Variation in mitochondrial function in hypoxia-sensitive and hypoxia-tolerant human glioma cells

We have shown previously that human glioblastoma multiforme cells vary in their ability to survive under hypoxic conditions. Under oxygen limiting conditions, hypoxia-tolerant cells decrease their oxygen consumption rate whereas hypoxia-sensitive cells continue to consume oxygen at a relatively steady rate until the oxygen supply becomes exhausted. We now show that hypoxia-tolerant and hypoxia-sensitive cells exhibit distinct patterns of mitochondrial function in response to hypoxic challenge. Hypoxia-tolerant cell lines retain stable mitochondrial membrane potential and ATP concentration when incubated under oxygen limiting conditions. In addition, hypoxia-tolerant cell lines are consistently more sensitive to a wide spectrum of inhibitors of mitochondrial function than are hypoxia-sensitive cells. In contrast, the hypoxia-sensitive cells are unable to maintain stable mitochondrial membrane potential and ATP levels when incubated at reduced oxygen tension. These results demonstrate significant differences in the mitochondrial function between these two phenotypes and reinforce previous data that suggest a regulatory role for mitochondria in the development of hypoxia tolerance

Conditional meta-analysis stratifying on detailed HLA genotypes identifies a novel type 1 diabetes locus around TCF19 in the MHC

The human leukocyte antigen (HLA) class II genes HLA-DRB1, -DQA1 and -DQB1 are the strongest genetic factors for type 1 diabetes (T1D). Additional loci in the major histocompatibility complex (MHC) are difficult to identify due to the region’s high gene density and complex linkage disequilibrium (LD). To facilitate the association analysis, two novel algorithms were implemented in this study: one for phasing the multi-allelic HLA genotypes in trio families, and one for partitioning the HLA strata in conditional testing. Screening and replication were performed on two large and independent datasets: the Wellcome Trust Case–Control Consortium (WTCCC) dataset of 2,000 cases and 1,504 controls, and the T1D Genetics Consortium (T1DGC) dataset of 2,300 nuclear families. After imputation, the two datasets have 1,941 common SNPs in the MHC, of which 22 were successfully tested and replicated based on the statistical testing stratifying on the detailed DRB1 and DQB1 genotypes. Further conditional tests using the combined dataset confirmed eight novel SNP associations around 31.3 Mb on chromosome 6 (rs3094663, p = 1.66 × 10−11 and rs2523619, p = 2.77 × 10−10 conditional on the DR/DQ genotypes). A subsequent LD analysis established TCF19, POU5F1, CCHCR1 and PSORS1C1 as potential causal genes for the observed association

Hough-Transform-Based Interpolation Scheme for Generating Accurate Dense Spatial Maps of Air Pollutants from Sparse Sensing

Part 2: Air and ClimateInternational audienceAir pollution is a significant health risk factor and causes many negative effects on the environment. Thus, arises the need for studying and assessing air-quality. Today, air-pollution assessment is mostly based on data acquired from Air Quality Monitoring (AQM) stations. These AQM stations provide continuous measurements and considered to be accurate; however, they are expensive to build and operate, thus scattered sparingly. To cope with this limitation, typically, the information obtained from those measurements is generalized with interpolation methods such as IDW or Kriging. Yet, the mathematical basis of those schemes defines that pollution extremum values are obtained at the measuring points. In addition, they are not considering the location of the pollution source or any physicochemical characteristics of pollutant hence do not reveal the real spatial air-pollution patterns. This research introduces a new interpolation scheme which breaks the interpolation process into two stages. At the first stage, the source of pollution and its estimated emission rate are inferred through a detection procedure which is based on the Hough Transform. At the second stage, based on the detected source location and emission, spatial dense pollution maps are created. The method requires, for its computation, to assume a dispersion model. To this end, any model can be used as sophisticated as it may be. Spatial maps created with simplified dispersion models in a computational simulation, show that the suggested interpolation scheme manages to create more accurate and more physically reasonable maps than the state-of-the-art

In pediatric lymphoblastic leukemia of B-cell origin, a small population of primitive blast cells is noncycling, suggesting them to be leukemia stem cell candidates

Kinetic investigations in pediatric acute lymphoblastic leukemia (ALL) are based on all blast cells and, therefore, reflect the proliferative characteristics of the predominant immunophenotype of leukemic cells. Nothing is known about proliferation of immunologically defined rare subpopulations of leukemic cells. In this study, mononuclear cells from the bone marrow of 15 children with untreated CD19 B-cell precursor ALL were examined for proliferative features according to the immunophenotype. After exclusion of highly proliferating residual normal hematopoietic cells, ∼ 3% of blast cells were CD19 and showed a low percentage of cells in S-phase assessed by the bromodeoxyuridine labeling index (BrdU-LI): median BrdU-LI, 0.19% [interquartile range (IQR), 0.15-0.40%]. In contrast, a median BrdU-LI of 7.2% (IQR, 5.7-8.8%) was found for the major CD19 blast cell compartment. Staining smears of sorted CD19 cells for CD10 or CD34 revealed a small fraction of CD19CD10 or CD19CD34 blast cells. These cells were almost nonproliferating with a median BrdU-LI of <0.1% (IQR, 0-0.2%). This proliferative behavior is suggestive of a stem/progenitor cell function and, in addition, the low proliferative activity might render them more resistant to an antiproliferation-based chemotherapy. However, xenotransplantation experiments will be necessary to demonstrate a possible stem cell function