Collagen-Like Proteins in Pathogenic E. coli Strains

The genome sequences of enterohaemorrhagic E. coli O157:H7 strains show multiple open-reading frames with collagen-like sequences that are absent from the common laboratory strain K-12. These putative collagens are included in prophages embedded in O157:H7 genomes. These prophages carry numerous genes related to strain virulence and have been shown to be inducible and capable of disseminating virulence factors by horizontal gene transfer. We have cloned two collagen-like proteins from E. coli O157:H7 into a laboratory strain and analysed the structure and conformation of the recombinant proteins and several of their constituting domains by a variety of spectroscopic, biophysical, and electron microscopy techniques. We show that these molecules exhibit many of the characteristics of vertebrate collagens, including trimer formation and the presence of a collagen triple helical domain. They also contain a C-terminal trimerization domain, and a trimeric α-helical coiled-coil domain with an unusual amino acid sequence almost completely lacking leucine, valine or isoleucine residues. Intriguingly, these molecules show high thermal stability, with the collagen domain being more stable than those of vertebrate fibrillar collagens, which are much longer and post-translationally modified. Under the electron microscope, collagen-like proteins from E. coli O157:H7 show a dumbbell shape, with two globular domains joined by a hinged stalk. This morphology is consistent with their likely role as trimeric phage side-tail proteins that participate in the attachment of phage particles to E. coli target cells, either directly or through assembly with other phage tail proteins. Thus, collagen-like proteins in enterohaemorrhagic E. coli genomes may have a direct role in the dissemination of virulence-related genes through infection of harmless strains by induced bacteriophages

Finding Novel Molecular Connections between Developmental Processes and Disease

<div><p>Identifying molecular connections between developmental processes and disease can lead to new hypotheses about health risks at all stages of life. Here we introduce a new approach to identifying significant connections between gene sets and disease genes, and apply it to several gene sets related to human development. To overcome the limits of incomplete and imperfect information linking genes to disease, we pool genes within disease subtrees in the MeSH taxonomy, and we demonstrate that such pooling improves the power and accuracy of our approach. Significance is assessed through permutation. We created a web-based visualization tool to facilitate multi-scale exploration of this large collection of significant connections (<a href="http://gda.cs.tufts.edu/development" target="_blank">http://gda.cs.tufts.edu/development</a>). High-level analysis of the results reveals expected connections between tissue-specific developmental processes and diseases linked to those tissues, and widespread connections to developmental disorders and cancers. Yet interesting new hypotheses may be derived from examining the unexpected connections. We highlight and discuss the implications of three such connections, linking dementia with bone development, polycystic ovary syndrome with cardiovascular development, and retinopathy of prematurity with lung development. Our results provide additional evidence that plays a key role in the early pathogenesis of polycystic ovary syndrome. Our evidence also suggests that the <i>VEGF</i> pathway and downstream <i>NFKB</i> signaling may explain the complex relationship between bronchopulmonary dysplasia and retinopathy of prematurity, and may form a bridge between two currently-competing hypotheses about the molecular origins of bronchopulmonary dysplasia. Further data exploration and similar queries about other gene sets may generate a variety of new information about the molecular relationships between additional diseases.</p></div

Overtreatment in the United States

Background: Overtreatment is a cause of preventable harm and waste in health care. Little is known about clinician perspectives on the problem. In this study, physicians were surveyed on the prevalence, causes, and implications of overtreatment. Methods: 2,106 physicians from an online community composed of doctors from the American Medical Association (AMA) masterfile participated in a survey. The survey inquired about the extent of overutilization, as well as causes, solutions, and implications for health care. Main outcome measures included: percentage of unnecessary medical care, most commonly cited reasons of overtreatment, potential solutions, and responses regarding association of profit and overtreatment. Findings: The response rate was 70.1%. Physicians reported that an interpolated median of 20.6% of overall medical care was unnecessary, including 22.0% of prescription medications, 24.9% of tests, and 11.1% of procedures. The most common cited reasons for overtreatment were fear of malpractice (84.7%), patient pressure/request (59.0%), and difficulty accessing medical records (38.2%). Potential solutions identified were training residents on appropriateness criteria (55.2%), easy access to outside health records (52.0%), and more practice guidelines (51.5%). Most respondents (70.8%) believed that physicians are more likely to perform unnecessary procedures when they profit from them. Most respondents believed that de-emphasizing fee-for-service physician compensation would reduce health care utilization and costs. Conclusion: From the physician perspective, overtreatment is common. Efforts to address the problem should consider the causes and solutions offered by physicians

DFLAT: functional annotation for human development.

BACKGROUND: Recent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus. DESCRIPTION: The Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation. CONCLUSIONS: Functional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT\u27s improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development. BMC Bioinformatics 2014; 15:45

Histogram showing - for each query gene set.

<p>The red lines show a difference of zero; values to the left of these lines represent individual random trials in which the traditional method outperformed the pooling method. This occurred only once, in one trial for the skin development gene set.</p

Pooling genes across related diseases to assess enrichment.

<p>a) Lung development genes linked directly to three related MeSH terms. The genes associated with each term are shown in a different color. b) By pooling the lung development genes from the subtree rooted at the <i>Neural tube defects</i> node, we obtain enough genes to identify significant enrichment at that node. Colors, the same as those in part a, indicate the disease terms with which the genes were associated before pooling.</p

The VEGF pathway and its relevance to both BPD hypotheses.

<p>The relationships shown here are derived from the VEGF, PI3K-AKT, mTOR, and HIF-1 signaling pathways and the “Pathways in Cancer” map in the KEGG Pathway database. Dashed lines represent indirect regulation. Genes highlighted in orange are the five lung development genes implicated in ROP.</p

Example of comparison between pooling approach and traditional approach.

<p>Illustration of the process for calculating and for the th random trial. 100 gene-disease associations involving genes in the query gene set are withheld. Using the remaining associations, p-values for enrichment of the disease gene set at each node are computed using both the traditional and pooling approaches. Nodes are assigned to or based on which approach shows more significant enrichment, and the rate at which each set is supported by withheld links is computed. The idea is that if a disease class is correctly linked to the query gene set, it should be more likely to be supported by withheld gene-disease associations from that same query set.</p

Expected results by tissue.

<p>Density of enrichment of developmental gene sets (labels on the right) in major disease subtrees. Values are z-score normalized densities, computed as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003578#s3" target="_blank">Methods</a>. Darker squares indicate that a larger fraction of the disease terms in the MeSH category have significant enrichment () of genes in the indicated gene set. Expected connections appear approximately along the diagonal in the first 7 columns, and throughout the rightmost two columns.</p