Genomic basis underlying the metabolome-mediated drought adaptation of maize

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Abstract

Background

Drought is a major environmental disaster that causes crop yield loss worldwide. Metabolites are involved in various environmental stress responses of plants. However, the genetic control of metabolomes underlying crop environmental stress adaptation remains elusive.

Results

Here, we perform non-targeted metabolic profiling of leaves for 385 maize natural inbred lines grown under well-watered as well as drought-stressed conditions. A total of 3890 metabolites are identified and 1035 of these are differentially produced between well-watered and drought-stressed conditions, representing effective indicators of maize drought response and tolerance. Genetic dissections reveal the associations between these metabolites and thousands of single-nucleotide polymorphisms (SNPs), which represented 3415 metabolite quantitative trait loci (mQTLs) and 2589 candidate genes. 78.6% of mQTLs (2684/3415) are novel drought-responsive QTLs. The regulatory variants that control the expression of the candidate genes are revealed by expression QTL (eQTL) analysis of the transcriptomes of leaves from 197 maize natural inbred lines. Integrated metabolic and transcriptomic assays identify dozens of environment-specific hub genes and their gene-metabolite regulatory networks. Comprehensive genetic and molecular studies reveal the roles and mechanisms of two hub genes, Bx12 and ZmGLK44, in regulating maize metabolite biosynthesis and drought tolerance.

Conclusion

Our studies reveal the first population-level metabolomes in crop drought response and uncover the natural variations and genetic control of these metabolomes underlying crop drought adaptation, demonstrating that multi-omics is a powerful strategy to dissect the genetic mechanisms of crop complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02481-1.

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Most cited references 81

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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An Introduction to the Bootstrap

Bradley Efron, R.J. Tibshirani (1994)

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

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Author and article information

Contributors

Wei Chen: chenwei0609@mail.hzau.edu.cn

Yariv Brotman: Brotman@mpimp-golm.mpg.de

Mingqiu Dai: mingqiudai@mail.hzau.edu.cn

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 6 September 2021

Publication date PMC-release: 6 September 2021

Publication date Collection: 2021

Volume: 22

Electronic Location Identifier: 260

Affiliations

[1 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, National Key Laboratory of Crop Genetic Improvement, , Huazhong Agricultural University, ; Wuhan, 430070 China

[2 ]Hubei Hongshan laboratory, Wuhan, 430070 China

[3 ]GRID grid.12136.37, ISNI 0000 0004 1937 0546, School of Plant Sciences and Food Security, The Institute for Cereal Crops Improvement, , Tel-Aviv University, ; 69978 Tel Aviv, Israel

[4 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Genetics, , Stanford University School of Medicine, ; Stanford, CA 94305 USA

[5 ]GRID grid.418390.7, ISNI 0000 0004 0491 976X, Max Planck Institute of Molecular Plant Physiology, ; 14476 Potsdam, Germany

[6 ]GRID grid.22935.3f, ISNI 0000 0004 0530 8290, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, , China Agricultural University, ; Beijing, 100193 China

[7 ]GRID grid.7489.2, ISNI 0000 0004 1937 0511, Department of Life Sciences, , Ben-Gurion University of the Negev, ; 8410501 Beersheba, Israel

Article

Publisher ID: 2481

DOI: 10.1186/s13059-021-02481-1

PMC ID: 8420056

PubMed ID: 34488839

SO-VID: 059d8d00-289e-442d-bbd9-31309ef1934e

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 25 February 2021

Date accepted : 25 August 2021

Funding

Funded by: Wuhan Applied Foundational Frontier Project

Award ID: 2020020601012258

Award Recipient : Fei Zhang

Funded by: Beijing Outstanding Young Scientist Program

Award ID: BJJWZYJH01201910019026

Award Recipient : Fei Zhang

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 32061143031

Award Recipient : Fei Zhang

Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;

Award ID: 2662020SKY009

Award Recipient : Fei Zhang

Funded by: 111 Project Crop genomics and Molecular Breeding

Award ID: B20051

Award Recipient : Fei Zhang

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ScienceOpen disciplines: Genetics

Keywords: maize,drought tolerance,metabolome,natural variation,stress responses

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ScienceOpen disciplines: Genetics

Keywords: maize, drought tolerance, metabolome, natural variation, stress responses

Genomic basis underlying the metabolome-mediated drought adaptation of maize

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Genome Integrity

Most cited references 81

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

An Introduction to the Bootstrap

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