Functional Clustering Algorithm for High-Dimensional Proteomics Data

Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks) is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA) to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type 1 (HTLV-1)-infected patients samples

The Sumo-targeted ubiquitin ligase RNF4 regulates the localization and function of the HTLV-1 oncoprotein Tax

The Really Interesting New Gene (RING) Finger Protein 4 (RNF4) represents a class of ubiquitin ligases that target Small Ubiquitin-like Modifier (SUMO)–modified proteins for ubiquitin modification. To date, the regulatory function of RNF4 appears to be ubiquitin-mediated degradation of sumoylated cellular proteins. In the present study, we show that the Human T-cell Leukemia Virus Type 1 (HTLV-1) oncoprotein Tax is a substrate for RNF4 both in vivo and in vitro. We mapped the RNF4-binding site to a region adjacent to the Tax ubiquitin/SUMO modification sites K280/K284. Interestingly, RNF4 modification of Tax protein results in relocalization of the oncoprotein from the nucleus to the cytoplasm. Overexpression of RNF4, but not the RNF4 RING mutant, resulted in cytoplasmic enrichment of Tax. The RNF4-induced nucleus-to-cytoplasm relocalization was associated with increased NF-κB–mediated and decreased cAMP Response Element-Binding (CREB)–mediated Tax activity. Finally, depletion of RNF4 by RNAi prevented the DNA damage–induced nuclear/cytoplasmic translocation of Tax. These results provide important new insight into STUbL-mediated pathways that regulate the subcellular localization and functional dynamics of viral oncogenes

Retrotransposition-Competent Human LINE-1 Induces Apoptosis in Cancer Cells With Intact p53

Retrotransposition of human LINE-1 (L1) element, a major representative non-LTR retrotransposon in the human genome, is known to be a source of insertional mutagenesis. However, nothing is known about effects of L1 retrotransposition on cell growth and differentiation. To investigate the potential for such biological effects and the impact that human L1 retrotransposition has upon cancer cell growth, we examined a panel of human L1 transformed cell lines following a complete retrotransposition process. The results demonstrated that transposition of L1 leads to the activation of the p53-mediated apoptotic pathway in human cancer cells that possess a wild-type p53. In addition, we found that inactivation of p53 in cells, where L1 was undergoing retrotransposition, inhibited the induction of apoptosis. This suggests an association between active retrotransposition and a competent p53 response in which induction of apoptosis is a major outcome. These data are consistent with a model in which human retrotransposition is sensed by the cell as a “genetic damaging event” and that massive retrotransposition triggers signaling pathways resulting in apoptosis

Phosphorylation regulates human T-cell leukemia virus type 1 Rex function

Background: Human T-cell leukemia virus type 1 (HTLV-1) is a pathogenic complex deltaretrovirus, which is the causative agent of adult T-cell leukemia/lymphoma (ATL) and HTLV- 1-associated myelopathy/tropical spastic paraparesis. In addition to the structural and enzymatic viral gene products, HTLV-1 encodes the positive regulatory proteins Tax and Rex along with viral accessory proteins. Tax and Rex proteins orchestrate the timely expression of viral genes important in viral replication and cellular transformation. Rex is a nucleolar-localizing shuttling protein that acts post-transcriptionally by binding and facilitating the export of the unspliced and incompletely spliced viral mRNAs from the nucleus to the cytoplasm. HTLV-1 Rex (Rex-1) is a phosphoprotein and general protein kinase inhibition correlates with reduced function. Therefore, it has been proposed that Rex-1 function may be regulated through site-specific phosphorylation. Results: We conducted a phosphoryl mapping of Rex-1 over-expressed in transfected 293 T cells using a combination of affinity purification and liquid chromatography tandem mass spectrometry. We achieved 100% physical coverage of the Rex-1 polypeptide and identified five novel phosphorylation sites at Thr-22, Ser-36, Thr-37, Ser-97, and Ser-106. We also confirmed evidence of two previously identified residues, Ser-70 and Thr-174, but found no evidence of phosphorylation at Ser-177. The functional significance of these phosphorylation events was evaluated using a Rex reporter assay and site-directed mutational analysis. Our results indicate that phosphorylation at Ser-97 and Thr-174 is critical for Rex-1 function. Conclusion: We have mapped completely the site-specific phosphorylation of Rex-1 identifying a total of seven residues; Thr-22, Ser-36, Thr-37, Ser-70, Ser-97, Ser-106, and Thr-174. Overall, this work is the first to completely map the phosphorylation sites in Rex-1 and provides important insight into the regulation of Rex-1 function

Computational protein biomarker prediction: a case study for prostate cancer

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions

Direct N-glycosylation profiling of urine and prostatic fluid glycoproteins and extracellular vesicles

Expressed prostatic secretions (EPS), also called post digital rectal exam urines, are proximal fluids of the prostate that are widely used for diagnostic and prognostic assays for prostate cancer. These fluids contain an abundant number of glycoproteins and extracellular vesicles secreted by the prostate gland, and the ability to detect changes in their N-glycans composition as a reflection of disease state represents potential new biomarker candidates. Methods to characterize these N-glycan constituents directly from clinical samples in a timely manner and with minimal sample processing requirements are not currently available. In this report, an approach is described to directly profile the N-glycan constituents of EPS urine samples, prostatic fluids and urine using imaging mass spectrometry for detection. An amine reactive slide is used to immobilize glycoproteins from a few microliters of spotted samples, followed by peptide N-glycosidase digestion. Over 100 N-glycan compositions can be detected with this method, and it works with urine, urine EPS, prostatic fluids, and urine EPS-derived extracellular vesicles. A comparison of the N-glycans detected from the fluids with tissue N-glycans from prostate cancer tissues was done, indicating a subset of N-glycans present in fluids derived from the gland lumens. The developed N-glycan profiling is amenable to analysis of larger clinical cohorts and adaptable to other biofluids