Impact of genetically engineered maize on agronomic, environmental and toxicological traits a meta-analysis of 21 years of field data, Scientific Reports, 2018-02-09

Despite the extensive cultivation of genetically engineered (GE) maize and considerable number of scientific reports on its agro-environmental impact, the risks and benefits of GE maize are still being debated and concerns about safety remain. This meta-analysis aimed at increasing knowledge on agronomic, environmental and toxicological traits of GE maize by analyzing the peer-reviewed literature (from 1996 to 2016) on yield, grain quality, non-target organisms (NTOs), target organisms (TOs) and soil biomass decomposition. Results provided strong evidence that GE maize performed better than its near isogenic line grain yield was 5.6 to 24.5% higher with lower concentrations of mycotoxins (−28.8%), fumonisin (−30.6%) and thricotecens (−36.5%). The NTOs analyzed were not affected by GE maize, except for Braconidae, represented by a parasitoid of European corn borer, the target of Lepidoptera active Bt maize. Biogeochemical cycle parameters such as lignin content in stalks and leaves did not vary, whereas biomass decomposition was higher in GE maize. The results support the cultivation of GE maize, mainly due to enhanced grain quality and reduction of human exposure to mycotoxins. Furthermore, the reduction of the parasitoid of the target and the lack of consistent effects on other NTOs are confirmed.

scientific reports genetics 500+-users 2018

Human Skeletal Muscle Possesses an Epigenetic Memory of Hypertrophy, Scientific Reports, 2018-01-24

It is unknown if adult human skeletal muscle has an epigenetic memory of earlier encounters with growth. We report, for the first time in humans, genome-wide DNA methylation (850,000 CpGs) and gene expression analysis after muscle hypertrophy (loading), return of muscle mass to baseline (unloading), followed by later hypertrophy (reloading). We discovered increased frequency of hypomethylation across the genome after reloading (18,816 CpGs) versus earlier loading (9,153 CpG sites). We also identified AXIN1, GRIK2, CAMK4, TRAF1 as hypomethylated genes with enhanced expression after loading that maintained their hypomethylated status even during unloading where muscle mass returned to control levels, indicating a memory of these genes methylation signatures following earlier hypertrophy. Further, UBR5, RPL35a, HEG1, PLA2G16, SETD3 displayed hypomethylation and enhanced gene expression following loading, and demonstrated the largest increases in hypomethylation, gene expression and muscle mass after later reloading, indicating an epigenetic memory in these genes. Finally, genes; GRIK2, TRAF1, BICC1, STAG1 were epigenetically sensitive to acute exercise demonstrating hypomethylation after a single bout of resistance exercise that was maintained 22 weeks later with the largest increase in gene expression and muscle mass after reloading. Overall, we identify an important epigenetic role for a number of largely unstudied genes in muscle hypertrophymemory.

scientific reports genetics 500+-users 2018

Identification of Pre-Existing Adaptive Immunity to Cas9 Proteins in Humans, bioRxiv, 2018-01-06

AbstractThe CRISPR-Cas9 system has proven to be a powerful tool for genome editing, allowing for the precise modification of specific DNA sequences within a cell. Many efforts are currently underway to use the CRISPR-Cas9 system for the therapeutic correction of human genetic diseases. The most widely used homologs of the Cas9 protein are derived from the bacteria Staphylococcus aureus (S. aureus) and Streptococcus pyogenes (S. pyogenes). Based on the fact that these two bacterial species cause infections in the human population at high frequencies, we looked for the presence of pre-existing adaptive immune responses to their respective Cas9 homologs, SaCas9 (S. aureus homolog of Cas9) and SpCas9 (S. pyogenes homolog of Cas9). To determine the presence of anti-Cas9 antibodies, we probed for the two homologs using human serum and were able to detect antibodies against both, with 79% of donors staining against SaCas9 and 65% of donors staining against SpCas9. Upon investigating the presence of antigen-specific T-cells against the two homologs in human peripheral blood, we found anti-SaCas9 T-cells in 46% of donors. Upon isolating, expanding, and conducting antigen re-stimulation experiments on several of these donors’ anti-SaCas9 T-cells, we observed an SaCas9-specific response confirming that these T-cells were antigen-specific. We were unable to detect antigen-specific T-cells against SpCas9, although the sensitivity of the assay precludes us from concluding that such T-cells do not exist. Together, this data demonstrates that there are pre-existing humoral and cell-mediated adaptive immune responses to Cas9 in humans, a factor which must be taken into account as the CRISPR-Cas9 system moves forward into clinical trials.

biorxiv immunology 500+-users 2018

Deep image reconstruction from human brain activity, bioRxiv, 2017-12-29

Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases or to the matching to exemplars. Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features. Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed reconstructs or generates images from brain activity, not simply matches to exemplars. A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.

biorxiv neuroscience 500+-users 2017

 

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