Back

Meta-Analysis of Public RNA Sequencing Data of Abscisic Acid-Related Abiotic Stresses in Arabidopsis thaliana

Shintani, M.; Tamura, K.; Bono, H.

2023-04-18 plant biology
10.1101/2023.04.17.537107 bioRxiv
Show abstract

Abiotic stresses such as drought, salinity, and cold negatively affect plant growth and crop productivity. Understanding the molecular mechanisms underlying plant responses to these stressors is essential for stress tolerance in crops. The plant hormone abscisic acid (ABA) is significantly increased upon abiotic stressors, inducing physiological responses to adapt to stress and regulate gene expression. Although many studies have examined the components of established stress signaling pathways, few have explored other unknown elements. This study aimed to identify novel stress-responsive genes in plants by performing a meta-analysis of public RNA sequencing (RNA-Seq) data on Arabidopsis thaliana, focusing on five ABA-related stress conditions (ABA, Salt, Dehydration, Osmotic, and Cold). The meta-analysis of 216 paired datasets from five stress conditions was conducted, and differentially expressed genes were identified by introducing a new metric, called TN (stress-treated (T) and non-treated (N))-score. We revealed that 14 genes were commonly upregulated and 8 genes were commonly downregulated across all five treatments, including some that were not previously associated with these stress responses. On the other hand, some genes regulated by salt, dehydration, and osmotic treatments were not regulated by exogenous ABA or cold stress, suggesting that they may be involved in the plant response to dehydration independent of ABA. Our meta-analysis revealed a list of candidate genes with unknown molecular mechanisms in ABA-dependent and ABA-independent stress responses. These genes could be valuable resources for selecting genome editing targets and potentially contribute to the discovery of novel stress tolerance mechanisms and pathways in plants.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Physiologia Plantarum
39 papers in training set
Top 0.1%
9.5%
2
Plant Molecular Biology
20 papers in training set
Top 0.1%
8.7%
3
Plant Physiology
238 papers in training set
Top 0.8%
7.7%
4
Environmental and Experimental Botany
12 papers in training set
Top 0.1%
7.7%
5
Plant Direct
95 papers in training set
Top 0.4%
6.6%
6
Journal of Experimental Botany
219 papers in training set
Top 1%
6.1%
7
The Plant Journal
215 papers in training set
Top 1%
5.4%
50% of probability mass above
8
Frontiers in Plant Science
256 papers in training set
Top 2%
5.4%
9
Plant Science
31 papers in training set
Top 0.1%
5.4%
10
Plant, Cell & Environment
78 papers in training set
Top 0.6%
4.2%
11
Plant and Cell Physiology
52 papers in training set
Top 0.4%
3.4%
12
Plant Stress
12 papers in training set
Top 0.1%
2.3%
13
Planta
18 papers in training set
Top 0.3%
1.9%
14
International Journal of Molecular Sciences
494 papers in training set
Top 8%
1.7%
15
BMC Plant Biology
57 papers in training set
Top 0.8%
1.7%
16
Plant Physiology and Biochemistry
20 papers in training set
Top 0.5%
1.7%
17
PLOS ONE
5266 papers in training set
Top 51%
1.5%
18
Frontiers in Genetics
230 papers in training set
Top 3%
1.4%
19
BMC Genomics
406 papers in training set
Top 5%
1.4%
20
Plants
43 papers in training set
Top 1%
1.3%
21
Scientific Reports
3612 papers in training set
Top 67%
1.1%
22
Plant Communications
36 papers in training set
Top 0.7%
1.1%
23
New Phytologist
346 papers in training set
Top 4%
1.0%
24
Plant Cell Reports
17 papers in training set
Top 0.6%
0.8%
25
Journal of Agricultural and Food Chemistry
15 papers in training set
Top 0.6%
0.8%
26
Plant Methods
42 papers in training set
Top 1%
0.6%
27
Horticulture Research
47 papers in training set
Top 1%
0.6%
28
eLife
5828 papers in training set
Top 70%
0.6%