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Research Article
Tian-Jiao Wei
Northeast Institute of Geography and Agroecology Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2550-5609
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Key message Candidate pathways for alkaline tolerance in alfalfa seedlings were identified; these included those for homeostasis of ions and redox status, biosynthesis of phenylpropanoids, flavonoids, and amino acids, and MAPK signaling.
Abstract Soil alkalization severely limits plant growth and development; however, the mechanisms of alkaline response remain largely unknown. In this study, we performed physiological and transcriptomic analyses using two alfalfa cultivars (Medicago sativa L.) with different sensitivities to alkaline conditions. The chlorophyll content and shoot fresh weight drastically declined in the alkaline-sensitive cultivar Algonquin (AG) following alkaline treatment (0-25 mM Na2CO3 solution), while the alkaline-tolerant cultivar Gongnong NO.1 (GN) maintained relatively stable growth and chlorophyll content. Physiological analysis revealed that compared with AG, GN had higher contents of Ca2+ and Mg2+; the ratios of Ca2+ and Mg2+ to Na+, proline and soluble sugar, and enzyme activities of peroxidase (POD) and catalase (CAT) decreased under the alkaline conditions. Further, transcriptomic analysis identified three categories of alkaline-responsive differentially expressed genes (DEGs) between the two cultivars: 48 genes commonly induced in both the cultivars (CAR), 574 genes from the tolerant cultivar (TAR), and 493 genes from the sensitive cultivar (SAR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that CAR genes were mostly involved in phenylpropanoid biosynthesis, lipid metabolism, and DNA replication and repair; TAR genes were significantly enriched in metabolic pathways, biosynthesis of secondary metabolites, MAPK signaling pathway, and flavonoid and amino acid biosynthesis; the SAR genes were specifically enriched in vitamin B6 metabolism. Taken together, the results identified candidate pathways associated with genetic variation in response to alkaline stress, providing novel insights into the mechanisms underlying alkaline tolerance in alfalfa.
Keywords
Medicago sativa L., Alfalfa, Alkaline stress, Transcriptomics, Alkaline-responsive genes
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This is a list of supplementary files associated with this preprint. Click to download.
- Fig.S1Fig.S2andFig.S3.docx
Fig. S1 The percentage of membrane injury (MI) and malonaldehyde content (MDA) in the leaves of alfalfa seedlings in response to alkaline stress. A The membrane injury (MI) of alfalfa seedlings was determined after 0 mM, 10 mM, 15 mM, 20 mM, and 25 mM Na2CO3 treatment for 7 days. * indicates significant differences at P < 0.05 (Student’s t test). B Changes in MDA content in the leaves of alfalfa seedlings under control or alkaline stress from 1 day, 3 days, 5 days, and 7 days. Different lowercase letters represent significant differences between cultivars at P < 0.05 (Duncan’s multiple range test). The values are the means ± standard errors Fig. S2 Expression profiles of all DEGs. The heatmap of DEGs and the FPKM distribution of all DEGs obtained by hierarchical cluster analysis. Each column in the figure represents a sample, and each row represents a gene. The colors in the graph indicate the magnitude of gene expression (log10 (FPKM + 0.01)) in the sample. Red indicates that the gene is highly expressed in the sample, and blue indicates that the gene expression level was low Fig. S3 The correlation between RNA-seq and qRT-PCR
- TableS1.xlsx
Table S1. Primers used in this study
- TableS2.xlsx
Table S2 Summary of clean reads and mapping ratio
- TableS3.xls
Table S3 FPKM of All DEGs in TGN vs CGN and TAG vs CAG
- TableS4.xls
Table S4 Log2 (Fold change) of DEGs in CAR, TAR and SAR
- TableS5.xlsx
Table S5 KEGG pathways of CAR, TAR and SAR
- TableS6.xlsx
Table S6 Validation of NGS data by qRT-PCR
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Tian-Jiao Wei
Northeast Institute of Geography and Agroecology Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2550-5609
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
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History
CURRENT STATUS: Posted
View the published article at Ecotoxicology and Environmental Safety.
Version 1
Posted 29 Mar, 2021
No community comments so far
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Comments: 0
PDF Downloads: ...
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License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Ecotoxicology and Environmental Safety.
Key message Candidate pathways for alkaline tolerance in alfalfa seedlings were identified; these included those for homeostasis of ions and redox status, biosynthesis of phenylpropanoids, flavonoids, and amino acids, and MAPK signaling.
Abstract Soil alkalization severely limits plant growth and development; however, the mechanisms of alkaline response remain largely unknown. In this study, we performed physiological and transcriptomic analyses using two alfalfa cultivars (Medicago sativa L.) with different sensitivities to alkaline conditions. The chlorophyll content and shoot fresh weight drastically declined in the alkaline-sensitive cultivar Algonquin (AG) following alkaline treatment (0-25 mM Na2CO3 solution), while the alkaline-tolerant cultivar Gongnong NO.1 (GN) maintained relatively stable growth and chlorophyll content. Physiological analysis revealed that compared with AG, GN had higher contents of Ca2+ and Mg2+; the ratios of Ca2+ and Mg2+ to Na+, proline and soluble sugar, and enzyme activities of peroxidase (POD) and catalase (CAT) decreased under the alkaline conditions. Further, transcriptomic analysis identified three categories of alkaline-responsive differentially expressed genes (DEGs) between the two cultivars: 48 genes commonly induced in both the cultivars (CAR), 574 genes from the tolerant cultivar (TAR), and 493 genes from the sensitive cultivar (SAR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that CAR genes were mostly involved in phenylpropanoid biosynthesis, lipid metabolism, and DNA replication and repair; TAR genes were significantly enriched in metabolic pathways, biosynthesis of secondary metabolites, MAPK signaling pathway, and flavonoid and amino acid biosynthesis; the SAR genes were specifically enriched in vitamin B6 metabolism. Taken together, the results identified candidate pathways associated with genetic variation in response to alkaline stress, providing novel insights into the mechanisms underlying alkaline tolerance in alfalfa.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
- Fig.S1Fig.S2andFig.S3.docx
Fig. S1 The percentage of membrane injury (MI) and malonaldehyde content (MDA) in the leaves of alfalfa seedlings in response to alkaline stress. A The membrane injury (MI) of alfalfa seedlings was determined after 0 mM, 10 mM, 15 mM, 20 mM, and 25 mM Na2CO3 treatment for 7 days. * indicates significant differences at P < 0.05 (Student’s t test). B Changes in MDA content in the leaves of alfalfa seedlings under control or alkaline stress from 1 day, 3 days, 5 days, and 7 days. Different lowercase letters represent significant differences between cultivars at P < 0.05 (Duncan’s multiple range test). The values are the means ± standard errors Fig. S2 Expression profiles of all DEGs. The heatmap of DEGs and the FPKM distribution of all DEGs obtained by hierarchical cluster analysis. Each column in the figure represents a sample, and each row represents a gene. The colors in the graph indicate the magnitude of gene expression (log10 (FPKM + 0.01)) in the sample. Red indicates that the gene is highly expressed in the sample, and blue indicates that the gene expression level was low Fig. S3 The correlation between RNA-seq and qRT-PCR
- TableS1.xlsx
Table S1. Primers used in this study
- TableS2.xlsx
Table S2 Summary of clean reads and mapping ratio
- TableS3.xls
Table S3 FPKM of All DEGs in TGN vs CGN and TAG vs CAG
- TableS4.xls
Table S4 Log2 (Fold change) of DEGs in CAR, TAR and SAR
- TableS5.xlsx
Table S5 KEGG pathways of CAR, TAR and SAR
- TableS6.xlsx
Table S6 Validation of NGS data by qRT-PCR
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