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      Transcriptomic Profiling of Tomato Leaves Identifies Novel Transcription Factors Responding to Dehydration Stress

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      International Journal of Molecular Sciences
      MDPI AG

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          Abstract

          Drought is among the most challenging environmental restrictions to tomatoes (Solanum lycopersi-cum), which causes dehydration of the tissues and results in massive loss of yield. Breeding for dehydration-tolerant tomatoes is a pressing issue as a result of global climate change that leads to increased duration and frequency of droughts. However, the key genes involved in dehydration response and tolerance in tomato are not widely known, and genes that can be targeted for dehydration-tolerant tomato breeding remains to be discovered. Here, we compared phenotypes and transcriptomic profiles of tomato leaves between control and dehydration conditions. We show that dehydration decreased the relative water content of tomato leaves after 2 h of dehydration treatment; however, it promoted the malondialdehyde (MDA) content and ion leakage ratio after 4 h and 12 h of dehydration, respectively. Moreover, dehydration stress triggered oxidative stress as we detected significant increases in H2O2 and O2− levels. Simultaneously, dehydration enhanced the activities of antioxidant enzymes including peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and phenylalanine ammonia-lyase (PAL). Genome-wide RNA sequencing of tomato leaves treated with or without dehydration (control) identified 8116 and 5670 differentially expressed genes (DEGs) after 2 h and 4 h of dehydration, respectively. These DEGs included genes involved in translation, photosynthesis, stress response, and cytoplasmic translation. We then focused specifically on DEGs annotated as transcription factors (TFs). RNA-seq analysis identified 742 TFs as DEGs by comparing samples dehydrated for 2 h with 0 h control, while among all the DEGs detected after 4 h of dehydration, only 499 of them were TFs. Furthermore, we performed real-time quantitative PCR analyses and validated expression patterns of 31 differentially expressed TFs of NAC, AP2/ERF, MYB, bHLH, bZIP, WRKY, and HB families. In addition, the transcriptomic data revealed that expression levels of six drought-responsive marker genes were upregulated by de-hydration treatment. Collectively, our findings not only provide a solid foundation for further functional characterization of dehydration-responsive TFs in tomatoes but may also benefit the improvement of dehydration/drought tolerance in tomatoes in the future.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              Differential expression analysis for sequence count data

              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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                Author and article information

                Contributors
                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                June 2023
                June 03 2023
                : 24
                : 11
                : 9725
                Article
                10.3390/ijms24119725
                b67329ac-66e1-43da-a521-1fc84384f49a
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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