State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), Nanjing Agricultural University, Nanjing 210095, China; Department of Biology, East Carolina University, Greenville, NC 27858, USA.
Department of Biology, East Carolina University, Greenville, NC 27858, USA; Key Laboratory of Biology and Genetic Improvement of Oil Crops of the Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, Hubei 430062, China.
Genomics. 2021 May;113(3):1146-1156. doi: 10.1016/j.ygeno.2021.02.018. Epub 2021 Mar 3.
Investigation of cotton response to nematode infection will allow us to better understand the cotton immune defense mechanism and design a better biotechnological approach for efficiently managing pest nematodes in cotton. In this study, we firstly treated cotton by root knot nematode (RKN, Meloidogyne incognita) infections, then we employed the high throughput deep sequencing technology to sequence and genome-widely identify all miRNAs in cotton; finally, we analyzed the functions of these miRNAs in cotton response to RKN infections. A total of 266 miRNAs, including 193 known and 73 novel miRNAs, were identified by deep sequencing technology, which belong to 67 conserved and 66 novel miRNA families, respectively. A majority of identified miRNA families only contain one miRNA; however, miR482 family contains 14 members and some others contain 2-13 members. Certain miRNAs were specifically expressed in RKN-infected cotton roots and others were completely inhibited by RKN infection. A total of 50 miRNAs were differentially expressed after RKN infection, in which 28 miRNAs were up-regulated and 22 were inhibited by RKN treatment. Based on degradome sequencing, 87 gene targets were identified to be targeted by 57 miRNAs. These miRNA-targeted genes are involved in the interaction of cotton plants and nematode infection. Based on GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, 466 genes from all 636 miRNA targets were mapped to 6340 GO terms, 181 genes from 228 targets of differentially expressed miRNAs were mapped to 1588 GO terms. The GO terms were then categorized into the three main GO classes: biological processes, cellular components, and molecular functions. The targets of differentially expressed miRNAs were enriched in 43 GO terms, including 22 biological processes, 10 cellular components, and 11 molecular functions (p < 0.05). Many identified processes were associated with organism responses to the environmental stresses, including regulation of nematode larval development, response to nematode, and response to flooding. Our results will enhance the study and application of developing new cotton cultivars for nematode resistance.
对棉株响应线虫感染的研究将有助于我们更好地理解棉花的免疫防御机制，并设计出更好的生物技术方法来有效防治棉花上的害虫线虫。在本研究中，我们首先用根结线虫（RKN，Meloidogyne incognita）感染棉花，然后利用高通量深度测序技术对棉花中的所有 miRNA 进行测序和全基因组鉴定；最后，我们分析了这些 miRNA 在棉花响应 RKN 感染中的功能。通过深度测序技术共鉴定出 266 个 miRNA，包括 193 个已知 miRNA 和 73 个新 miRNA，分别属于 67 个保守 miRNA 家族和 66 个新 miRNA 家族。鉴定的大多数 miRNA 家族只含有一个 miRNA；然而，miR482 家族包含 14 个成员，其他家族包含 2-13 个成员。某些 miRNA 仅在 RKN 感染的棉花根中特异性表达，而其他 miRNA 则完全被 RKN 感染抑制。RKN 感染后共有 50 个 miRNA 表达差异，其中 28 个上调，22 个下调。基于降解组测序，共鉴定到 57 个 miRNA 靶向的 87 个基因靶标。这些 miRNA 靶向的基因参与了棉花植物与线虫感染的相互作用。根据 GO（基因本体论）和 KEGG（京都基因与基因组百科全书）分析，从所有 636 个 miRNA 靶标中得到的 466 个基因映射到 6340 个 GO 术语，从 22 个差异表达 miRNA 的靶标中得到的 181 个基因映射到 1588 个 GO 术语。GO 术语进一步分为三个主要的 GO 类别：生物过程、细胞成分和分子功能。差异表达 miRNA 的靶标富集在 43 个 GO 术语中，包括 22 个生物过程、10 个细胞成分和 11 个分子功能（p<0.05）。许多鉴定的过程与生物体对环境胁迫的响应有关，包括对线虫幼虫发育的调控、对线虫的响应和对洪水的响应。我们的研究结果将增强对线虫抗性新棉花品种的研究和应用。
