Retroviral DNA Integration: ASLV, HIV, and MLV Show Distinct Target Site Preferences

The completion of the human genome sequence has made possible genome-wide studies of retroviral DNA integration. Here we report an analysis of 3,127 integration site sequences from human cells. We compared retroviral vectors derived from human immunodeficiency virus (HIV), avian sarcoma-leukosis virus (ASLV), and murine leukemia virus (MLV). Effects of gene activity on integration targeting were assessed by transcriptional profiling of infected cells. Integration by HIV vectors, analyzed in two primary cell types and several cell lines, strongly favored active genes. An analysis of the effects of tissue-specific transcription showed that it resulted in tissue-specific integration targeting by HIV, though the effect was quantitatively modest. Chromosomal regions rich in expressed genes were favored for HIV integration, but these regions were found to be interleaved with unfavorable regions at CpG islands. MLV vectors showed a strong bias in favor of integration near transcription start sites, as reported previously. ASLV vectors showed only a weak preference for active genes and no preference for transcription start regions. Thus, each of the three retroviruses studied showed unique integration site preferences, suggesting that virus-specific binding of integration complexes to chromatin features likely guides site selection

Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources

<p>Abstract</p> <p>Background</p> <p>Gene expression profiling studies of mastitis in ruminants have provided key but fragmented knowledge for the understanding of the disease. A systematic combination of different expression profiling studies via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of infections with mammary gland pathogens, including samples from cattle challenged <it>in vivo </it>with <it>S. aureus</it>, <it>E. coli</it>, and <it>S. uberis</it>, samples from goats challenged <it>in vivo </it>with <it>S. aureus</it>, as well as cattle macrophages and ovine dendritic cells infected <it>in vitro </it>with <it>S. aureus</it>. We combined different time points from those studies, testing different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific.</p> <p>Results</p> <p>Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the general disease response. In the overall response, pathways related to immune response and inflammation, as well as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk fat content depression commonly observed during mastitis infection. Complementarities between early and late stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by few genes, including <it>XBP1 </it>and <it>SREBF1</it>.</p> <p>The cattle-specific response was characterized by alteration of the immune response and by modification of lipid metabolism. Comparison of <it>E. coli </it>and <it>S. aureus </it>infections in cattle <it>in vivo </it>revealed that affected genes showing opposite regulation had the same altered biological functions and provided evidence that <it>E. coli </it>caused a stronger host response.</p> <p>Conclusions</p> <p>This meta-analysis approach reinforces previous findings but also reveals several novel themes, including the involvement of genes, biological functions, and pathways that were not identified in individual studies. As such, it provides an interesting proof of principle for future studies combining information from diverse heterogeneous sources.</p

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Effects of Tissue-Specific Transcription on Integration Site Selection in Different Cell Types

<p>Genes hosting integration events by the HIV vector were analyzed for their expression levels in transcriptional profiling data from IMR90, PBMC, and SupT1 cells. For each gene hosting an integration event, the expression values from the three cell types were then ranked lowest (red), medium (orange), and highest (yellow). The values were summed and displayed separately for each set of integration sites: (A) IMR90 sites, (B) PBMC sites, and (C) SupT1 sites. In each case there was a significant trend for the cell type hosting the integration events to show the highest expression values relative to the other two (<i>p <</i> 0.05 for all comparisons).</p

Relationship between Integration Sites and Transcriptional Intensity in the Human Genome

<p>The human chromosomes are shown numbered. HIV integration sites from all datasets in Table 1 are shown as blue “lollipops”; MLV integration sites are shown in lavender; and ASLV integration sites are shown in green. Transcriptional activity is shown by the red shading on each of the chromosomes (derived from quantification of nonnormalized EST libraries, see text). Centromeres, which are mostly unsequenced, are shown as grey rectangles.</p

The Effects of Proximity to CpG Islands Differs for HIV, MLV, and ASLV Integration

<p>The viral vectors and cell types studied are indicated by color. A value of one indicates no bias, less than one indicates disfavored integration, and more than one indicates favored integration. The x-axis (from plus or minus 1 kb to 50 kb) indicates distance from the edge of a CpG island in either direction along the genome. The statistical analysis specifically removed the favorable effects of being in a gene and being in a region containing expressed genes to highlight the effects of CpG islands alone. When effects of gene density and activity are left in, HIV integration goes from disfavored at short distances (less than 1 kb) to favored at longer distances (more than 10 kb). This is because at longer distances the association with genes is significant—many CpG islands are within 10 kb of a gene, and genes are favored targets for HIV integration. To carry out this analysis, the numbers of experimentally determined and matched control sites were fitted according to whether they were near a CpG island, whether they were in genes, and the level of the expression density variable. Each variable contributes a “multiplier” for the ratio of the number of experimental to control sites. The multiplier for “near CpG island” is shown (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020234#sd002" target="_blank">see Protocol S2</a>, p. 9–12).</p

Comparison of Transcriptional Intensity to Integration Intensity on Human Chromosome 11

<p>All data were quantified in 2-Mb intervals. The top line shows summed EST data documenting the “transcriptional intensity” for each chromosomal interval (data from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020234#pbio-0020234-Mungall1" target="_blank">Mungall and al. [2003]</a>). The bottom three lines show the summed frequency of integration site sequences in each interval. The numbers of ESTs (top) or integration sites (bottom three) are shown on the y-axis.</p

Integration Intensity in Genes and Intergenic Regions

<p>Genes or intergenic regions were normalized to a common length and then divided into ten intervals to allow comparison. The number of integration sites in each interval was divided by the number of matched random control sites and the value plotted. A value of one indicates no difference between the experimental sites and the random controls. Viruses and cell types studied are as marked above each graph. The direction of transcription within each gene is from left to right. Note that our normalization method de-emphasizes favored MLV integration events just upstream of gene 5′ ends (outside transcription units), as reported by <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020234#pbio-0020234-Wu1" target="_blank">Wu et al. (2003)</a>. We carried out an analysis specially designed to identify this effect and confirmed that the regions just upstream of gene 5′ ends are favored for MLV integration when reanalyzed with the matched random control data (unpublished data).</p

Frequency of Integration in Human Chromosomes

<p>Human chromosome numbers are indicated at the bottom of the figure. The number of integration events detected in each chromosome was divided by the number expected from the matched random control. The line at one indicates the bar height expected if the observed number of integration events matched the expected number. Higher bars indicate favored integration, lower bars, disfavored integration. Most of the cell types studied were from human females; too little data were available for the Y chromosome for meaningful analysis.</p