PDF(Pubmed)
Abstract：
Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.
摘要：
越来越多的关于杀虫剂抗药性的报告继续阻碍了媒介控制战略在遏制疟疾传播方面的进展。这使得确定新的杀虫剂目标或替代媒介控制策略成为必要。本质分类器AcRossEukaRyote（CLEARER），用于基本基因的留一有机体交叉验证机器学习分类器，用于预测冈比亚按蚊的必需基因，并选择了实验验证的预测基因。CLEARER算法在六种模式生物上进行了训练：秀丽隐杆线虫，黑腹果蝇,智人,小家鼠,酿酒酵母和裂殖酵母，并用于识别An中的必需基因。冈比亚.在An的10426个基因中。冈比亚,1,946个基因（18.7％）被预测为细胞必需基因（CEG），1716个（16.5%）是生物体必需基因（OEGs），852个基因(8.2%)作为OEGs和CEGs都是必需的。RNA干扰（RNAi）用于验证前三个高表达的非核糖体预测作为可能的载体控制目标，通过确定这些基因对An生存的影响。冈比亚G3蚊子.此外,精氨酸酶（AGAP008783）敲低对蚊子感染伯氏疟原虫的影响进行了评估，我们先前根据阻塞点分析计算推断的一种酶是必不可少的。精氨酸酶和前三个基因，AGAP007406(伸长因子1-α，Elf1),AGAP002076（热休克70kDa蛋白1/8，HSP），AGAP009441(伸长系数2,Elf2),击倒效率为91%，75%,63%,61%,分别。与对照组相比，击倒HSP或Elf2显著降低了蚊子的寿命（p<0.0001），Elf1或精氨酸酶敲除对存活没有影响。然而,与注射LacZ的对照相比,精氨酸酶敲除显著降低了蚊子中肠中的伯氏疟原虫卵母细胞计数。研究表明，HSP和Elf2是蚊子存活的重要贡献者，精氨酸酶对寄生虫的发育也很重要。因此将它们作为矢量控制的可能目标。
