Differential viral accessibility (DIVA) identifies alterations in chromatin architecture through large-scale mapping of lentiviral integration sites.

Alterations in chromatin structure play a major role in the epigenetic regulation of gene expression. Here, we describe a step-by-step protocol for differential viral accessibility (DIVA), a method for identifying changes in chromatin accessibility genome-wide. Commonly used methods for mapping accessible genomic loci have strong preferences toward detecting 'open' chromatin found at regulatory regions but are not well suited to studying chromatin accessibility in gene bodies and intergenic regions. DIVA overcomes this limitation, enabling a broader range of sites to be interrogated. Conceptually, DIVA is similar to ATAC-seq in that it relies on the integration of exogenous DNA into the genome to map accessible chromatin, except that chromatin architecture is probed through mapping integration sites of exogenous lentiviruses. An isogenic pair of cell lines are transduced with a lentiviral vector, followed by PCR amplification and Illumina sequencing of virus-genome junctions; the resulting sequences define a set of unique lentiviral integration sites, which are compared to determine whether genomic loci exhibit significantly altered accessibility between experimental and control cells. Experienced researchers will take 6 d to generate lentiviral stocks and transduce the target cells, a further 5 d to prepare the Illumina sequencing libraries and a few hours to perform the bioinformatic analysis

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

The effect of turbulent viscous shear stress on red blood cell hemolysis

[[abstract]]Non-physiologic turbulent flow occurs in medical cardiovascular devices resulting in hemodynamic stresses that may damage red blood cells (RBC) and cause hemolysis. Hemolysis was previously thought to result from Reynolds shear stress (RSS) in turbulent flows. A more recent hypothesis suggests that turbulent viscous shear stresses (TVSS) at spatial scales similar in size to RBCs are related to their damage. We applied two-dimensional digital particle image velocimetry to measure the flow field of a free-submerged axisymmetric jet that was utilized to hemolyze porcine RBCs in selected locations. Assuming a dynamic equilibrium for the sub-grid scale (SGS) energy flux between the resolved and the sub-grid scales, the SGS energy flux was calculated from the strain rate tensor computed from the resolved velocity fields. The SGS stress was determined by the Smagorinsky model, from which the turbulence dissipation rate and then TVSS were estimated. Our results showed the hemolytic threshold of the Reynolds stresses was up to 517 Pa, and the TVSSs were at least an order of magnitude less than the RSS. The results provide further insight into the relationship between turbulence and RBC damage.[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]JP

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p

Clinicians’ response to hyperoxia in ventilated patients in a Dutch ICU depends on the level of FiO2

Hyperoxia may induce pulmonary injury and may increase oxidative stress. In this retrospective database study we aimed to evaluate the response to hyperoxia by intensivists in a Dutch academic intensive care unit. All arterial blood gas (ABG) data from mechanically ventilated patients from 2005 until 2009 were extracted from an electronic storage database of a mixed 32-bed intensive care unit in a university hospital in Amsterdam. Mechanical ventilation settings at the time of the ABG tests were retrieved. The results of 126,778 ABG tests from 5,498 mechanically ventilated patients were retrieved including corresponding ventilator settings. In 28,222 (22%) of the ABG tests the arterial oxygen tension (PaO2) was > 16 kPa (120 mmHg). In only 25% of the tests with PaO2 > 16 kPa (120 mmHg) was the fraction of inspired oxygen (FiO(2)) decreased. Hyperoxia was accepted without adjustment in ventilator settings if FiO(2) was 0.4 or lower. Hyperoxia is frequently seen but in most cases does not lead to adjustment of ventilator settings if FiO(2) <0.41. Implementation of guidelines concerning oxygen therapy should be improved and further research is needed concerning the effects of frequently encountered hyperoxi

The Formation of the First Massive Black Holes

Supermassive black holes (SMBHs) are common in local galactic nuclei, and SMBHs as massive as several billion solar masses already exist at redshift z=6. These earliest SMBHs may grow by the combination of radiation-pressure-limited accretion and mergers of stellar-mass seed BHs, left behind by the first generation of metal-free stars, or may be formed by more rapid direct collapse of gas in rare special environments where dense gas can accumulate without first fragmenting into stars. This chapter offers a review of these two competing scenarios, as well as some more exotic alternative ideas. It also briefly discusses how the different models may be distinguished in the future by observations with JWST, (e)LISA and other instruments.Comment: 47 pages with 306 references; this review is a chapter in "The First Galaxies - Theoretical Predictions and Observational Clues", Springer Astrophysics and Space Science Library, Eds. T. Wiklind, V. Bromm & B. Mobasher, in pres

Serotype Distribution and Invasive Potential of Group B Streptococcus Isolates Causing Disease in Infants and Colonizing Maternal-Newborn Dyads

Serotype-specific polysaccharide based group B streptococcus (GBS) vaccines are being developed. An understanding of the serotype epidemiology associated with maternal colonization and invasive disease in infants is necessary to determine the potential coverage of serotype-specific GBS vaccines.Colonizing GBS isolates were identified by vaginal swabbing of mothers during active labor and from skin of their newborns post-delivery. Invasive GBS isolates from infants were identified through laboratory-based surveillance. GBS serotyping was done by latex agglutination. Serologically non-typeable isolates were typed by a serotype-specific PCR method. The invasive potential of GBS serotypes associated with sepsis within seven days of birth was evaluated in association to maternal colonizing serotypes.GBS was identified in 289 (52.4%) newborns born to 551 women with GBS-vaginal colonization and from 113 (5.6%) newborns born to 2,010 mothers in whom GBS was not cultured from vaginal swabs. The serotype distribution among vaginal-colonizing isolates was as follows: III (37.3%), Ia (30.1%), and II (11.3%), V (10.2%), Ib (6.7%) and IV (3.7%). There were no significant differences in serotype distribution between vaginal and newborn colonizing isolates (P = 0.77). Serotype distribution of invasive GBS isolates were significantly different to that of colonizing isolates (P<0.0001). Serotype III was the most common invasive serotype in newborns less than 7 days (57.7%) and in infants 7 to 90 days of age (84.3%; P<0.001). Relative to serotype III, other serotypes showed reduced invasive potential: Ia (0.49; 95%CI 0.31-0.77), II (0.30; 95%CI 0.13-0.67) and V (0.38; 95%CI 0.17-0.83).In South Africa, an anti-GBS vaccine including serotypes Ia, Ib and III has the potential of preventing 74.1%, 85.4% and 98.2% of GBS associated with maternal vaginal-colonization, invasive disease in neonates less than 7 days and invasive disease in infants between 7-90 days of age, respectively

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions