scholarly journals High-throughput profiling and analysis of plant responses over time to abiotic stress

Plant Direct ◽  
2017 ◽  
Vol 1 (4) ◽  
pp. e00023 ◽  
Author(s):  
Kira M. Veley ◽  
Jeffrey C. Berry ◽  
Sarah J. Fentress ◽  
Daniel P. Schachtman ◽  
Ivan Baxter ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
High Throughput ◽  
Plant Responses ◽  
Over Time

scholarly journals High-throughput profiling and analysis of plant responses over time to abiotic stress

2017 ◽  
Author(s):  
Kira M. Veley ◽  
Jeffrey C. Berry ◽  
Sarah J. Fentress ◽  
Daniel P. Schachtman ◽  
Ivan Baxter ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
High Throughput ◽  
Stress Responses ◽  
Low Cost ◽  
Abiotic Stress Tolerance ◽  
Crop Improvement ◽  
Plant Responses ◽  
Nitrogen Deprivation ◽  
Plant Nitrogen ◽  
Over Time

ABSTRACTSorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G × E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G × E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.

scholarly journals Identification of conserved and novel miRNAs responsive to heat stress in flowering Chinese cabbage using high-throughput sequencing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Waqas Ahmed ◽  
Yanshi Xia ◽  
Hua Zhang ◽  
Ronghua Li ◽  
Guihua Bai ◽  
...  
Keyword(s):  
Heat Stress ◽  
Abiotic Stress ◽  
High Throughput ◽  
Chinese Cabbage ◽  
Stress Responses ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Plant Responses ◽  
Limited Information ◽  
Flowering Chinese Cabbage

Abstract Plant microRNAs (miRNAs) are noncoding and endogenous key regulators that play significant functions in regulating plant responses to stress, and plant growth and development. Heat stress is a critical abiotic stress that reduces the yield and quality of flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee). However, limited information is available on whether miRNAs are involved in the regulation of heat stress in B. campestris. A high-throughput sequencing approach was used to identify novel and conserved heat-responsive miRNAs in four small RNA libraries of flowering Chinese cabbage using leaves collected at 0 h, 1 h, 6 h and 12 h after a 38 °C heat-stress treatment. The analysis identified 41 conserved miRNAs (belonging to 19 MIR families), of which MIR156, MIR159, MIR168, MIR171 and MIR1885 had the most abundant molecules. Prediction and evaluation of novel miRNAs using the unannotated reads resulted in 18 candidate miRNAs. Differential expression analysis showed that most of the identified miRNAs were downregulated in heat-treated groups. To better understand functional importance, bioinformatic analysis predicted 432 unique putative target miRNAs involved in cells, cell parts, catalytic activity, cellular processes and abiotic stress responses. Furthermore, the Kyoto Encyclopedia of Genes and Genomes maps of flowering Chinese cabbage identified the significant role of miRNAs in stress adaptation and stress tolerance, and in several mitogen-activated protein kinases signaling pathways including cell death. This work presents a comprehensive study of the miRNAs for understanding the regulatory mechanisms and their participation in the heat stress of flowering Chinese cabbage.

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 High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals Potassium: A Vital Regulator of Plant Responses and Tolerance to Abiotic Stresses

Agronomy ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 31 ◽  
Author(s):  
Mirza Hasanuzzaman ◽  
M. Bhuyan ◽  
Kamrun Nahar ◽  
Md. Hossain ◽  
Jubayer Mahmud ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
Stress Tolerance ◽  
Molecular Mechanisms ◽  
Abiotic Stress Tolerance ◽  
Ion Homeostasis ◽  
Enzyme Activation ◽  
Regulatory Function ◽  
Plant Responses ◽  
Plant Structure ◽  
Physiological Processes

Among the plant nutrients, potassium (K) is one of the vital elements required for plant growth and physiology. Potassium is not only a constituent of the plant structure but it also has a regulatory function in several biochemical processes related to protein synthesis, carbohydrate metabolism, and enzyme activation. Several physiological processes depend on K, such as stomatal regulation and photosynthesis. In recent decades, K was found to provide abiotic stress tolerance. Under salt stress, K helps to maintain ion homeostasis and to regulate the osmotic balance. Under drought stress conditions, K regulates stomatal opening and helps plants adapt to water deficits. Many reports support the notion that K enhances antioxidant defense in plants and therefore protects them from oxidative stress under various environmental adversities. In addition, this element provides some cellular signaling alone or in association with other signaling molecules and phytohormones. Although considerable progress has been made in understanding K-induced abiotic stress tolerance in plants, the exact molecular mechanisms of these protections are still under investigation. In this review, we summarized the recent literature on the biological functions of K, its uptake, its translocation, and its role in plant abiotic stress tolerance.

High temperatures modify plant responses to abiotic stress conditions

2020 ◽  
Vol 170 (3) ◽  
pp. 335-344 ◽  
Author(s):  
Damián Balfagón ◽  
Sara I. Zandalinas ◽  
Ron Mittler ◽  
Aurelio Gómez‐Cadenas
Keyword(s):  
Abiotic Stress ◽  
High Temperatures ◽  
Stress Conditions ◽  
Plant Responses

scholarly journals Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines

2019 ◽  
Vol 9 (10) ◽  
pp. 3369-3380 ◽  
Author(s):  
Mehdi Momen ◽  
Malachy T. Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
Abiotic Stress ◽  
High Throughput ◽  
Legendre Polynomials ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Basis Functions ◽  
Random Regression ◽  
Growth Stages ◽  
Growth Environment ◽  
B Splines

Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.

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