ArticlePloS one2018
Elucidation and analyses of the regulatory networks of upland and lowland ecotypes of switchgrass in response to drought and salt stresses.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.Nature communications · 2022Article
- Temporal regulation of cold transcriptional response in switchgrass.Frontiers in plant science · 2022Article
- Transcriptomic and metabolomic analyses of Lycium ruthenicum and Lycium barbarum fruits during ripening.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Switchgrass is an important bioenergy crop typically grown in marginal lands, where the plants must often deal with abiotic stresses such as drought and salt. Alamo is known to be more tolerant to both stress types than Dacotah, two ecotypes of switchgrass. Understanding of their stress response and adaptation programs can have important implications to engineering more stress tolerant plants. We present here a computational study by analyzing time-course transcriptomic data of the two ecotypes to elucidate and compare their regulatory systems in response to drought and salt stresses. A total of 1,693 genes (target genes or TGs) are found to be differentially expressed and possibly regulated by 143 transcription factors (TFs) in response to drought stress together in the two ecotypes. Similarly, 1,535 TGs regulated by 110 TFs are identified to be involved in response to salt stress. Two regulatory networks are constructed to predict their regulatory relationships. In addition, a time-dependent hidden Markov model is derived for each ecotype responding to each stress type, to provide a dynamic view of how each regulatory network changes its behavior over time. A few new insights about the response mechanisms are predicted from the regulatory networks and the time-dependent models. Comparative analyses between the network models of the two ecotypes reveal key commonalities and main differences between the two regulatory systems. Overall, our results provide new information about the complex regulatory mechanisms of switchgrass responding to drought and salt stresses.
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