ReviewThe Journal of biological chemistry2018
Engineering abiotic stress response in plants for biomass production.
Review in The Journal of biological chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- CRISPR/Cas technology: fueling the future of Biofuel production with sugarcane.Functional & integrative genomics · 2024Review
- Review
- Genome-Wide Identification and Expression Analysis ofPlants (Basel, Switzerland) · 2022Article
- Potential Use of Hyperspectral Reflectance as a High-Throughput Nondestructive Phenotyping Tool for Assessing Salt Tolerance in Advanced Spring Wheat Lines under Field Conditions.Plants (Basel, Switzerland) · 2021Article
- Engineering Climate-Change-Resilient Crops: New Tools and Approaches.International journal of molecular sciences · 2021Review
- Physico-chemical properties and toxicological effects on plant and algal models of carbon nanosheets from a nettle fibre clone.Scientific reports · 2021Article
- Article
- Article
- Plant Cell Walls Tackling Climate Change: Insights into Plant Cell Wall Remodeling, Its Regulation, and Biotechnological Strategies to Improve Crop Adaptations and Photosynthesis in Response to Global Warming.Plants (Basel, Switzerland) · 2020Review
- Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat.Frontiers in plant science · 2020Article
- Drought and heat stress-related proteins: an update about their functional relevance in imparting stress tolerance in agricultural crops.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
- Nanobiotechnology approaches for engineering smart plant sensors.Nature nanotechnology · 2019Review
- Introduction to the Thematic Minireview Series: Green biological chemistry.The Journal of biological chemistry · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the major challenges in today's agriculture is to achieve enhanced plant growth and biomass even under adverse environmental conditions. Recent advancements in genetics and molecular biology have enabled the identification of a complex signaling network contributing toward plant growth and development on the one hand and abiotic stress response on the other hand. As an outcome of these studies, three major approaches have been identified as having the potential to improve biomass production in plants under abiotic stress conditions. These approaches deal with having changes in the following: (i) plant-microbe interactions; (ii) cell wall biosynthesis; and (iii) phytohormone levels. At the same time, employing functional genomics and genetics-based approaches, a very large number of genes have been identified that play a key role in abiotic stress tolerance. Our Minireview is an attempt to unveil the cross-talk that has just started to emerge between the transcriptional circuitries for biomass production and abiotic stress response. This knowledge may serve as a valuable resource to eventually custom design the crop plants for higher biomass production, in a more sustainable manner, in marginal lands under variable climatic conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.