Gene transfer vectors targeted to human prostate cancer : do we need better preclinical testing systems?

Destruction of cancer cells by genetically modified viral and nonviral vectors has been the aim of many research programs. The ability to target cytotoxic gene therapies to the cells of interest is an essential prerequisite, and the treatment has always had the potential to provide better and more long-lasting therapy than existing chemotherapies. However, the potency of these infectious agents requires effective testing systems, in which hypotheses can be explored both in vitro and in vivo before the establishment of clinical trials in humans. The real prospect of off-target effects should be eliminated in the preclinical stage, if current prejudices against such therapies are to be overcome. In this review we have set out, using adenoviral vectors as a commonly used example, to discuss some of the key parameters required to develop more effective testing, and to critically assess the current cellular models for the development and testing of prostate cancer biotherapy. Only by developing models that more closely mirror human tissues will we be able to translate literature publications into clinical trials and hence into acceptable alternative treatments for the most commonly diagnosed cancer in humans.

The phylogeny of Ureaplasma urealyticum based on the mba gene fragment

Sequencing of mba gene fragments of reference strains of Ureaplasma urealyticum serovars 1, 3, 6, 14, in addition to 33 clinical U. urealyticum isolates is reported. A phylogenetic tree deduced from an alignment of these sequences clearly demonstrates two major clusters (confidence limit 100%), which equate to the parvo and T960 biovars, and five types which we have designated mba 1, 3, 6, 8 and X. These relationships are supported by bootstrap analysis. Polymorphisms within the mba fragment of types mba 1, 3, and 6 were used to define nine subtypes (mba 1a, 1b, 3a, 3b, 3c, 3d, 3e, 6a, and 6b) thus facilitating high resolution typing of U. urealyticum. Inclusion of the reference strains for serovars 1, 3, 6, and 8 in the mba typing scheme showed that the results of this analysis are broadly consistent with currently accepted serotyping. In addition a ure gene fragment from nine of the clinical isolates was amplified and sequenced. Comparisons of the sequences clearly distinguished the two biovars of U. urealyticum; however this fragment was invariant within the parvo biovar. This study has shown that the sequence of the mba can reveal the fine details of the relationships between U. urealyticum isolates and also supports the significant evolutionary gap between the two biovars.

Slotted distributed space-time coding

Distributed space-time coding (DSTC) exploits the concept of cooperative diversity and space-time coding to offer a powerful bandwidth efficient solution with improved diversity. In this paper, we evaluate the performance of DSTC with slotted amplify-and-forward protocol (SAF). Relay nodes between the source and the destination nodes are grouped into two relay clusters based on their respective locations and these relay clusters cooperate to transmit the space-time coded signal to the destination node in different time frames. We further extend the proposed Slotted-DSTC to Slotted DSTC with redundant code (Slotted-DSTC-R) protocol where the relay nodes in both relay clusters forward the same space-time coded signal to the destination node to achieve a higher diversity order.

Seed-based biclustering of gene expression data

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Variability in estimation of self-reported dietary intake data from elite athletes resulting from coding by different sports dietitians

A routine activity for a sports dietitian is to estimate energy and nutrient intake from an athlete's self-reported food intake. Decisions made by the dietitian when coding a food record are a source of variability in the data. The aim of the present study was to determine the variability in estimation of the daily energy and key nutrient intakes of elite athletes, when experienced coders analyzed the same food record using the same database and software package. Seven-day food records from a dietary survey of athletes in the 1996 Australian Olympic team were randomly selected to provide 13 sets of records, each set representing the self-reported food intake of an endurance, team, weight restricted, and sprint/power athlete. Each set was coded by 3-5 members of Sports Dietitians Australia, making a total of 52 athletes, 53 dietitians, and 1456 athlete-days of data. We estimated within- and between- athlete and dietitian variances for each dietary nutrient using mixed modeling, and we combined the variances to express variability as a coefficient of variation (typical variation as a percent of the mean). Variability in the mean of 7-day estimates of a nutrient was 2- to 3-fold less than that of a single day. The variability contributed by the coder was less than the true athlete variability for a 1-day record but was of similar magnitude for a 7-day record. The most variable nutrients (e.g., vitamin C, vitamin A, cholesterol) had approximately 3-fold more variability than least variable nutrients (e.g., energy, carbohydrate, magnesium). These athlete and coder variabilities need to be taken into account in dietary assessment of athletes for counseling and research.

Prediction of IBD based on population history for fine gene mapping

A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBDL), among individuals without pedigree, given information on surrounding markers and population history. These IBDL probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBD L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.

Candidate gene analysis for quantitative traits using the transmission disequilibrium test : the example of the melanocortin 4-receptor in pigs

Population-wide associations between loci due to linkage disequilibrium can be used to map quantitative trait loci (QTL) with high resolution. However, spurious associations between markers and QTL can also arise as a consequence of population stratification. Statistical methods that cannot differentiate between loci associations due to linkage disequilibria from those caused in other ways can render false-positive results. The transmission-disequilibrium test (TDT) is a robust test for detecting QTL. The TDT exploits within-family associations that are not affected by population stratification. However, some TDTs are formulated in a rigid-form, with reduced potential applications. In this study we generalize TDT using mixed linear models to allow greater statistical flexibility. Allelic effects are estimated with two independent parameters: one exploiting the robust within-family information and the other the potentially biased between-family information. A significant difference between these two parameters can be used as evidence for spurious association. This methodology was then used to test the effects of the fourth melanocortin receptor (MC4R) on production traits in the pig. The new analyses supported the previously reported results; i.e., the studied polymorphism is either causal of in very strong linkage disequilibrium with the causal mutation, and provided no evidence for spurious association.

What vs how : comparing students' testing and coding skills

The well-known difficulties students exhibit when learning to program are often characterised as either difficulties in understanding the problem to be solved or difficulties in devising and coding a computational solution. It would therefore be helpful to understand which of these gives students the greatest trouble. Unit testing is a mainstay of large-scale software development and maintenance. A unit test suite serves not only for acceptance testing, but is also a form of requirements specification, as exemplified by agile programming methodologies in which the tests are developed before the corresponding program code. In order to better understand students’ conceptual difficulties with programming, we conducted a series of experiments in which students were required to write both unit tests and program code for non-trivial problems. Their code and tests were then assessed separately for correctness and ‘coverage’, respectively. The results allowed us to directly compare students’ abilities to characterise a computational problem, as a unit test suite, and develop a corresponding solution, as executable code. Since understanding a problem is a pre-requisite to solving it, we expected students’ unit testing skills to be a strong predictor of their ability to successfully implement the corresponding program. Instead, however, we found that students’testing abilities lag well behind their coding skills.

Insulin and the insulin-like growth factor system : a novel theory for hyperinsulinemic laminitis pathogenesis

Research into hyperinsulinemic laminitis has progressed significantly in recent years with the use of the prolonged-euglycemic, hyperinsulinemic clamp (p-EHC). Previous investigations of laminitis pathophysiology have focused on digital vascular dysfunction, inflammation, altered glucose metabolism within the lamellae, and lamellar basement membrane breakdown by metalloproteinases. The etiopathogenesis of laminitis occurring in association with hyperinsulinemia is yet to be fully characterized, but it may not involve these mechanisms. Insulin stimulates cellular proliferation and can also affect other body systems, such as the insulin-like growth factor (IGF) system. Insulin-like growth factor-1 (IGF-1) is structurally homologous to insulin and, like insulin, binds with strong affinity to a specific tyrosine kinase receptor on the cell surface to produce its effects, which include promoting cell proliferation. Receptors for IGF-1 (IGF-1R) are present in the lamellar epidermis. An alternative theory for the pathogenesis of hyperinsulinemic laminitis is that uncontrolled cell proliferation, mediated through both the insulin receptor (InsR) and IGF-1R, leads to lengthening, weakening, and failure of the lamellae. An analysis of the proliferative activity of lamellar epidermal cells during the developmental and acute phases of hyperinsulinemic laminitis, and lamellar gene expression of the InsR and IGF-1R was undertaken.

A description of the Mei2-like protein family; structure, phylogenetic distribution and biological context

The Schizosaccharomyces pombe Mei2 gene encodes an RNA recognition motif (RRM) protein that stimulates meiosis upon binding a specific non-coding RNA and subsequent accumulation in a “mei2-dot” in the nucleus. We present here the first systematic characterization of the family of proteins with characteristic Mei2-like amino acid sequences. Mei2-like proteins are an ancient eukaryotic protein family with three identifiable RRMs. The C-terminal RRM (RRM3) is unique to Mei2-like proteins and is the most highly conserved of the three RRMs. RRM3 also contains conserved sequence elements at its C-terminus not found in other RRM domains. Single copy Mei2-like genes are present in some fungi, in alveolates such as Paramecium and in the early branching eukaryote Entamoeba histolytica, while plants contain small families of Mei2-like genes. While the C-terminal RRM is highly conserved between plants and fungi, indicating conservation of molecular mechanisms, plant Mei2-like genes have changed biological context to regulate various aspects of developmental pattern formation.

A tumour-derived mutant allele of XRCC2 preferentially suppresses homologous recombination at DNA replication forks

Homologous recombination repair (HRR) is required for both the repair of DNA double strand breaks (DSBs) and the maintenance of the integrity of DNA replication forks. To determine the effect of a mutant allele of the RAD51 paralog XRCC2 (342delT) found in an HRR-defective tumour cell line, 342delT was introduced into HRR proficient cells containing a recombination reporter substrate. In one set of transfectants, expression of 342delT conferred sensitivity to thymidine and mitomycin C and suppressed HRR induced at the recombination reporter by thymidine but not by DSBs. In a second set of transfectants, the expression of 342delT was accompanied by a decreased level of the full-length XRCC2. These cells were defective in the induction of HRR by either thymidine or DSBs. Thus 342delT suppresses recombination induced by thymidine in a dominant negative manner while recombination induced by DSBs appears to depend upon the level of XRCC2 as well as the expression of the mutant XRCC2 allele. These results suggest that HRR pathways responding to stalled replication forks or DSBs are genetically distinguishable. They further suggest a critical role for XRCC2 in HRR at replication forks, possibly in the loading of RAD51 onto gapped DNA.

Mass spectrometry-based metabolomics towards understanding of gene functions with a diversity of biological contexts

Currently, mass spectrometry-based metabolomics studies extend beyond conventional chemical categorization and metabolic phenotype analysis to understanding gene function in various biological contexts (e.g., mammalian, plant, and microbial). These novel utilities have led to many innovative discoveries in the following areas: disease pathogenesis, therapeutic pathway or target identification, the biochemistry of animal and plant physiological and pathological activities in response to diverse stimuli, and molecular signatures of host-pathogen interactions during microbial infection. In this review, we critically evaluate the representative applications of mass spectrometry-based metabolomics to better understand gene function in diverse biological contexts, with special emphasis on working principles, study protocols, and possible future development of this technique. Collectively, this review raises awareness within the biomedical community of the scientific value and applicability of mass spectrometry-based metabolomics strategies to better understand gene function, thus advancing this application's utility in a broad range of biological fields