PGPg_finder:用于从基因组和元基因组数据中识别植物生长促进基因的全面且用户友好的管道

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Thierry Alexandre Pellegrinetti , Gabriel Gustavo Tavares Nunes Monteiro , Leandro Nascimento Lemos , Renato Augusto Corrêa dos Santos , Artur Gomes Barros , Lucas William Mendes
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引用次数: 0

摘要

在全基因组和元基因组测序数据中识别和比较植物生长促进性状(PGPT)能极大地推动农业研究,促进作物的可持续生产。本研究介绍了 PGPg_finder,这是一个综合管道,旨在注释和比较全基因组和元基因组测序数据集中的 PGPT。该管道利用直接序列注释和从头组装方法来准确检测 PGPT。通过交叉引用 PLaBAse 数据库中的序列,它能识别并量化原始数据集中这些基因的存在,从而有助于直观地比较 PGPT 在不同样本中的丰度和分布。我们通过分析五种根瘤菌株的基因组评估了 PGPg_finder 的性能:我们评估了 PGPg_finder 的性能,我们分析了五株根瘤菌的基因组:Paenibacillus vini、Paenibacillus polymyxa、Fictibacillus sp.、Brevibacillus agri 和 Bacillus cereus,还分析了亚马逊雨林中从森林到牧场转换过程中大块土壤的元基因组样本。基因组工作流程揭示了与底物利用、非生物压力中和、磷酸盐溶解和铁获取相关的若干基因。它还确定了特定菌系的独特基因,包括与多粘菌属(P. polymyxa)的定殖和植物源底物利用、维尼菌属(P. vini)的法定量感应反应和生物膜形成、农杆菌属(B. agri)的重金属解毒和氮获取以及蜡样芽孢杆菌属(B. cereus)的孢子生产和生物压力中和有关的基因。Fictibacillus sp.菌株在表面附着、应激反应、异生物降解、磷酸盐溶解和植物激素产生方面有几个独特的基因。PGPg_finder 的使用凸显了其发现新型接种剂和菌株的潜力。元基因组工作流程区分了亚马逊雨林土壤和牧场土壤中促进植物生长的基因图谱,后者的图谱更符合单碳水化合物消耗、非生物胁迫耐受性、运动性和趋化性以及磷矿化。原生林显示出与复杂有机物降解、氧化应激耐受性、异生物降解、杀菌活性、铁获取和挥发性途径相关的特征。这些发现强调了 PGPg_finder 在准确识别和比较 PGPT 基因方面的有效性和灵敏度,突出了不同样本之间的共性和差异。该管道的应用有可能极大地促进植物生长促进微生物的鉴定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PGPg_finder: A comprehensive and user-friendly pipeline for identifying plant growth-promoting genes in genomic and metagenomic data

Identifying and comparing plant growth-promoting traits (PGPT) within whole-genome and metagenomic sequencing data can significantly advance agricultural research and promote sustainable crop production. This study introduces PGPg_finder, a comprehensive pipeline designed to annotate and compare PGPT from both whole-genome and metagenome sequencing datasets. This pipeline utilizes direct sequence annotation alongside de novo assembly methods to accurately detect PGPT. By cross-referencing sequences from the PLaBAse database, it identifies and quantifies the presence of these genes within the original datasets, facilitating an intuitive comparison of the abundance and distribution of PGPT across various samples. We evaluated the performance of PGPg_finder by analyzing genomes from five rhizobacterial strains: Paenibacillus vini, Paenibacillus polymyxa, Fictibacillus sp., Brevibacillus agri, and Bacillus cereus, and also metagenomic samples from bulk soils subjected to forest-to-pasture conversion in the Amazon rainforest. The genomic workflow revealed several genes associated with substrate utilization, abiotic stress neutralization, phosphate solubilization, and iron acquisition. It also identified genes unique to specific lineages, including those associated with colonization and plant-derived substrate usage in P. polymyxa, quorum sensing response and biofilm formation in P. vini, heavy metal detoxification and nitrogen acquisition in B. agri, and spore production and neutralizing biotic stress in B. cereus. The strain Fictibacillus sp. presented several unique genes related to surface attachment, stress response, xenobiotic degradation, phosphate solubilization, and phytohormone production. The use of PGPg_finder highlights its potential to uncover novel inoculants and strains. The metagenomic workflow distinguished plant-growth promotion gene profiles between soils from the Amazon rainforest and pasture, with the latter showing a profile more aligned with simple carbohydrate consumption, abiotic stress tolerance, motility and chemotaxis, and phosphorus mineralization. Native forests exhibited a profile associated with the degradation of complex organic matter, oxidative stress tolerance, xenobiotic degradation, bactericidal activity, iron acquisition, and volatile pathways. These findings underscore the effectiveness and sensitivity of PGPg_finder in accurately identifying and comparing PGPT genes, highlighting both commonalities and variations across samples. The application of this pipeline has the potential to significantly facilitate the identification of plant growth-promoting microbes.

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CiteScore
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