5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Global transcriptomic analysis reveals candidate genes associated with different phosphorus acquisition strategies among soybean varieties

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction

          Soybean adapts to phosphorus-deficient soils through three important phosphorus acquisition strategies, namely altered root conformation, exudation of carboxylic acids, and symbiosis with clumping mycorrhizal fungi. However, the trade-offs and regulatory mechanisms of these three phosphorus acquisition strategies in soybean have not been researched.

          Methods

          In this study, we investigated the responses of ten different soybean varieties to low soil phosphorus availability by determining biomass, phosphorus accumulation, root morphology, exudation, and mycorrhizal colonization rate. Furthermore, the molecular regulatory mechanisms underlying root phosphorus acquisition strategies were examined among varieties with different low-phosphorus tolerance using transcriptome sequencing and weighted gene co-expression network analysis.

          Results and discussion

          The results showed that two types of phosphorus acquisition strategies—“outsourcing” and “do-it-yourself”—were employed by soybean varieties under low phosphorus availability. The “do-it-yourself” varieties, represented by QD11, Zh30, and Sd, obtained sufficient phosphorus by increasing their root surface area and secreting carboxylic acids. In contrast, the “outsourcing” varieties, represented by Zh301, Zh13, and Hc6, used increased symbiosis with mycorrhizae to obtain phosphorus owing to their large root diameters. Transcriptome analysis showed that the direction of acetyl-CoA metabolism could be the dividing line between the two strategies of soybean selection. ERF1 and WRKY1 may be involved in the regulation of phosphorus acquisition strategies for soybeans grown under low P environments. These findings will enhance our understanding of phosphorus acquisition strategies in soybeans. In addition, they will facilitate the development of breeding strategies that are more flexible to accommodate a variety of production scenarios in agriculture under low phosphorus environments.

          Background

          Association of gastric atrophy or cancer with levels of serum pepsinogens, gastrin-17 and anti- Helicobacter pylori IgG antibody have been extensively studied. However, the association of serum pepsinogen and gastrin-17 with H. pylori infection has not been studied in a large population.

          Related collections

          Most cited references79

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          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
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                19 December 2022
                2022
                : 13
                : 1080014
                Affiliations
                [1] 1 College of Agriculture, Guizhou University , Guiyang, China
                [2] 2 Agricultural Ecological Environment and Resources Protection Station of Bijie Agricultural and Rural Bureau , Guiyang, China
                [3] 3 Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry , Beijing, China
                [4] 4 Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH , Jülich, Germany
                [5] 5 School of Natural Sciences, Environment Centre Wales, Bangor University , Bangor, United Kingdom
                [6] 6 Institute for Forest Resources & Environment of Guizhou, College of Forestry, Guizhou University , Guiyang, China
                Author notes

                Edited by: Anoop Kumar Srivastava, Central Citrus Research Institute (ICAR), India

                Reviewed by: Pandiyan Muthuramalingam, Gyeongsang National University, South Korea; Suresh Kumar Malhotra, Directorate of Knowledge Management in Agriculture (ICAR), India

                This article was submitted to Plant Nutrition, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.1080014
                9806128
                36600925
                0a850df0-691f-4698-9cbe-ef420640dd51
                Copyright © 2022 Yang, Yang, Chen, Tan, Bol, Duan and He

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 October 2022
                : 28 November 2022
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 81, Pages: 14, Words: 6167
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 31860115, 32260804
                Funded by: High-end Foreign Experts Recruitment Plan of China , doi 10.13039/501100018608;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                soybean,root phosphorus acquisition strategy,root diameter,acetyl-coa,transcriptome

                Comments

                Comment on this article