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      Identification of Compounds with Potential Therapeutic Uses from Sweet Pepper ( Capsicum annuum L.) Fruits and Their Modulation by Nitric Oxide (NO)

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          Abstract

          Plant species are precursors of a wide variety of secondary metabolites that, besides being useful for themselves, can also be used by humans for their consumption and economic benefit. Pepper ( Capsicum annuum L.) fruit is not only a common food and spice source, it also stands out for containing high amounts of antioxidants (such as vitamins C and A), polyphenols and capsaicinoids. Particular attention has been paid to capsaicin, whose anti-inflammatory, antiproliferative and analgesic activities have been reported in the literature. Due to the potential interest in pepper metabolites for human use, in this project, we carried out an investigation to identify new bioactive compounds of this crop. To achieve this, we applied a metabolomic approach, using an HPLC (high-performance liquid chromatography) separative technique coupled to metabolite identification by high resolution mass spectrometry (HRMS). After chromatographic analysis and data processing against metabolic databases, 12 differential bioactive compounds were identified in sweet pepper fruits, including quercetin and its derivatives, L-tryptophan, phytosphingosin, FAD, gingerglycolipid A, tetrahydropentoxylin, blumenol C glucoside, colnelenic acid and capsoside A. The abundance of these metabolites varied depending on the ripening stage of the fruits, either immature green or ripe red. We also studied the variation of these 12 metabolites upon treatment with exogenous nitric oxide (NO), a free radical gas involved in a good number of physiological processes in higher plants such as germination, growth, flowering, senescence, and fruit ripening, among others. Overall, it was found that the content of the analyzed metabolites depended on the ripening stage and on the presence of NO. The metabolic pattern followed by quercetin and its derivatives, as a consequence of the ripening stage and NO treatment, was also corroborated by transcriptomic analysis of genes involved in the synthesis of these compounds. This opens new research perspectives on the pepper fruit’s bioactive compounds with nutraceutical potentiality, where biotechnological strategies can be applied for optimizing the level of these beneficial compounds.

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

          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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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              voom: precision weights unlock linear model analysis tools for RNA-seq read counts

              New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                25 April 2021
                May 2021
                : 22
                : 9
                : 4476
                Affiliations
                [1 ]Group of Antioxidant, Free Radicals and Nitric Oxide in Biotechnology, Food and Agriculture, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, CSIC, 18008 Granada, Spain; luciaguevaramonge@ 123456gmail.com (L.G.); mardomana2191@ 123456gmail.com (M.Á.D.-A.); alba10081996@ 123456gmail.com (A.O.); salvador.gonzalez@ 123456eez.csic.es (S.G.-G.); javier.corpas@ 123456eez.csic.es (F.J.C.)
                [2 ]Department of Screening & Target Validation, Fundación MEDINA, 18016 Granada, Spain; caridad.diaz@ 123456medinaandalucia.es (C.D.); francisca.vicente@ 123456medinaandalucia.es (F.V.); jose.perezdelpalacio@ 123456medinaandalucia.es (J.P.d.P.)
                Author notes
                [* ]Correspondence: josemanuel.palma@ 123456eez.csic.es ; Tel.: +34-958-181-1600; Fax: +34-958-181-609
                Author information
                https://orcid.org/0000-0002-3678-5503
                https://orcid.org/0000-0003-1240-0143
                https://orcid.org/0000-0003-3198-9205
                https://orcid.org/0000-0002-1814-9212
                https://orcid.org/0000-0001-8918-6873
                https://orcid.org/0000-0001-6673-3571
                Article
                ijms-22-04476
                10.3390/ijms22094476
                8123290
                33922964
                bd58fc79-c52a-4c3e-8eec-6793231404d6
                © 2021 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 ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 25 March 2021
                : 20 April 2021
                Categories
                Article

                Molecular biology
                fruit ripening,gingerglycolipid a,hplc-hrms,melatonin,nitric oxide,phytosphingosin,quercetin,transcriptomics,l-tryptophan

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