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      Key Maize Drought-Responsive Genes and Pathways Revealed by Comparative Transcriptome and Physiological Analyses of Contrasting Inbred Lines

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          To unravel the molecular mechanisms underpinning maize ( Zea mays L.) drought stress tolerance, we conducted comprehensive comparative transcriptome and physiological analyses of drought-tolerant YE8112 and drought-sensitive MO17 inbred line seedlings that had been exposed to drought treatment for seven days. Resultantly, YE8112 seedlings maintained comparatively higher leaf relative water and proline contents, greatly increased peroxidase activity, but decreased malondialdehyde content, than MO17 seedlings. Using an RNA sequencing (RNA-seq)-based approach, we identified a total of 10,612 differentially expressed genes (DEGs). From these, we mined out four critical sets of drought responsive DEGs, including 80 specific to YE8112, 5140 shared between the two lines after drought treatment (SD_TD), five DEGs of YE8112 also regulated in SD_TD, and four overlapping DEGs between the two lines. Drought-stressed YE8112 DEGs were primarily associated with nitrogen metabolism and amino-acid biosynthesis pathways, whereas MO17 DEGs were enriched in the ribosome pathway. Additionally, our physiological analyses results were consistent with the predicted RNA-seq-based findings. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis and the RNA-seq results of twenty representative DEGs were highly correlated ( R 2 = 98.86%). Crucially, tolerant line YE8112 drought-responsive genes were predominantly implicated in stress signal transduction; cellular redox homeostasis maintenance; MYB, NAC, WRKY, and PLATZ transcriptional factor modulated; carbohydrate synthesis and cell-wall remodeling; amino acid biosynthesis; and protein ubiquitination processes. Our findings offer insights into the molecular networks mediating maize drought stress tolerance.

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          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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              We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.

                Author and article information

                Int J Mol Sci
                Int J Mol Sci
                International Journal of Molecular Sciences
                13 March 2019
                March 2019
                : 20
                : 6
                [1 ]Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China; tzenda@ (T.Z.); m15028293845@ (S.L.); 15733289921@ (X.W.); m15612245597@ (G.L.); m15633790536@ (H.J.); 18331220513@ (A.D.); 18233230155@ (Y.Y.)
                [2 ]North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
                Author notes
                [* ]Correspondence: hjduan@ ; Tel.: +86-139-3127-9716

                These authors contributed equally to this work.

                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (



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