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      Investigating the resistance responses to Alternaria brassicicola in 'Korla' fragrant pear fruit induced by a biocontrol strain Bacillus subtilis Y2

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      Postharvest Biology and Technology
      Elsevier BV

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          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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            Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

            Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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              Systemic acquired resistance: turning local infection into global defense.

              Systemic acquired resistance (SAR) is an induced immune mechanism in plants. Unlike vertebrate adaptive immunity, SAR is broad spectrum, with no specificity to the initial infection. An avirulent pathogen causing local programmed cell death can induce SAR through generation of mobile signals, accumulation of the defense hormone salicylic acid, and secretion of the antimicrobial PR (pathogenesis-related) proteins. Consequently, the rest of the plant is protected from secondary infection for a period of weeks to months. SAR can even be passed on to progeny through epigenetic regulation. The Arabidopsis NPR1 (nonexpresser of PR genes 1) protein is a master regulator of SAR. Recent study has shown that salicylic acid directly binds to the NPR1 adaptor proteins NPR3 and NPR4, regulates their interactions with NPR1, and controls NPR1 protein stability. However, how NPR1 interacts with TGA transcription factors to activate defense gene expression is still not well understood. In addition, redox regulators, the mediator complex, WRKY transcription factors, endoplasmic reticulum-resident proteins, and DNA repair proteins play critical roles in SAR.
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                Author and article information

                Contributors
                Journal
                Postharvest Biology and Technology
                Postharvest Biology and Technology
                Elsevier BV
                09255214
                May 2023
                May 2023
                : 199
                : 112293
                Article
                10.1016/j.postharvbio.2023.112293
                5d0de08b-c7c1-4e9e-963d-1ca1b9762bca
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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