Ace and ace-like genes of invasive redlegged earth mite: copy number variation,target-site mutations,and their associations with organophosphate insensitivity

Background

Invasive Australian populations of redlegged earth mite, Halotydeus destructor (Tucker), are evolving increasing organophosphate resistance. In addition to the canonical ace gene, the target gene of organophosphates, the H. destructor genome contains many radiated ace-like genes that vary in copy number and amino acid sequence. In this work, we characterise copy number and target-site mutation variation at the canonical ace and ace-like genes and test for potential associations with organophosphate insensitivity. This was achieved through comparisons of whole-genome pool-seq data from alive and dead mites following organophosphate exposure.

Results

A combination of increased copy number and target-site mutations at the canonical ace was associated with organophosphate insensitivity in H. destructor. Resistant populations were segregating for G119S, A201S, F331Y at the canonical ace. A subset of populations also had copy numbers of canonical ace > 2, which potentially helps overexpress proteins carrying these target-site mutations. Haplotypes possessing different copy numbers and target-site mutations of the canonical ace gene may be under selection across H. destructor populations. We also detected some evidence that increases in copy number of radiated ace-like genes are associated with organophosphate insensitivity, which might suggest potential roles in sequestration or breakdown of organophosphates.

Conclusion

Different combinations of target-site mutations and (or) copy number variation in the canonical ace and ace-like genes may provide non-convergent ways for H. destructor to respond to organophosphate selection. However, these changes may only play a partial role in organophosphate insensitivity, which appears to have a polygenic architecture. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Genomic Prediction of Arsenic Tolerance and Grain Yield in Rice: Contribution of Trait-Specific Markers and Multi-Environment Models

Many rice-growing areas are affected by high concentrations of arsenic(As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability(PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic(1600 K SNPs) and phenotypic(grain As content, grain yield and days to flowering) data of the Bengal and Assam Aus Panel. Under the base-line single-environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content, respectively; the three prediction methods(Bayesian Lasso, genomic best linear unbiased prediction and reproducing kernel Hilbert spaces) were considered to perform similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17 K, without negative effect on PA of genomic predictions. Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for genotype × environment interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest predictive ability(0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel. Genomic prediction can also be applied to breeding for other complex traits under multiple environments.     

Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction

Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single-cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid-parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate-to-low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid-parent values as emphasized by our simulation study, but can be expected to shift to GCA-based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.     

猕猴桃溃疡病菌biovar 3群体MLVA分型技术的建立与应用

 丁香假单胞菌猕猴桃致病变种生物型3(Pseudomonas syringae pv. actinidiae biovar 3,Psa3)是猕猴桃溃疡病菌的世界流行群体,但仅在中国存在复杂的遗传多样性。开发适于Psa3群体分型的MLVA(multilocus variable-number tandem-repeat analysis)技术是探索中国Psa3起源与流行学特性的基础。本研究对7个Psa3菌株进行了全基因组测序,结合已公布的86个全基因组数据,进行比较分析发现,中国Psa3至少存在7个亚群;在各亚群间存在多态性的24个串联重复序列中,其中10个可以通过琼脂糖凝胶电泳区分开且变异指数合适,据此建立了适于Psa3的MLVA技术。采用该技术对分别来自贵州和陕西的62和9个Psa3菌株进行群体分型,分型结果与全基因组分析高度一致,证明该MLVA体系分型准确。MLVA分型结果表明:贵州主产区修文县Psa3有3个MLVA群体,其中亚群4的组内分化明显,代表最早发生的群体;而亚群 1和3的结构单一,且多在新果园发现,是新传入群体。总之,本研究建立了一套可用于Psa3群体分型的MLVA技术,将有助于解析中国各猕猴桃产区Psa3群体结构,以及探索中国Psa3的起源、传播和流行学特征。     

全基因组选择育种策略及在水产动物育种中的应用前景

全基因组选择的概念自2001年由Meuwissen等提出后便引起了动物育种工作者的广泛关注.目前,澳大利亚、新西兰、荷兰、美国的研究小组已经应用该方法进行了优质种牛的选择育种,并取得了很好的效果.此外在鸡和猪的选择育种中也有该方法的应用,但在水产动物选育中尚未见该方法使用的报道.本文对“全基因组选择育种”的概念和提出背景进行了归纳,对全基因组选择育种的优势进行了阐述,并详细介绍了其具体的策略,总结了目前全基因组育种所广泛采用的方法以及取得的成果,旨在为该方法在水产动物育种方面的应用研究提供科学参考.     

水稻稻瘟病菌胁迫应答cDNA片段的表达及定位

以抗稻瘟病品系G205为材料,应用cDNA微阵列分别获得了一个受稻瘟病菌诱导的含NBS LRR的cDNA克隆（暂命名为RIM1, rice induced by Magnaporthe grisea）和一个受稻瘟病菌抑制的编码腈水解酶（Nitrilase）的cDNA克隆（暂命名为NIT）,并通过Northern得到证实。RFLP分析将RIM1和NIT分别定位于水稻第2和第3染色体上,它们均位于控制水稻稻瘟病部分抗性QTL区间。     

基于阵列芯片现场高灵敏同步快速检测粮油中氨基甲酸酯类农药残留混合污染

The quality and safety of agricultural products is the technique focus of the strategy of rural vitaliza⁃tion. Carbamate pesticide residues are seriously threatening the safety of agricultural product and human life and health. Nowadays, the common detection methods are instrument-based for carbamate pesticide residue, which dis⁃ satisfy the request of an on-site rapid, sensitive and simultaneous detection for multiple carbamate pesticide resi⁃ dues. Based on the home-made monoclonal antibodies against carbofuran and carbaryl respectivity, a microarray for rapid and simultaneous detection of multiple carbamate pesticide residues was developed within 30 min. Under the optimized conditions, the limits of detection（LODs）were found to be 0.11 and 0.09 ng/g, while the linear ranges were 0.33-3000 and 0.18-1500 ng/g for carbofuran and carbaryl respectively. By using spiked peanut samples, it recorded recoveries of 88.4%-98.3%, 90.1%-118.8% for carbofuran and carbaryl respectively. The RSD of intraarray were found at 9.0%, 3.5%, while the RSD of inter-array were recorded at 12.3%, 16.3% respectively. The de⁃ tection results of the microarray were highly consistent with that of HPLC-MS/MS. This proposed microarray provid⁃ ed an on-site high-sensitivity, simultaneous and rapid detection of carbofuran and carbaryl, and could be widely used in the fields of agricultural product quality safety, food safety and environmental monitoring.