessible at

Page 241–246

Application Note

A Computational Workflow to Identify Allele-specific Expression and Epigenetic Modification in Maize

Xiaoxing Wei, Xiangfeng Wang

Allele-specific expression refers to the preferential expression of one of the two alleles in a diploid genome, which has been thought largely attributable to the associated cis-element variation and allele-specific epigenetic modification patterns. Allele-specific expression may contribute to the heterosis (or hybrid vigor) effect in hybrid plants that are produced from crosses of closely-related species, subspecies and/or inbred lines. In this study, using Illumina high-throughput sequencing of maize transcriptomics, chromatic H3K27me3 histone modification and DNA methylation data, we developed a new computational framework to identify allele-specifically expressed genes by simultaneously tracking allele-specific gene expression patterns and the epigenetic modification landscape in the seedling tissues of hybrid maize. This approach relies on detecting nucleotide polymorphisms and any genomic structural variation between two parental genomes in order to distinguish paternally or maternally derived sequencing reads. This computational pipeline also incorporates a modified Chi-square test to statistically identify allele-specific gene expression and epigenetic modification based on the Poisson distribution.

Page 247–252

Meeting Report

Promote Connections of Young Computational Biologists in China

Shihua Zhang, Xiu-Jie Wang

Page 253–256