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Author: Héctor Cervera; Silvia Ambrós; Guillermo P. Bernet; Guillermo Rodrigo; Santiago F. Elena
Title: Viral fitness predicts the magnitude and direction of perturbations in the infected host transcriptome
  • Document date: 2017_10_20
  • ID: 0qmsripp_15_0
    Snippet: The number of altered genes depends on viral fitness. Next, following the same rationale than in the previous section, we sought to determine whether the number of DEGs also depends on the difference in fitness between WT and the mutant TEV genotypes. In this case, we hypothesized that the overlap in the lists of DEGs must be similar for WT and viruses of equivalent fitness (e.g., PC48 or PC55), whereas the magnitude of the overlap between DEG li.....
    Document: The number of altered genes depends on viral fitness. Next, following the same rationale than in the previous section, we sought to determine whether the number of DEGs also depends on the difference in fitness between WT and the mutant TEV genotypes. In this case, we hypothesized that the overlap in the lists of DEGs must be similar for WT and viruses of equivalent fitness (e.g., PC48 or PC55), whereas the magnitude of the overlap between DEG lists would decrease as differences in fitness exist. Of particular interest following all results presented above is the similarity between PC95, a mutant of the replicase NIb gene, and CLA11, a mutant of the VSR HC-Pro gene. These two mutations led to close fitness values ( Fig. 2A) , but also resulted in significantly similar gene expression profiles (Fig. 4B ). At first sight, one may argue that their impact in transcriptomic profiles should be different since these mutations affect virus proteins that are functionally unrelated. However, our results suggest that the effects on the overall virus-host interaction of each mutant are canalized in the same way. This clearly exemplifies that viral fitness, despite its incompleteness, is a figure that contains high information about the virus-host interaction. The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/206789 doi: bioRxiv preprint functions that appear as enriched among negatively correlated DEGs. Together, these results suggest that positively correlated DEGs play a role in the transcriptional regulation of host defenses. By contrast, DEGs with negative correlation between expression and TEV fitness participate more in catalytic and transport activities than genes with positive correlation, suggesting a redirection of resources by the host to face the viral infection, which is not independent of viral fitness. VQ29 is a negative transcriptional regulator of light-mediated inhibition of hypocotyl elongation that likely promotes the transcriptional activation of phytochrome interacting factor 1 (PIF1) during early seedling development, participates in the jasmonic acid-mediated (JA) plant basal defense and the VQ proteins interact with WRKY transcription factors 43 . GASA1 encodes for a gibberellin-and brassinosteroid-regulated protein possibly involved in cell elongation 44 , also reported to be involved in resistance to abiotic stress through ROS signaling 45 . PLAT1 encodes for a lipase/lipoxygenase that promotes abiotic stress tolerance 46 RBCS3B is involved in carbon fixation during photosynthesis and in yielding sufficient Rubisco content 48 . AGL20 is a DNA-binding MADS-box transcription activator modulating the . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/206789 doi: bioRxiv preprint expression of homeotic genes involved in flower development and maintenance of inflorescence meristem identity, transitions between vegetative stages of plant development and in tolerance to cold 49 . FDM1 is an SGS3-like protein that acts in RNA-directed DNA methylation participating in the RNA silencing defense pathway 50 . GBSS1 is involved in glucan biosynthesis and responsible of amylase synthesis essential for plant growth and other developmental processes 51 . Relative expression data were calculated using the DDC T method normalized by each one of the two ref

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