El Nino and Health Risks from Landscape Fire Emissions in Southeast Asia

Emissions from landscape fires affect both climate and air quality. Here, we combine satellite-derived fire estimates and atmospheric modelling to quantify health effects from fire emissions in southeast Asia from 1997 to 2006. This region has large interannual variability in fire activity owing to coupling between El Nino-induced droughts and anthropogenic land-use change. We show that during strong El Nino years, fires contribute up to 200 micrograms per cubic meter and 50 ppb in annual average fine particulate matter (PM2.5) and ozone surface concentrations near fire sources, respectively. This corresponds to a fire contribution of 200 additional days per year that exceed the World Health Organization 50 micrograms per cubic metre 24-hr PM(sub 2.5) interim target and an estimated 10,800 (6,800-14,300)-person (approximately 2 percent) annual increase in regional adult cardiovascular mortality. Our results indicate that reducing regional deforestation and degradation fires would improve public health along with widely established benefits from reducing carbon emissions, preserving biodiversity and maintaining ecosystem services

Real-Time Definition of Non-Randomness in the Distribution of Genomic Events

Features such as mutations or structural characteristics can be non-randomly or non-uniformly distributed within a genome. So far, computer simulations were required for statistical inferences on the distribution of sequence motifs. Here, we show that these analyses are possible using an analytical, mathematical approach. For the assessment of non-randomness, our calculations only require information including genome size, number of (sampled) sequence motifs and distance parameters. We have developed computer programs evaluating our analytical formulas for the real-time determination of expected values and p-values. This approach permits a flexible cluster definition that can be applied to most effectively identify non-random or non-uniform sequence motif distribution. As an example, we show the effectivity and reliability of our mathematical approach in clinical retroviral vector integration site distribution

Characterization of Ku702–NLS as Bipartite Nuclear Localization Sequence for Non-Viral Gene Delivery

Several barriers have to be overcome in order to achieve gene expression in target cells, e.g. cellular uptake, endosomal release and translocation to the nucleus. Nuclear localization sequences (NLS) enhance gene delivery by increasing the uptake of plasmid DNA (pDNA) to the nucleus. So far, only monopartite NLS were analysed for non-viral gene delivery. In this study, we examined the characteristics of a novel bipartite NLS like construct, namely NLS Ku70. We synthesized a dimeric structure of a modified NLS from the Ku70 protein (Ku702-NLS), a nuclear transport active mutant of Ku702-NLS (s1Ku702-NLS) and a nuclear transport deficient mutant of Ku702-NLS (s2Ku702). We examined the transfection efficiency of binary Ku702-NLS/DNA and ternary Ku702-NLS/PEI/DNA gene vector complexes in vitro by using standard transfection protocols as well as the magnetofection method. The application of Ku702-NLS and s1Ku702-NLS increased gene transfer efficiency in vitro and in vivo. This study shows for the first time that the use of bipartite NLS compounds alone or in combination with cationic polymers is a promising strategy to enhance the efficiency of non-viral gene transfer

Murine Leukemias with Retroviral Insertions at Lmo2 Are Predictive of the Leukemias Induced in SCID-X1 Patients Following Retroviral Gene Therapy

Five X-linked severe combined immunodeficiency patients (SCID-X1) successfully treated with autologous bone marrow stem cells infected ex vivo with an IL2RG-containing retrovirus subsequently developed T-cell leukemia and four contained insertional mutations at LMO2. Genetic evidence also suggests a role for IL2RG in tumor formation, although this remains controversial. Here, we show that the genes and signaling pathways deregulated in murine leukemias with retroviral insertions at Lmo2 are similar to those deregulated in human leukemias with high LMO2 expression and are highly predictive of the leukemias induced in SCID-X1 patients. We also provide additional evidence supporting the notion that IL2RG and LMO2 cooperate in leukemia induction but are not sufficient and require additional cooperating mutations. The highly concordant nature of the genetic events giving rise to mouse and human leukemias with mutations at Lmo2 are an encouraging sign to those wanting to use mice to model human cancer and may help in designing safer methods for retroviral gene therapy

Estimated Comparative Integration Hotspots Identify Different Behaviors of Retroviral Gene Transfer Vectors

Integration of retroviral vectors in the human genome follows non random patterns that favor insertional deregulation of gene expression and may cause risks of insertional mutagenesis when used in clinical gene therapy. Understanding how viral vectors integrate into the human genome is a key issue in predicting these risks. We provide a new statistical method to compare retroviral integration patterns. We identified the positions where vectors derived from the Human Immunodeficiency Virus (HIV) and the Moloney Murine Leukemia Virus (MLV) show different integration behaviors in human hematopoietic progenitor cells. Non-parametric density estimation was used to identify candidate comparative hotspots, which were then tested and ranked. We found 100 significative comparative hotspots, distributed throughout the chromosomes. HIV hotspots were wider and contained more genes than MLV ones. A Gene Ontology analysis of HIV targets showed enrichment of genes involved in antigen processing and presentation, reflecting the high HIV integration frequency observed at the MHC locus on chromosome 6. Four histone modifications/variants had a different mean density in comparative hotspots (H2AZ, H3K4me1, H3K4me3, H3K9me1), while gene expression within the comparative hotspots did not differ from background. These findings suggest the existence of epigenetic or nuclear three-dimensional topology contexts guiding retroviral integration to specific chromosome areas

Analyzing the Number of Common Integration Sites of Viral Vectors – New Methods and Computer Programs

Vectors based on γ-retroviruses or lentiviruses have been shown to stably express therapeutical transgenes and effectively cure different hematological diseases. Molecular follow up of the insertional repertoire of gene corrected cells in patients and preclinical animal models revealed different integration preferences in the host genome including clusters of integrations in small genomic areas (CIS; common integrations sites). In the majority, these CIS were found in or near genes, with the potential to influence the clonal fate of the affected cell. To determine whether the observed degree of clustering is statistically compatible with an assumed standard model of spatial distribution of integrants, we have developed various methods and computer programs for γ-retroviral and lentiviral integration site distribution. In particular, we have devised and implemented mathematical and statistical approaches for comparing two experimental samples with different numbers of integration sites with respect to the propensity to form CIS as well as for the analysis of coincidences of integration sites obtained from different blood compartments. The programs and statistical tools described here are available as workspaces in R code and allow the fast detection of excessive clustering of integration sites from any retrovirally transduced sample and thus contribute to the assessment of potential treatment-related risks in preclinical and clinical retroviral gene therapy studies

Methodology and software to detect viral integration site hot-spots

<p>Abstract</p> <p>Background</p> <p>Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.</p> <p>Results</p> <p>We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.</p> <p>Conclusions</p> <p>The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.</p

Insights in 17β-HSD1 Enzyme Kinetics and Ligand Binding by Dynamic Motion Investigation

BACKGROUND: Bisubstrate enzymes, such as 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), exist in solution as an ensemble of conformations. 17beta-HSD1 catalyzes the last step of the biosynthesis of estradiol and, thus, it is a potentially attractive target for breast cancer treatment. METHODOLOGY/PRINCIPAL FINDINGS: To elucidate the conformational transitions of its catalytic cycle, a structural analysis of all available crystal structures was performed and representative conformations were assigned to each step of the putative kinetic mechanism. To cover most of the conformational space, all-atom molecular dynamic simulations were performed using the four crystallographic structures best describing apoform, opened, occluded and closed state of 17beta-HSD1 as starting structures. With three of them, binary and ternary complexes were built with NADPH and NADPH-estrone, respectively, while two were investigated as apoform. Free energy calculations were performed in order to judge more accurately which of the MD complexes describes a specific kinetic step. CONCLUSIONS/SIGNIFICANCE: Remarkably, the analysis of the eight long range trajectories resulting from this multi-trajectory/-complex approach revealed an essential role played by the backbone and side chain motions, especially of the betaF alphaG'-loop, in cofactor and substrate binding. Thus, a selected-fit mechanism is suggested for 17beta-HSD1, where ligand-binding induced concerted motions of the FG-segment and the C-terminal part guide the enzyme along its preferred catalytic pathway. Overall, we could assign different enzyme conformations to the five steps of the random bi-bi kinetic cycle of 17beta-HSD1 and we could postulate a preferred pathway for it. This study lays the basis for more-targeted biochemical studies on 17beta-HSD1, as well as for the design of specific inhibitors of this enzyme. Moreover, it provides a useful guideline for other enzymes, also characterized by a rigid core and a flexible region directing their catalysis

A LigA Three-Domain Region Protects Hamsters from Lethal Infection by Leptospira interrogans

The leptospiral LigA protein consists of 13 bacterial immunoglobulin-like (Big) domains and is the only purified recombinant subunit vaccine that has been demonstrated to protect against lethal challenge by a clinical isolate of Leptospira interrogans in the hamster model of leptospirosis. We determined the minimum number and location of LigA domains required for immunoprotection. Immunization with domains 11 and 12 was found to be required but insufficient for protection. Inclusion of a third domain, either 10 or 13, was required for 100% survival after intraperitoneal challenge with Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130. As in previous studies, survivors had renal colonization; here, we quantitated the leptospiral burden by qPCR to be 1.2×103 to 8×105 copies of leptospiral DNA per microgram of kidney DNA. Although renal histopathology in survivors revealed tubulointerstitial changes indicating an inflammatory response to the infection, blood chemistry analysis indicated that renal function was normal. These studies define the Big domains of LigA that account for its vaccine efficacy and highlight the need for additional strategies to achieve sterilizing immunity to protect the mammalian host from leptospiral infection and its consequences

Deciphering the Code for Retroviral Integration Target Site Selection

Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses