Nyirakanani Chantal, Tamisier Lucie, Bizimana Jean Pierre, Rollin Johan, Nduwumuremyi Athanase, Bigirimana Vincent de Paul, Selmi Ilhem, Lasois Ludivine, Vanderschuren Hervé, Massart Sébastien
Plant Genetics and Rhizosphere Processes Laboratory, TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech, Gembloux 5030, Belgium.
Department of Crop Sciences, School of Agriculture and Food Sciences, College of Agriculture, Animal Sciences and Veterinary Medicine, University of Rwanda, Musanze 210, Rwanda.
Virus Evol. 2023 Aug 24;9(2):vead053. doi: 10.1093/ve/vead053. eCollection 2023.
Cassava Brown Streak Disease (CBSD), which is caused by cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV), represents one of the most devastating threats to cassava production in Africa, including in Rwanda where a dramatic epidemic in 2014 dropped cassava yield from 3.3 million to 900,000 tonnes (1). Studying viral genetic diversity at the genome level is essential in disease management, as it can provide valuable information on the origin and dynamics of epidemic events. To fill the current lack of genome-based diversity studies of UCBSV, we performed a nationwide survey of cassava ipomovirus genomic sequences in Rwanda by high-throughput sequencing (HTS) of pools of plants sampled from 130 cassava fields in thirteen cassava-producing districts, spanning seven agro-ecological zones with contrasting climatic conditions and different cassava cultivars. HTS allowed the assembly of a nearly complete consensus genome of UCBSV in twelve districts. The phylogenetic analysis revealed high homology between UCBSV genome sequences, with a maximum of 0.8 per cent divergence between genomes at the nucleotide level. An in-depth investigation based on Single Nucleotide Polymorphisms (SNPs) was conducted to explore the genome diversity beyond the consensus sequences. First, to ensure the validity of the result, a panel of SNPs was confirmed by independent reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing. Furthermore, the combination of fixation index () calculation and Principal Component Analysis (PCA) based on SNP patterns identified three different UCBSV haplotypes geographically clustered. The haplotype 2 (H) was restricted to the central regions, where the NAROCAS 1 cultivar is predominantly farmed. RT-PCR and Sanger sequencing of individual NAROCAS1 plants confirmed their association with H. Haplotype 1 was widely spread, with a 100 per cent occurrence in the Eastern region, while Haplotype 3 was only found in the Western region. These haplotypes' associations with specific cultivars or regions would need further confirmation. Our results prove that a much more complex picture of genetic diversity can be deciphered beyond the consensus sequences, with practical implications on virus epidemiology, evolution, and disease management. Our methodology proposes a high-resolution analysis of genome diversity beyond the consensus between and within samples. It can be used at various scales, from individual plants to pooled samples of virus-infected plants. Our findings also showed how subtle genetic differences could be informative on the potential impact of agricultural practices, as the presence and frequency of a virus haplotype could be correlated with the dissemination and adoption of improved cultivars.
木薯褐色条纹病(CBSD)由木薯褐色条纹病毒(CBSV)和乌干达木薯褐色条纹病毒(UCBSV)引起,是非洲木薯生产面临的最具毁灭性的威胁之一,在卢旺达也是如此,2014年的一场严重疫情使木薯产量从330万吨降至90万吨(1)。在基因组水平上研究病毒遗传多样性对于疾病管理至关重要,因为它可以提供有关疫情事件起源和动态的有价值信息。为了填补目前关于UCBSV缺乏基于基因组的多样性研究的空白,我们通过对来自13个木薯生产区130个木薯田采集的植物样本池进行高通量测序(HTS),在卢旺达开展了一项全国性的木薯甘薯潜隐病毒基因组序列调查,这些生产区跨越七个农业生态区,气候条件各异,种植的木薯品种也不同。高通量测序使得在12个区组装出了近乎完整的UCBSV共有基因组。系统发育分析表明UCBSV基因组序列之间具有高度同源性,基因组在核苷酸水平上的最大差异为0.8%。基于单核苷酸多态性(SNP)进行了深入研究,以探索共有序列之外的基因组多样性。首先,为确保结果的有效性,通过独立的逆转录聚合酶链反应(RT-PCR)和桑格测序对一组SNP进行了验证。此外,基于SNP模式计算固定指数()并结合主成分分析(PCA),确定了在地理上聚类的三种不同的UCBSV单倍型。单倍型2(H)局限于中部地区,该地区主要种植NAROCAS 1品种。对单个NAROCAS1植株进行RT-PCR和桑格测序,证实它们与单倍型H相关。单倍型1广泛分布,在东部地区的出现率为100%,而单倍型3仅在西部地区发现。这些单倍型与特定品种或地区的关联还需要进一步确认。我们的结果证明,除了共有序列之外,还可以解读出更为复杂的遗传多样性情况,这对病毒流行病学、进化和疾病管理具有实际意义。我们的方法提出了一种超越样本间和样本内共有序列的基因组多样性高分辨率分析方法。它可用于从单株植物到病毒感染植物混合样本的各种规模研究。我们的研究结果还表明,细微的遗传差异如何能够为农业实践的潜在影响提供信息,因为病毒单倍型的存在和频率可能与改良品种的传播和采用相关。