Gamma-tocotrienol as an effective agent in targeting prostate cancer stem cell-like population.

Emerging evidence supports that prostate cancer originates from a rare subpopulation of cells, namely prostate cancer stem cells (CSCs). Conventional therapies for prostate cancer are believed to mainly target the majority of differentiated tumor cells but spare CSCs, which may account for the subsequent disease relapse after treatment. Therefore, successful elimination of CSCs may be an effective strategy to achieve complete remission from this disease. Gamma-tocotrienols (γ-T3) is one of the vitamin-E constituents, which have been shown to have anticancer effects against a wide range of human cancers. Recently, we have reported that γ-T3 treatment not only inhibits prostate cancer cell invasion but also sensitizes the cells to docetaxel-induced apoptosis, suggesting that γ-T3 may be an effective therapeutic agent against advanced stage prostate cancer. Here, we demonstrate for the first time that γ-T3 can downregulate the expression of prostate CSC markers (CD133/CD44) in androgen-independent prostate cancer cell lines (PC-3 and DU145), as evident from Western blotting analysis. Meanwhile, the spheroid formation ability of the prostate cancer cells was significantly hampered by γ-T3 treatment. In addition, pretreatment of PC-3 cells with γ-T3 was found to suppress tumor initiation ability of the cells. More importantly, although CD133-enriched PC-3 cells were highly resistant to docetaxel treatment, these cells were as sensitive to γ-T3 treatment as the CD133-depleted population. Our data suggest that γ-T3 may be an effective agent in targeting prostate CSCs, which may account for its anticancer and chemosensitizing effects reported in previous studies.

Evidence of gamma-tocotrienol as an apoptosis-inducing, invasion-suppressing, and chemotherapy drug-sensitizing agent in human melanoma cells.

To date, the most effective cure for metastatic melanoma remains the surgical resection of the primary tumor. Recently, tocotrienol-rich-fraction has shown antiproliferative effect on cancer cells. To elucidate this anticancer property in malignant melanoma, this study aimed, first, to identify the most potent isomer for eliminating melanoma cells and second to decipher the molecular pathway responsible for its activity. Results showed that the inhibitory effect of gamma-tocotrienol was most potent, which resulted in induction of apoptosis as evidenced by activation of procaspases and the accumulation of sub-G1 cell population. Examination of the prosurvival genes revealed that the gamma-tocotrienol-induced cell death was associated with suppression of NF-kappaB, EGF-R, and Id family proteins. Meanwhile, gamma-tocotrienol treatment also resulted in induction of JNK signaling pathway, and inhibition of JNK activity by selective inhibitor was able to partially block the effect of gamma-tocotrienol. Interestingly, gamma-tocotrienol treatment led to suppression of mesenchymal markers and the restoration of E-cadherin and gamma-catenin expression, which was associated with suppression of cell invasion capability. Furthermore, synergistic effect was observed when cells were cotreated with gamma-tocotrienol and chemotherapy drugs. Together, our results demonstrated for the first time the anti-invasion and chemonsensitization effect of gamma-tocotrienol against human malignant melanoma cells.

Automated charge state determination of complex isotope-resolved mass spectra by peak-target Fourier transform.

This study describes a new algorithm for charge state determination of complex isotope-resolved mass spectra. This algorithm is based on peak-target Fourier transform (PTFT) of isotope packets. It is modified from the widely used Fourier transform method because Fourier transform may give ambiguous charge state assignment for low signal-to-noise ratio (S/N) or overlapping isotopic clusters. The PTFT algorithm applies a novel "folding" strategy to enhance peaks that are symmetrically spaced about the targeted peak before applying the FT. The "folding" strategy multiplies each point to the high-m/z side of the targeted peak by its counterpart on the low-m/z side. A Fourier transform of this "folded" spectrum is thus simplified, emphasizing the charge state of the "chosen" ion, whereas ions of other charge states contribute less to the transformed data. An intensity-dependent technique is also proposed for charge state determination from frequency signals. The performance of PTFT is demonstrated using experimental electrospray ionization Fourier transform ion cyclotron resonance mass spectra. The results show that PTFT is robust for charge state determination of low S/N and overlapping isotopic clusters, and also useful for manual verification of potential hidden isotopic clusters that may be missed by the current analysis algorithms, i.e., AID-MS or THRASH.

Molecular gene expression signature patterns for gastric cancer diagnosis.

It is an accepted clinical practice to diagnose gastric cancer by using histological techniques on tissue obtained through endoscopic biopsy. However, the use of these techniques often results in difficulty distinguishing between benign and malignant growth due to the ambiguous nature of some of the morphological features observed. In order to improve this situation, public domain gene expression data has been analysed and a set of molecular gene expression signatures has been discovered that distinguishes between normal and malignant growth. In addition, a separate distinct gene expression signature has been identified that appears to aid in the prognosis and indicate survival rates of patients. It is proposed that the use of the molecular gene expression signatures described in this manuscript when used in conjunction with the traditional histological techniques already in clinical practice will enhance and improve the diagnosis of gastric cancer.

Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays.

Gene transcription in a set of 49 human primary lung adenocarcinomas and 9 normal lung tissue samples was examined using Affymetrix GeneChip technology. A total of 3442 genes, called the set M AD, were found to be either up- or down-regulated by at least 2-fold between the two phenotypes. Genes assigned to a particular gene ontology term were found, in many cases, to be significantly unevenly distributed between the genes in and outside M AD. Terms that were overrepresented in M AD included functions directly implicated in the cancer cell metabolism. Based on their functional roles and expression profiles, genes in M AD were grouped into likely co-regulated gene sets. Highly conserved sequences in the 5 kb region upstream of the genes in these sets were identified with the motif discovery tool, MoDEL. Potential oncogenic transcription factors and their corresponding binding sites were identified in these conserved regions using the TRANSFAC 8.3 database. Several of the transcription factors identified in this study have been shown elsewhere to be involved in oncogenic processes. This study searched beyond phenotypic gene expression profiles in cancer cells, in order to identify the more important regulatory transcription factors that caused these aberrations in gene expression.

Molecular diagnosis of human cancer type by gene expression profiles and independent component analysis.

The precise diagnosis of cancer type based on microarray data is of particular importance and is also a challenging task. We have devised a novel pattern recognition procedure based on independent component analysis (ICA). Different from the conventional cancer classification methods, which are limited in their clinical applicability of cancer diagnosis, our method extracts explicitly, by ICA algorithm, a set of specific diagnostic patterns of normal and tumor tissues corresponding to a set of biomarkers for clinical use. We validated our procedure with the colon and prostate cancer data sets and achieved good diagnosis (>90%) on the data sets studied here. This technique is also suitable for the identification of diagnostic expression patterns for other human cancers and demonstrates the feasibility of simple and accurate molecular cancer diagnostics for clinical implementation.

Generation of predictive pharmacophore model for SARS-coronavirus main proteinase.

Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery. In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARS-CoV). Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugs containing the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. This means that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for further SARS-CoV main proteinase inhibitor development.

Putative structure and function of ORF3 in SARS coronavirus.

Based on molecular modeling techniques we constructed a rational 3D model of ORF3 in SARS coronavirus (SARS-CoV). Our studies suggest that the function of ORF3 could be involved in FAD/NAD binding according to its predicted structure and comparison with other structure neighbors. Furthermore, we identified three pairs of non-canonical N-H⋯π interactions in the structure of ORF3, which can make contributions to the stability of protein structure. These results provide important clues for better understanding of SARS-CoV ORF3 and trying new therapeutic strategies.

Relationship of SARS-CoV to other pathogenic RNA viruses explored by tetranucleotide usage profiling.

BACKGROUND: The exact origin of the cause of the Severe Acute Respiratory Syndrome (SARS) is still an open question. The genomic sequence relationship of SARS-CoV with 30 different single-stranded RNA (ssRNA) viruses of various families was studied using two non-standard approaches. Both approaches began with the vectorial profiling of the tetra-nucleotide usage pattern V for each virus. In approach one, a distance measure of a vector V, based on correlation coefficient was devised to construct a relationship tree by the neighbor-joining algorithm. In approach two, a multivariate factor analysis was performed to derive the embedded tetra-nucleotide usage patterns. These patterns were subsequently used to classify the selected viruses. RESULTS: Both approaches yielded relationship outcomes that are consistent with the known virus classification. They also indicated that the genome of RNA viruses from the same family conform to a specific pattern of word usage. Based on the correlation of the overall tetra-nucleotide usage patterns, the Transmissible Gastroenteritis Virus (TGV) and the Feline CoronaVirus (FCoV) are closest to SARS-CoV. Surprisingly also, the RNA viruses that do not go through a DNA stage displayed a remarkable discrimination against the CpG and UpA di-nucleotide (z = -77.31, -52.48 respectively) and selection for UpG and CpA (z = 65.79,49.99 respectively). Potential factors influencing these biases are discussed. CONCLUSION: The study of genomic word usage is a powerful method to classify RNA viruses. The congruence of the relationship outcomes with the known classification indicates that there exist phylogenetic signals in the tetra-nucleotide usage patterns, that is most prominent in the replicase open reading frames.

The 3D structure analysis of SARS-CoV S1 protein reveals a link to influenza virus neuraminidase and implications for drug and antibody discovery.

The spike protein of SARS-associated coronavirus (SARS-CoV) is an important target for anti-SARS drug discovery. Its S1 domain is responsible for receptor binding and SARS-CoV entry into cells. In this study, we constructed a rational 3D model for S1 domain of SARS-CoV spike protein by fold recognition and molecular modeling techniques. We found that there is a structure similarity between S1 protein and influenza virus neuraminidase. Our analyses suggest that the existing anti-influenza virus inhibitors and anti-neuraminidase antibody could be used as a starting point for designing anti-SARS drugs, vaccines and antibodies. Interestingly, our prediction for antibody is consistent with a recently experimental discovery of anti-SARS antibody.

Id1, inhibitor of differentiation, is a key protein mediating anti-tumor responses of gamma-tocotrienol in breast cancer cells.

Gamma-tocotrienol has demonstrated anti-proliferative effect on breast cancer (BCa) cells, but mechanisms involved are largely unknown. This study aimed at deciphering the molecular pathways responsible for its activity. Our results showed that treatment of BCa cells with gamma-tocotrienol resulted in induction of apoptosis as evidenced by activation of pro-caspases, accumulation of sub-G1 cells and DNA fragmentations. Examination of the pro-survival genes revealed that the gamma-tocotrienol-induced cell death was associated with suppression of Id1 and NF-kappaB through modulation of their upstream regulators (Src, Smad1/5/8, Fak and LOX). Meanwhile, gamma-tocotrienol treatment also resulted in the induction of JNK signaling pathway and inhibition of JNK activity by specific inhibitor partially blocked the effect of gamma-tocotrienol. Furthermore, synergistic effect was observed when cells were co-treated with gamma-tocotrienol and Docetaxel. Interestingly, in cells that treated with gamma-tocotrienol, alpha-tocopherol or beta-aminoproprionitrile were found to partially restore Id1 expression. Meanwhile, this restoration of Id1 was found to protect the cells from gamma-tocotrienol induced apoptosis. Consistent outcome was observed in cells ectopically transfected with the Id-1 gene. Our results suggested that the anti-proliferative and chemosensitization effect of gamma-tocotrienol on BCa cells may be mediated through downregulation of Id1 protein.

Exploring the binding mechanism of the main proteinase in SARS-associated coronavirus and its implication to anti-SARS drug design.

The main proteinase of SARS-associated coronavirus (SARS-CoV) plays an important role in viral transcription and replication, and is an attractive target for anti-SARS drug development. The important thing is to understand its binding mechanism with possible ligands. In this study, we investigated possible noncanonical interactions, potential inhibitors, and binding pockets in the main proteinase of SARS-CoV based on its recently determined crystal structure. These findings provide a wide clue to searching for anti-SARS drug. Interestingly, we found that similar structure patterns exist in SARS-CoV main proteinase with Poliovirus 3c Proteinase, Rhinovirus 3c Protease, Nsp4 Proteinase From Equine Arteritis Virus, Hepatitis C Virus Ns3 Protease, Hepatitis A Virus 3c Protease, and Dengue Virus Ns3 Protease. It suggests that the available drugs in these viruses could be used to fight SARS disease.

Old drugs as lead compounds for a new disease? Binding analysis of SARS coronavirus main proteinase with HIV, psychotic and parasite drugs.

The SARS-associated coronavirus (SARS-CoV) main proteinase is a key enzyme in viral polyprotein processing. To allow structure-based design of drugs directed at SARS-CoV main proteinase, we predicted its binding pockets and affinities with existing HIV, psychotic and parasite drugs (lopinavir, ritonavir, niclosamide and promazine), which show signs of inhibiting the replication of SARS-CoV. Our results suggest that these drugs and another two HIV inhibitors (PNU and UC2) could be used as templates for designing SARS-CoV proteinase inhibitors.