Molecular cytogenetic characterization of novel wheat-rye T1RS.1AL translocation lines with resistance to powdery mildew and stripe rust derived from the Chinese rye landrace Qinling.

Stripe rust and powdery mildew are serious diseases that severely decrease the yield of wheat. Planting wheat cultivars with powdery mildew and stripe rust resistance genes is the most effective way to control these two diseases. Introducing disease-resistance genes from related species into the wheat genome via chromosome translocation is an important way to improve wheat disease resistance. In this study, nine novel T1RS.1AL translocation lines were developed from the cross of wheat cultivar Chuannong25 (CN25) and a Chinese rye Qinling. The results of non-denaturing fluorescence in situ hybridization (ND-FISH) and PCR showed that all new lines were homozygous for the T1RS.1AL translocation. These new T1RS.1AL translocation lines exhibited strong resistance to stripe rust and powdery mildew. The cytogenetics results indicated that the resistance of the new lines was conferred by the 1RS chromosome arms, which came from Qinling rye. The genetic analysis indicated that there were new dominant resistance genes on the 1RS chromosome arm resistant to stripe rust and powdery mildew, and their resistance patterns were different from Yr9, Pm8, and Pm17 genes. In addition, the T1RS.1AL translocation lines generally exhibited better agronomic traits in the field relative to CN25. These T1RS.1AL translocations have great potential in wheat-breeding programs in the future.

Identification of a stable major-effect quantitative trait locus for pre-harvest sprouting in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping.

KEY MESSAGE: A major and stable QTL cQSGR.sau.3D, which can explain 33.25% of the phenotypic variation in SGR, was mapped and validated, and cQSGR.sau.3D was found to be independent of GI. In this study, a recombinant inbred line (RIL) population containing 304 lines derived from the cross of Chuan-nong17 (CN17) and Chuan-nong11 (CN11) was genotyped using the Wheat55K single-nucleotide polymorphism array. A high-density genetic map consisting of 8329 markers spanning 4131.54 cM and distributed across 21 wheat chromosomes was constructed. QTLs for whole spike germination rate (SGR) were identified in multiple years. Six and fourteen QTLs were identified using the Inclusive Composite Interval Mapping-Biparental Populations and Multi-Environment Trial methods, respectively. A total of 106 digenic epistatic QTLs were also detected in this study. One of the additive QTLs, cQSGR.sau.3D, which was mapped in the region from 3.5 to 4.5 cM from linkage group 3D-2 on chromosome 3D, can explain 33.25% of the phenotypic variation in SGR and be considered a major and stable QTL for SGR. This QTL was independent of the seeds' germination traits, such as germination index. One Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110772653, which is tightly linked to cQSGR.sau.3D, was developed. The genetic effect of cQSGR.sau.3D on SGR in the RIL and natural populations was successfully confirmed. Furthermore, within the interval in which cQSGR.sau.3D is located in Chinese Spring reference genomes, thirty-seven genes were found. cQSGR.sau.3D may provide new resources for pre-harvest sprouting resistance breeding of wheat in the future.

Molecular Cytogenetic and Physiological Characterization of a Novel Wheat-Rye T1RS.1BL Translocation Line from Secale cereal L. Weining with Resistance to Stripe Rust and Functional "Stay Green" Trait.

In this study, a novel T1RS.1BL translocation line RT843-5 was selected from a cross between wheat Mianyang11 (MY11) and Weining rye. The results of MC-FISH, PCR, and A-PAGE showed that RT843-5 contained two intact T1RS.1BL translocation chromosomes. RT843-5 showed resistance to the most virulent and frequently occurring stripe rust races/isolates. Additionally, RT843-5 showed resistance in the field in locations where stripe rust outbreaks have been the most severe in China. Genetic analysis indicated one new gene for stripe rust resistance, located on 1RS of RT843-5, which was tentatively named YrRt843. Furthermore, the chlorophyll content, the activities of catalase (CAT), and superoxide dismutase (SOD), and the net photosynthetic rate (Pn) of RT843-5 were significantly higher than those in its wheat parent MY11, whereas malondialdehyde (MDA) accumulation was significantly lower after anthesis in RT843-5 compared to in MY11. RT843-5 had a significantly higher 1000-kernel weight and yield than MY11. The results indicated that RT843-5 exhibited functional stay-green traits after anthesis, that delayed the senescence process in wheat leaves during the filling stage and had positive effects on grain yield. The present study indicated that Weining rye may carry untapped variations as a potential source of resistance, and that RT843-5 could be an important material for wheat breeding programs in the future.

Development and Molecular Cytogenetic Characterization of Novel Primary Wheat-Rye 1RS.1BL Translocation Lines from Multiple Rye Sources with Resistance to Stripe Rust.

Stripe rust (caused by Puccinia striiformis f. sp. tritici) is one of the most severe diseases for wheat production. An important method to improve the stripe rust resistance of wheat is to introduce resistance genes from related species into the wheat genome. The 1RS.1BL wheat-rye translocation from Petkus rye has contributed substantially to wheat resistance breeding worldwide. However, given the breakdown of the stripe rust resistance gene Yr9 in 1RS, its importance for wheat improvement has decreased. In this study, we developed 166 new primary 1RS.1BL translocation lines by crossing rye varieties Weining, Baili, and Aigan with several wheat cultivars. Cytogenetic and molecular analyses indicated that all of these lines contained a pair of intact 1RS.1BL translocation chromosomes. The stripe rust resistance of these translocation lines and their wheat parents was evaluated in southwestern China during the severe stripe rust epidemics in 2015 and 2021. The results showed diverse effects of the 1RS.1BL translocations from different rye cultivars on resistance to stripe rust. The highest genetic diversity was observed in 1RS.1BL translocations derived from diverse rye varieties but in the same wheat background. The development of diverse 1RS.1BL translocation lines offers ample opportunities to introduce new variations into wheat for improving stripe rust resistance. Finally, 71 new translocation lines, including nine developed from the cross of MY11 × Aigan, four from MY11 × Baili, 40 from MY11 × Weining, 14 from A42912 × Baili, and four from A42912 × Weining. These lines showed consistent resistance to stripe rust in fields under frequent changes of the pathogen races and could be useful genetic stocks for breeding wheat cultivars with resistance to stripe rust.

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.