Plant responses to global change: next generation biogeography

2016 ◽  
Vol 37 (2) ◽  
pp. 93-119 ◽  
Author(s):  
Jeremy S. Johnson ◽  
Keith D. Gaddis ◽  
David M. Cairns ◽  
Charles W. Lafon ◽  
Konstantin V. Krutovsky
Keyword(s):  
Global Change ◽  
Plant Responses ◽  
Next Generation

scholarly journals Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Metabolites ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 445
Author(s):  
Morena M. Tinte ◽  
Kekeletso H. Chele ◽  
Justin J. J. van der Hooft ◽  
Fidele Tugizimana
Keyword(s):  
Machine Learning ◽  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Industrial Revolution ◽  
Chemical Space ◽  
Big Data Analytics ◽  
Plant Responses ◽  
Next Generation ◽  
Computational Tools

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

scholarly journals Ecometabolomics for a Better Understanding of Plant Responses and Acclimation to Abiotic Factors Linked to Global Change

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 239 ◽  
Author(s):  
Jordi Sardans ◽  
Albert Gargallo-Garriga ◽  
Otmar Urban ◽  
Karel Klem ◽  
Tom W.N. Walker ◽  
...  
Keyword(s):  
Amino Acids ◽  
Heat Stress ◽  
Global Change ◽  
Secondary Metabolites ◽  
De Novo ◽  
Abiotic Factors ◽  
Soluble Sugars ◽  
Metabolic Responses ◽  
Plant Responses ◽  
Co2 Concentrations

The number of ecometabolomic studies, which use metabolomic analyses to disentangle organisms’ metabolic responses and acclimation to a changing environment, has grown exponentially in recent years. Here, we review the results and conclusions of ecometabolomic studies on the impacts of four main drivers of global change (increasing frequencies of drought episodes, heat stress, increasing atmospheric carbon dioxide (CO2) concentrations and increasing nitrogen (N) loads) on plant metabolism. Ecometabolomic studies of drought effects confirmed findings of previous target studies, in which most changes in metabolism are characterized by increased concentrations of soluble sugars and carbohydrate derivatives and frequently also by elevated concentrations of free amino acids. Secondary metabolites, especially flavonoids and terpenes, also commonly exhibited increased concentrations when drought intensified. Under heat and increasing N loads, soluble amino acids derived from glutamate and glutamine were the most responsive metabolites. Foliar metabolic responses to elevated atmospheric CO2 concentrations were dominated by greater production of monosaccharides and associated synthesis of secondary metabolites, such as terpenes, rather than secondary metabolites synthesized along longer sugar pathways involving N-rich precursor molecules, such as those formed from cyclic amino acids and along the shikimate pathway. We suggest that breeding for crop genotypes tolerant to drought and heat stress should be based on their capacity to increase the concentrations of C-rich compounds more than the concentrations of smaller N-rich molecules, such as amino acids. This could facilitate rapid and efficient stress response by reducing protein catabolism without compromising enzymatic capacity or increasing the requirement for re-transcription and de novo biosynthesis of proteins.

scholarly journals Genetic approaches to study plant responses to environmental stresses

2018 ◽  
Author(s):  
Khaled Moustafa ◽  
Joanna M. Cross
Keyword(s):  
Gene Expression ◽  
Next Generation Sequencing ◽  
Systems Biology ◽  
Massively Parallel Sequencing ◽  
Environmental Stresses ◽  
Plant Responses ◽  
Rna Seq ◽  
Next Generation ◽  
Rnase Protection ◽  
Generation Sequencing

The assessment of gene expression levels is an important step toward elucidating gene functions temporally and spatially. Decades ago, typical studies were focusing on a few genes individually, whereas now researchers are able to examine whole genomes at once. The upgrade of throughput levels aided the introduction of systems biology approaches whereby cell functional networks can be scrutinized in their entireties to unravel potential functional interacting components. The birth of systems biology goes hand-in-hand with huge technological advancements and enables a fairly rapid detection of all transcripts in studied biological samples. Even so, earlier technologies that were restricted to probing single genes or a subset of genes still have their place in research laboratories. The objective here is to highlight key approaches used in gene expression analysis in plant responses to environmental stresses, or, more generally, any other condition of interest. Northern blots, RNase protection assays, and qPCR are described for their targeted detection of one or a few transcripts at a once. Differential display and serial analysis of gene expression represent non-targeted methods to evaluate expression changes of a significant number of gene transcripts. Finally, microarrays and RNA-seq (next-generation sequencing) contribute to the ultimate goal of identifying and quantifying all transcripts in a cell under conditions or stages of study. Recent examples of applications as well as principles, advantages, and drawbacks of each method are contrasted. We also suggest replacing the term "Next-Generation Sequencing (NGS)" with another less confusing synonym such as "RNA-seq", "high throughput sequencing", or "massively parallel sequencing" to avoid confusion with any future sequencing technologies.

scholarly journals Genetic approaches to study plant responses to environmental stresses

2018 ◽  
Author(s):  
Khaled Moustafa
Keyword(s):  
Gene Expression ◽  
Next Generation Sequencing ◽  
Systems Biology ◽  
Massively Parallel Sequencing ◽  
Environmental Stresses ◽  
Plant Responses ◽  
Rna Seq ◽  
Next Generation ◽  
Rnase Protection ◽  
Generation Sequencing

The assessment of gene expression levels is an important step toward elucidating gene functions temporally and spatially. Decades ago, typical studies were focusing on a few genes individually, whereas now researchers are able to examine whole genomes at once. The upgrade of throughput levels aided the introduction of systems biology approaches whereby cell functional networks can be scrutinized in their entireties to unravel potential functional interacting components. The birth of systems biology goes hand-in-hand with huge technological advancements and enables a fairly rapid detection of all transcripts in studied biological samples. Even so, earlier technologies that were restricted to probing single genes or a subset of genes still have their place in research laboratories. The objective here is to highlight key approaches used in gene expression analysis in plant responses to environmental stresses, or, more generally, any other condition of interest. Northern blots, RNase protection assays, and qPCR are described for their targeted detection of one or a few transcripts at a once. Differential display and serial analysis of gene expression represent non-targeted methods to evaluate expression changes of a significant number of gene transcripts. Finally, microarrays and RNA-seq (next-generation sequencing) contribute to the ultimate goal of identifying and quantifying all transcripts in a cell under conditions or stages of study. Recent examples of applications as well as principles, advantages, and drawbacks of each method are contrasted. We also suggest replacing the term "Next-Generation Sequencing (NGS)" with another less confusing synonym such as "RNA-seq", "high throughput sequencing", or "massively parallel sequencing" to avoid confusion with any future sequencing technologies.

TEACHING THE NEXT GENERATION SCIENCE STANDARDS THROUGH THE LENS OF GLOBAL CHANGE TOPICS

2016 ◽  
Author(s):  
Jessica R. Bean ◽  
◽  
Kristen Mitchell ◽  
Kathleen Zoehfeld ◽  
Lisa D. White
Keyword(s):  
Global Change ◽  
Next Generation Science Standards ◽  
Next Generation ◽  
Science Standards

scholarly journals Diversity-induced plant history and soil history effects modulate plant responses to global change

2021 ◽  
Author(s):  
Peter Dietrich ◽  
Jens Schumacher ◽  
Nico Eisenhauer ◽  
Christiane Roscher
Keyword(s):  
Global Change ◽  
Plant Diversity ◽  
Plant Traits ◽  
Common Garden ◽  
Nitrogen Addition ◽  
Plant Responses ◽  
Growth Strategies ◽  
History Effects ◽  
History Of ◽  
High Diversity

AbstractGlobal change has dramatic impacts on grassland diversity. However, little is known about how fast species can adapt to these changes and how this affects their responses to global change. To close this gap, we performed a common garden experiment testing whether plant responses to global change are influenced by the selection history of the plants and the conditioning history of soil at different levels of plant diversity. Therefore, we collected seeds and took soil samples from 14-year old plant communities of a biodiversity experiment. Offspring of plants from low- and high-diversity communities were either grown in their own soil or in soil of a different community, and were either exposed to drought, increased nitrogen input, or a combination of both. Results show that, under nitrogen addition, offspring of plants selected at high diversity produced more biomass than those selected at low diversity, while drought neutralized differences in biomass production. Moreover, under the influence of global change drivers, mainly soil, and to a lesser extent plant history, influenced the expression of plant traits. Our results show that plant diversity modulates plant-soil interactions and growth strategies of plants, which feedback on the eco-evolutionary pathways of the plants and thus their responses to global change.

Fungal symbionts alter plant responses to global change

2013 ◽  
Vol 100 (7) ◽  
pp. 1445-1457 ◽  
Author(s):  
Stephanie N. Kivlin ◽  
Sarah M. Emery ◽  
Jennifer A. Rudgers
Keyword(s):  
Global Change ◽  
Plant Responses

scholarly journals Fieldwork in landscape ecology

2020 ◽  
Author(s):  
Jesse E. D. Miller ◽  
Carly D. Ziter ◽  
Michael J Koontz
Keyword(s):  
Global Change ◽  
Landscape Ecology ◽  
Critical Role ◽  
Sensor Data ◽  
Next Generation ◽  
Field Methods ◽  
Computational Approaches ◽  
A Priority

Fieldwork has played a critical role in the development of landscape ecology, and it remains essential for addressing contemporary challenges such as understanding the landscape ecology of global change. Advances in technology have expanded the scope of fieldwork to include the deployment of drones and other sensors, and in recent years, researchers have expressed concerns that traditional fieldwork (e.g., organismal observation) may be declining. Continuing to train the next generation of researchers in field methods should be a priority for landscape ecologists. Indeed, there is great potential for combining fieldwork with modern sensor data and computational approaches to advance the field of landscape ecology.

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