Oliver J. Semmes

Lectin Capture Strategies Combined with Mass Spectrometry for the Discovery of Serum Glycoprotein Biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.

Proteomic approaches to biomarker discovery in prostate and bladder cancers

Proteomic technologies, including high resolution two‐dimensional electrophoresis (2‐DE), antibody/protein arrays, and advances in mass spectrometry (MS), are providing the tools needed to discover and identify disease associated biomarkers. Although application of these technologies to search for potential diagnostic/prognostic biomarkers asscociated with prostate and bladder cancer have been somewhat limited to date, proteins either overexpressed or underexpressed have been detected in both these urological cancers. Recent advances in mass spectrometry, especially platforms that permit rapid “fingerprint” profiling of multiple biomarkers, and tandem mass spectrometers for protein identification, will most assuredly enhance the discovery, identification, and characterization of potential cancer associated biomarkers. Furthermore, application of laser capture microdissection microscopes has provided a rapid and reproducible approach to procure pure populations of cells. This technology coupled to 2‐DE and MS has significantly aided the elucidation of the differential expression profiles between disease, benign and normal prostate and bladder cell populations. Finally, development and application of learning algorithms and bioinformatics to the data generated by these proteomic technologies will be essential in determining the clinical potential of a protein biomarker. The purpose of this review is to provide the reader with an overview of the application of these technologies in the search and identification of potential diagnostic/prognostic biomarkers for prostate and bladder cancers.

Restriction of Virus Infection but Not Catalytic dNTPase Activity Is Regulated by Phosphorylation of SAMHD1

ABSTRACT SAMHD1 is a host protein responsible, at least in part, for the inefficient infection of dendritic, myeloid, and resting T cells by HIV-1. Interestingly, HIV-2 and SIVsm viruses are able to counteract SAMHD1 by targeting it for proteasomal degradation using their Vpx proteins. It has been proposed that SAMHD1 is a dGTP-dependent deoxynucleoside triphosphohydrolase (dNTPase) that restricts HIV-1 by reducing cellular dNTP levels to below that required for reverse transcription. However, nothing is known about SAMHD1 posttranslational modifications and their potential role in regulating SAMHD1 function. We used 32P labeling and immunoblotting with phospho-specific antibodies to identify SAMHD1 as a phosphoprotein. Several amino acids in SAMHD1 were identified to be sites of phosphorylation using direct mass spectrometry. Mutation of these residues to alanine to prevent phosphorylation or to glutamic acid to mimic phosphorylation had no effect on the nuclear localization of SAMHD1 or its sensitivity to Vpx-mediated degradation. Furthermore, neither alanine nor glutamic acid substitutions had a significant effect on SAMHD1 dNTPase activity in an in vitro assay. Interestingly, however, we found that a T592E mutation, mimicking constitutive phosphorylation at a main phosphorylation site, severely affected the ability of SAMHD1 to restrict HIV-1 in a U937 cell-based restriction assay. In contrast, a T592A mutant was still capable of restricting HIV-1. These results indicate that SAMHD1 phosphorylation may be a negative regulator of SAMHD1 restriction activity. This conclusion is supported by our finding that SAMHD1 is hyperphosphorylated in monocytoid THP-1 cells under nonrestrictive conditions.

LUNG CANCER BIOMARKERS

The molecular characterization of lung cancer has changed the classification and treatment of these tumors, becoming an essential component of pathologic diagnosis and oncologic therapy decisions. Through the recognition of novel biomarkers, such as epidermal growth factor receptor mutations and anaplastic lymphoma kinase translocations, it is possible to identify subsets of patients who benefit from targeted molecular therapies. The success of targeted anticancer therapies and new immunotherapy approaches has created a new paradigm of personalized therapy and has led to accelerated development of new drugs for lung cancer treatment. This article focuses on clinically relevant cancer biomarkers as targets for therapy and potential new targets for drug development.

Methyltransferase PRMT1 Is a Binding Partner of HBx and a Negative Regulator of Hepatitis B Virus Transcription

ABSTRACT The hepatitis B virus X protein (HBx) is essential for virus replication and has been implicated in the development of liver cancer. HBx is recruited to viral and cellular promoters and activates transcription by interacting with transcription factors and coactivators. Here, we purified HBx-associated factors in nuclear extracts from HepG2 hepatoma cells and identified protein arginine methyltransferase 1 (PRMT1) as a novel HBx-interacting protein. We showed that PRMT1 overexpression reduced the transcription of hepatitis B virus (HBV), and this inhibition was dependent on the methyltransferase function of PRMT1. Conversely, depletion of PRMT1 correlated with increased HBV transcription. Using a quantitative chromatin immunoprecipitation assay, we found that PRMT1 is recruited to HBV DNA, suggesting a direct effect of PRMT1 on the regulation of HBV transcription. Finally, we showed that HBx expression inhibited PRMT1-mediated protein methylation. Downregulation of PRMT1 activity was further observed in HBV-replicating cells in an in vivo animal model. Altogether, our results support the notion that the binding of HBx to PRMT1 might benefit viral replication by relieving the inhibitory activity of PRMT1 on HBV transcription.

BclAF1 restriction factor is neutralized by proteasomal degradation and microRNA repression during human cytomegalovirus infection.

Cell proteins can restrict the replication of viruses. Here, we identify the cellular BclAF1 protein as a human cytomegalovirus restriction factor and describe two independent mechanisms the virus uses to decrease its steady-state levels. Immediately following infection, the viral pp71 and UL35 proteins, which are delivered to cells within virions, direct the proteasomal degradation of BclAF1. Although BclAF1 reaccumulates through the middle stages of infection, it is subsequently down-regulated at late times by miR-UL112-1, a virus-encoded microRNA. In the absence of BclAF1 neutralization, viral gene expression and replication are inhibited. These data identify two temporally and mechanistically distinct functions used by human cytomegalovirus to down-regulate a cellular antiviral protein.

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

BackgroundWe have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process.ResultsWe first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network.ConclusionThe interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.

A proteomic study of TAR-RNA binding protein (TRBP)-associated factors

BackgroundThe human TAR RNA-binding protein, TRBP, was first identified and cloned based on its high affinity binding to the small hairpin trans-activation responsive (TAR) RNA of HIV-1. TRBP has more recently been found to be a constituent of the RNA-induced silencing complex (RISC) serving as a Dicer co-factor in the processing of the ~70 nucleotide pre-microRNAs(miRNAs) to 21-25 nucleotide mature miRNAs.FindingsUsing co-immunoprecipitation and protein-identification by mass spectrometry, we characterized intracellular proteins that complex with TRBP. These interacting proteins include those that have been described to act in protein synthesis, RNA modifications and processing, DNA transcription, and cell proliferation.ConclusionsOur findings provide a proteome of factors that may cooperate with TRBP in activities such as miRNA processing and in RNA interference by the RISC complex.

Divergent subcellular locations of HTLV-I tax and Int-6: A contrast between in vitro protein-protein binding and intracellular protein colocalization

Protein-protein interactions define many important molecular and cellular processes in prokaryotic and eukaryotic biology. In trying to delineate the contact between two proteins, the yeast two-hybrid assay has emerged as a powerful technique. Complementing the yeast two-hybrid assay are in vitro techniques (e.g. GST-fusion-protein chromatography) that can also yield information on protein-protein associations. However, unambiguous functional significance to these interactions is best supported through a finding of colocalization of two proteins inside cells. In instances where two proteins interact in vitro but have divergent localizations within cells one needs to reconsider the biological importance of the former finding. Here, we present evidence for different subcellular locations of HTLV-I Tax and the Int-6 protein. We suggest a reexploration of the functional significance between Tax and Int-6 in cellular transformation.

A Bayesian network approach to feature selection in mass spectrometry data

BackgroundTime-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates.ResultsThe method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally.ConclusionsThis method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides additional information about the relationships among features that is useful to guide further biochemical analysis. In addition, when used to classify new data, these feature sets are far more consistent than those produced by many traditional techniques.