A model for transition of 5 '-nuclease domain of DNA polymerase I from inert to active modes

Bacteria contain DNA polymerase I (PolI), a single polypeptide chain consisting of similar to 930 residues, possessing DNA-dependent DNA polymerase, 3'-5' proofreading and 5'-3' exonuclease (also known as flap endonuclease) activities. PolI is particularly important in the processing of Okazaki fragments generated during lagging strand replication and must ultimately produce a double-stranded substrate with a nick suitable for DNA ligase to seal. PolI's activities must be highly coordinated both temporally and spatially otherwise uncontrolled 5'-nuclease activity could attack a nick and produce extended gaps leading to potentially lethal double-strand breaks. To investigate the mechanism of how PolI efficiently produces these nicks, we present theoretical studies on the dynamics of two possible scenarios or models. In one the flap DNA substrate can transit from the polymerase active site to the 5'-nuclease active site, with the relative position of the two active sites being kept fixed; while the other is that the 5'-nuclease domain can transit from the inactive mode, with the 5'-nuclease active site distant from the cleavage site on the DNA substrate, to the active mode, where the active site and substrate cleavage site are juxtaposed. The theoretical results based on the former scenario are inconsistent with the available experimental data that indicated that the majority of 5'-nucleolytic processing events are carried out by the same PolI molecule that has just extended the upstream primer terminus. By contrast, the theoretical results on the latter model, which is constructed based on available structural studies, are consistent with the experimental data. We thus conclude that the latter model rather than the former one is reasonable to describe the cooperation of the PolI's polymerase and 5'-3' exonuclease activities. Moreover, predicted results for the latter model are presented

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure

Risk indicators for severe impaired oral health among indigenous Australian young adults

Background Oral health impairment comprises three conceptual domains; pain, appearance and function. This study sought to: (1) estimate the prevalence of severe oral health impairment as assessed by a summary oral health impairment measure, including aspects of dental pain, dissatisfaction with dental appearance and difficulty eating, among a birth cohort of Indigenous Australian young adults (n = 442, age range 16-20 years); (2) compare prevalence according to demographic, socio-economic, behavioural, dental service utilisation and oral health outcome risk indicators; and (3) ascertain the independent contribution of those risk indicators to severe oral health impairment in this population. Methods Data were from the Aboriginal Birth Cohort (ABC) study, a prospective longitudinal investigation of Aboriginal individuals born 1987-1990 at an Australian regional hospital. Data for this analysis pertained to Wave-3 of the study only. Severe oral health impairment was defined as reported experience of toothache, poor dental appearance and food avoidance in the last 12 months. Logistic regression models were used to evaluate effects of demographic, socio-economic, behavioural, dental service utilisation and clinical oral disease indicators on severe oral health impairment. Effects were quantified as odds ratios (OR). Results The percent of participants with severe oral health impairment was 16.3 (95% CI 12.9-19.7). In the multivariate model, severe oral health impairment was associated with untreated dental decay (OR 4.0, 95% CI 1.6-9.6). In addition to that clinical indicator, greater odds of severe oral health impairment were associated with being female (OR 2.0, 95% CI 1.2-3.6), being aged 19-20 years (OR 2.1, 95% CI 1.2-3.6), soft drink consumption every day or a few days a week (OR 2.6, 95% 1.2-5.6) and non-ownership of a toothbrush (OR 1.9, 95% CI 1.1-3.4). Conclusions Severe oral health impairment was prevalent among this population. The findings suggest that public health strategies that address prevention and treatment of dental disease, self-regulation of soft drink consumption and ownership of oral self-care devices are needed if severe oral health impairment among Indigenous Australian young adults is to be reduced.Lisa M Jamieson, Kaye F Roberts-Thomson and Susan M Sayer

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes

The functional cancer map: A systems-level synopsis of genetic deregulation in cancer

<p>Abstract</p> <p>Background</p> <p>Cancer cells are characterized by massive dysegulation of physiological cell functions with considerable disruption of transcriptional regulation. Genome-wide transcriptome profiling can be utilized for early detection and molecular classification of cancers. Accurate discrimination of functionally different tumor types may help to guide selection of targeted therapy in translational research. Concise grouping of tumor types in cancer maps according to their molecular profile may further be helpful for the development of new therapeutic modalities or open new avenues for already established therapies.</p> <p>Methods</p> <p>Complete available human tumor data of the Stanford Microarray Database was downloaded and filtered for relevance, adequacy and reliability. A total of 649 tumor samples from more than 1400 experiments and 58 different tissues were analyzed. Next, a method to score deregulation of KEGG pathway maps in different tumor entities was established, which was then used to convert hundreds of gene expression profiles into corresponding tumor-specific pathway activity profiles. Based on the latter, we defined a measure for functional similarity between tumor entities, which yielded to phylogeny of tumors.</p> <p>Results</p> <p>We provide a comprehensive, easy-to-interpret functional cancer map that characterizes tumor types with respect to their biological and functional behavior. Consistently, multiple pathways commonly associated with tumor progression were revealed as common features in the majority of the tumors. However, several pathways previously not linked to carcinogenesis were identified in multiple cancers suggesting an essential role of these pathways in cancer biology. Among these pathways were 'ECM-receptor interaction', 'Complement and Coagulation cascades', and 'PPAR signaling pathway'.</p> <p>Conclusion</p> <p>The functional cancer map provides a systematic view on molecular similarities across different cancers by comparing tumors on the level of pathway activity. This work resulted in identification of novel superimposed functional pathways potentially linked to cancer biology. Therefore, our work may serve as a starting point for rationalizing combination of tumor therapeutics as well as for expanding the application of well-established targeted tumor therapies.</p

Mycobacterial PIMs Inhibit Host Inflammatory Responses through CD14-Dependent and CD14-Independent Mechanisms

Mycobacteria develop strategies to evade the host immune system. Among them, mycobacterial LAM or PIMs inhibit the expression of pro-inflammatory cytokines by activated macrophages. Here, using synthetic PIM analogues, we analyzed the mode of action of PIM anti-inflammatory effects. Synthetic PIM1 isomer and PIM2 mimetic potently inhibit TNF and IL-12 p40 expression induced by TLR2 or TLR4 pathways, but not by TLR9, in murine macrophages. We show inhibition of LPS binding to TLR4/MD2/CD14 expressing HEK cells by PIM1 and PIM2 analogues. More specifically, the binding of LPS to CD14 was inhibited by PIM1 and PIM2 analogues. CD14 was dispensable for PIM1 and PIM2 analogues functional inhibition of TLR2 agonists induced TNF, as shown in CD14-deficient macrophages. The use of rough-LPS, that stimulates TLR4 pathway independently of CD14, allowed to discriminate between CD14-dependent and CD14-independent anti-inflammatory effects of PIMs on LPS-induced macrophage responses. PIM1 and PIM2 analogues inhibited LPS-induced TNF release by a CD14-dependent pathway, while IL-12 p40 inhibition was CD14-independent, suggesting that PIMs have multifold inhibitory effects on the TLR4 signalling pathway

Genomic, Proteomic and Physiological Characterization of a T5-like Bacteriophage for Control of Shiga Toxin-Producing Escherichia coli O157:H7

Despite multiple control measures, Escherichia coli O157:H7 (STEC O157:H7) continues to be responsible for many food borne outbreaks in North America and elsewhere. Bacteriophage therapy may prove useful for controlling this pathogen in the host, their environment and food. Bacteriophage vB_EcoS_AKFV33 (AKFV33), a T5-like phage of Siphoviridae lysed common phage types of STEC O157:H7 and not non-O157 E. coli. Moreover, STEC O157:H7 isolated from the same feedlot pen from which the phage was obtained, were highly susceptible to AKFV33. Adsorption rate constant and burst size were estimated to be 9.31×10−9 ml/min and 350 PFU/infected cell, respectively. The genome of AKVF33 was 108,853 bp (38.95% G+C), containing 160 open reading frames (ORFs), 22 tRNA genes and 32 strong promoters recognized by host RNA polymerase. Of 12 ORFs without homologues to T5-like phages, 7 predicted novel proteins while others exhibited low identity (<60%) to proteins in the National Centre for Biotechnology Information database. AKVF33 also lacked the L-shaped tail fiber protein typical of T5, but was predicted to have tail fibers comprised of 2 novel proteins with low identity (37–41%) to tail fibers of E. coli phage phiEco32 of Podoviridae, a putative side tail fiber protein of a prophage from E. coli IAI39 and a conserved domain protein of E. coli MS196-1. The receptor-binding tail protein (pb5) shared an overall identify of 29–72% to that of other T5-like phages, with no region coding for more than 6 amino acids in common. Proteomic analysis identified 4 structural proteins corresponding to the capsid, major tail, tail fiber and pore-forming tail tip (pb2). The genome of AKFV33 lacked regions coding for known virulence factors, integration-related proteins or antibiotic resistance determinants. Phage AKFV33 is a unique, highly lytic STEC O157:H7-specific T5-like phage that may have considerable potential as a pre- and post-harvest biocontrol agent

The Airway Microbiota in Cystic Fibrosis: A Complex Fungal and Bacterial Community—Implications for Therapeutic Management

International audienceBackground Given the polymicrobial nature of pulmonary infections in patients with cystic fibrosis (CF), it is essential to enhance our knowledge on the composition of the microbial community to improve patient management. In this study, we developed a pyrosequencing approach to extensively explore the diversity and dynamics of fungal and prokaryotic populations in CF lower airways. Methodology and Principal Findings Fungi and bacteria diversity in eight sputum samples collected from four adult CF patients was investigated using conventional microbiological culturing and high-throughput pyrosequencing approach targeting the ITS2 locus and the 16S rDNA gene. The unveiled microbial community structure was compared to the clinical profile of the CF patients. Pyrosequencing confirmed recently reported bacterial diversity and observed complex fungal communities, in which more than 60% of the species or genera were not detected by cultures. Strikingly, the diversity and species richness of fungal and bacterial communities was significantly lower in patients with decreased lung function and poor clinical status. Values of Chao1 richness estimator were statistically correlated with values of the Shwachman-Kulczycki score, body mass index, forced vital capacity, and forced expiratory volume in 1 s (p = 0.046, 0.047, 0.004, and 0.001, respectively for fungal Chao1 indices, and p = 0.010, 0.047, 0.002, and 0.0003, respectively for bacterial Chao1 values). Phylogenetic analysis showed high molecular diversities at the sub-species level for the main fungal and bacterial taxa identified in the present study. Anaerobes were isolated with Pseudomonas aeruginosa, which was more likely to be observed in association with Candida albicans than with Aspergillus fumigatus