Frances V. Fuller-Pace

Bio: Frances V. Fuller-Pace is an academic researcher from University of Dundee. The author has contributed to research in topics: RNA Helicase A & DDX5. The author has an hindex of 36, co-authored 61 publications receiving 4276 citations. Previous affiliations of Frances V. Fuller-Pace include Ninewells Hospital.

DExD/H box RNA helicases: multifunctional proteins with important roles in transcriptional regulation.

Abstract: The DExD/H box family of proteins includes a large number of proteins that play important roles in RNA metabolism. Members of this family have been shown to act as RNA helicases or unwindases, using the energy from ATP hydrolysis to unwind RNA structures or dissociate RNA–protein complexes in cellular processes that require modulation of RNA structures. However, it is clear that several members of this family are multifunctional and, in addition to acting as RNA helicases in processes such as pre-mRNA processing, play important roles in transcriptional regulation. In this review I shall concentrate on RNA helicase A (Dhx9), DP103 (Ddx20), p68 (Ddx5) and p72 (Ddx17), proteins for which there is a strong body of evidence showing that they play important roles in transcription, often as coactivators or corepressors through their interaction with key components of the transcriptional machinery, such as CREB-binding protein, p300, RNA polymerase II and histone deacetylases.

Phosphorylation of human estrogen receptor α at serine 118 by two distinct signal transduction pathways revealed by phosphorylation-specific antisera

Abstract: Phosphorylation of human estrogen receptor α at serine 118 by two distinct signal transduction pathways revealed by phosphorylation-specific antisera

The DEAD box protein p68: a novel transcriptional coactivator of the p53 tumour suppressor

Abstract: The DEAD box RNA helicase, p68, has been implicated in various cellular processes and has been shown to possess transcriptional coactivator function. Here, we show that p68 potently synergises with the p53 tumour suppressor protein to stimulate transcription from p53-dependent promoters and that endogenous p68 and p53 co-immunoprecipitate from nuclear extracts. Strikingly, RNAi suppression of p68 inhibits p53 target gene expression in response to DNA damage, as well as p53-dependent apoptosis, but does not influence p53 stabilisation or expression of non-p53-responsive genes. We also show, by chromatin immunoprecipitation, that p68 is recruited to the p21 promoter in a p53-dependent manner, consistent with a role in promoting transcriptional initiation. Interestingly, p68 knock-down does not significantly affect NF-κB activation, suggesting that the stimulation of p53 transcriptional activity is not due to a general transcription effect. This study represents the first report of the involvement of an RNA helicase in the p53 response, and highlights a novel mechanism by which p68 may act as a tumour cosuppressor in governing p53 transcriptional activity.

The RNA helicase p68 is a novel androgen receptor coactivator involved in splicing and is overexpressed in prostate cancer.

Abstract: The androgen receptor (AR) is a member of the nuclear steroid hormone receptor family and is thought to play an important role in the development of both androgen-dependent and androgen-independent prostatic malignancy. Elucidating roles by which cofactors regulate AR transcriptional activity may provide therapeutic advancement for prostate cancer (PCa). The DEAD box RNA helicase p68 (Ddx5) was identified as a novel AR-interacting protein by yeast two-hybrid screening, and we sought to examine the involvement of p68 in AR signaling and PCa. The p68-AR interaction was verified by colocalization of overexpressed protein by immunofluorescence and confirmed in vivo by coimmunoprecipitation in the PCa LNCaP cell line. Chromatin immunoprecipitation in the same cell line showed AR and p68 recruitment to the promoter region of the androgen-responsive prostate-specific antigen (PSA) gene. Luciferase reporter, minigene splicing assays, and RNA interference (RNAi) were used to examine a functional role of p68 in AR-regulated gene expression, whereby p68 targeted RNAi reduced AR-regulated PSA expression, and p68 enhanced AR-regulated repression of CD44 splicing (P = 0.008). Tyrosine phosphorylation of p68 was found to enhance coactivation of ligand-dependent transcription of AR-regulated luciferase reporters independent of ATP-binding. Finally, we observe increased frequency and expression of p68 in PCa compared with benign tissue using a comprehensive prostate tissue microarray (P = 0.003; P = 0.008). These findings implicate p68 as a novel AR transcriptional coactivator that is significantly overexpressed in PCa with a possible role in progression to hormone-refractory disease.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Flaviviridae :T he Viruses and Their Replication

Abstract: FLAVIVIRUSES 1103 Background and Classification 1103 Structure and Physical Properties of the Virion 1104 Binding and Entry 1105 Genome Structure 1106 Translation and Proteolytic Processing 1107 Features of the Structural Proteins 1108 Features of the Nonstructural Proteins 1109 RNA Replication 1112 Membrane Reorganization and the Compartmentalization of Flavivirus Replication 1112 Assembly and Release of Particles from Flavivirus-infected Cells 1112 Host Resistance to Flavivirus Infection 1113

A Randomized Trial Of Exemestane After Two To Three Years Of Tamoxifen Therapy In Postmenopausal Women With Primary Breast Cancer.

Abstract: background Tamoxifen, taken for five years, is the standard adjuvant treatment for postmenopausal women with primary, estrogen-receptor–positive breast cancer. Despite this treatment, however, some patients have a relapse. methods We conducted a double-blind, randomized trial to test whether, after two to three years of tamoxifen therapy, switching to exemestane was more effective than continuing tamoxifen therapy for the remainder of the five years of treatment. The primary end point was disease-free survival. results Of the 4742 patients enrolled, 2362 were randomly assigned to switch to exemestane, and 2380 to continue to receive tamoxifen. After a median follow-up of 30.6 months, 449 first events (local or metastatic recurrence, contralateral breast cancer, or death) were reported — 183 in the exemestane group and 266 in the tamoxifen group. The unadjusted hazard ratio in the exemestane group as compared with the tamoxifen group was 0.68 (95 percent confidence interval, 0.56 to 0.82; P<0.001 by the log-rank test), representing a 32 percent reduction in risk and corresponding to an absolute benefit in terms of disease-free survival of 4.7 percent (95 percent confidence interval, 2.6 to 6.8) at three years after randomization. Overall survival was not significantly different in the two groups, with 93 deaths occurring in the exemestane group and 106 in the tamoxifen group. Severe toxic effects of exemestane were rare. Contralateral breast cancer occurred in 20 patients in the tamoxifen group and 9 in the exemestane group (P=0.04). conclusions Exemestane therapy after two to three years of tamoxifen therapy significantly improved disease-free survival as compared with the standard five years of tamoxifen treatment.

The ClusPro web server for protein-protein docking.

Abstract: The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.