Plant architecture and leaf color are important factors influencing cotton fiber yield. In this study, based on genetic analysis, stem paraffin sectioning, and phytohormone treatments, we showed that the dwarf-red (DR) cotton mutant is a gibberellin-sensitive mutant caused by a mutation in a single dominant locus, designated GhDR. Using bulked segregant analysis (BSA) and genotyping by target sequencing (GBTS) approaches, we located the causative mutation to a ~197-kb genetic interval on chromosome A09 containing 25 annotated genes. Based on gene annotation and expression changes between the mutant and normal plants, GH_A09G2280 was considered to be the best candidate gene responsible for the dwarf and red mutant phenotypes. A 2-nucleotide deletion was found in the coding region of GhDR/GH_A09G2280 in the DR mutant, which caused a frameshift and truncation of GhDR. GhDR is a homolog of Arabidopsis AtBBX24, and encodes a B-box zinc finger protein. The frameshift deletion eliminated the C-terminal nuclear localization domain and the VP domain of GhDR, and altered its subcellular localization. A comparative transcriptome analysis demonstrated downregulation of the key genes involved in gibberellin biosynthesis and the signaling transduction network, as well as upregulation of the genes related to gibberellin degradation and the anthocyanin biosynthetic pathway in the DR mutant. The results of this study revealed the potential molecular basis by which plant architecture and anthocyanin accumulation are regulated in cotton.  

SrUGT76G1, the most well-studied diterpene glycosyltransferase in Stevia rebaudiana, is key to the biosynthesis of economically important steviol glycosides (SGs).  However, the molecular regulatory mechanism of SrUGT76G1 has rarely been explored.  In this study, we identified a MYB transcription factor, SrMYB1, using a yeast one-hybrid screening assay.  SrMYB1 belongs to the typical R2R3-type MYB protein and is specifically localized in the nucleus with strong transactivation activity.  The transcript of SrMYB1 is predominantly accumulated in flowers, but is also present at a lower level in leaves.  Yeast one-hybrid and electrophoretic mobility shift assays verified that SrMYB1 binds directly to the MYB binding sites in the F4-3 fragment (+50–(–141)) of the SrUGT76G1 promoter.  Furthermore, we found that SrMYB1 could significantly repress the expression of SrUGT76G1 in both epidermal cells of tobacco leaves and stevia callus.  Taken together, our results demonstrate that SrMYB1 is an essential upstream regulator of SrUGT76G1 and provide novel insight into the regulatory network for the SGs metabolic pathway in S. rebaudiana.

Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses.  However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on.  Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements.  We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation.  This involves designing an easy-to-use cloud-based strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases.  With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes.  Using accuracy, precision, recall and F₁ to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively.  Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect.  Currently, the client (mini program) has been released on the WeChat platform.  The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.

Soybean mosaic virus (SMV) is one of the major viral pathogens affecting soybean crops worldwide.  Three SMV resistance genes, R_SC4, R_SC8, and R_SC14Q , have been identified and mapped on soybean chromosomes 14, 2, and 13 from Dabaima, Kefeng 1, and Qihuang 1 cultivars, respectively.  Soybean cultivar Nannong 1138-2 is widely grown in the Yangtze River Valley of China.  In this study, crosses were made between Qihuang 1×Kefeng 1 and Dabaima×Nannong 1138-2.  Ten simple sequence repeat (SSR) markers linked to three resistance loci (R_SC4, R_SC8, and R_SC14Q ) were used to assist pyramided breeding.  Pyramided families containing three resistance loci (R_SC4, R_SC8, and R_SC14Q ) were evaluated by inoculating them with 21 SMV strains from China.  Results indicated that the 10 markers can be used effectively to assist the selection of resistant individuals containing R_SC4, R_SC8, and R_SC14Q .  A total of 53 F6 plants were confirmed to contain three homozygous alleles conferring resistance to SMV.  Five F₇ homozygous pyramided families exhibited resistance to 21 strains of SMV and showed desirable agronomic traits using dual selection.  The strategy of pyramiding resistance gene derived from different varieties has practical breeding value in providing broad-spectrum resistance against the existing strains of SMV in China.  

Dairy cows undergo tremendous changes in physiological, metabolism and the immune function from pregnancy to lactation that are associated with cows being susceptible to metabolic and infectious diseases. The objective of this study is to investigate the changes of plasma proteome on 21 d before expected calving and 1 d after calving from dairy cows using an integrated proteomic approach consisting of minor abundance protein enrichment by ProteoMiner beads, protein labeling by isobaric tags for relative and absolute quantification, and protein identification by liquid chromatography coupled with tandem mass spectrometry. Nineteen proteins were changed around the time of calving. These proteins were associated with response to stress, including acute-phase response and defense response, based on the proteins annotation. In particular, three up-regulated proteins after calving including factor V, α2-antiplasmin and prothrombin were assigned into the complement and coagulation pathway. These results may provide new information in elucidating host response to lactation and parturition stress, and inflammatory-like conditions at the protein level. Differential proteins may serve as potential markers to regulate the lactation and parturition stress in periparturient dairy cows.

Soybean mosaic virus (SMV) disease is one of the most destructive viral diseases in soybean (Glycine max (L.) Merr.). SMV strain SC3 is the major prevalent strain in huang-huai and Yangtze valleys, China. The soybean cultivar Qihuang 1 is of a rich resistance spectrum and has a wide range of application in breeding programs in China. In this study, F1, F2 and F2:3 from Qihuang 1×nannong 1138-2 were used to study inheritance and linkage mapping of the SC3 resistance gene in Qihuang 1. The secondary F2 population and near isogenic lines (nILs) derived from residual heterozygous lines (RhLs) of Qihuang 1×nannong 1138-2 were separatively used in the fine mapping and candidate gene analysis of the resistance gene. Results indicated that a single dominant gene (designated RSC3Q) controls resistance, which was located on chromosome 13. Two genomic-simple sequence repeat (SSR) markers BARCSOYSSR_13_1114 and BARCSOYSSR_13_1136 were found flanking the two sides of the RSC3Q. The interval between the two markers was 651 kb. Quantitative real-time PCR analysis of the candidate genes showed that five genes (Glyma13g25730, 25750, 25950, 25970 and 26000) were likely involved in soybean SMV resistance. These results would have utility in cloning of RSC3Q resistance candidate gene and marker-assisted selection (MaS) in resistance breeding to SMV.

Bacterial leaf streak (BLS) of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc) is a worldwide destructive disease. Development of resistant varieties is considered to be one of the most effective and eco-friendly ways to control the disease. However, only a few genes/QTLs having resistance to BLS have been identified in rice until now. In the present study, we have identified and primarily mapped a BLS-resistance gene, bls1, from a rice line DP3, derived from the wild rice species Oryza rufipogon Griff. A BC2F2 (9311/DP3//9311) population was constructed to map BLS-resistance gene in the rice line DP3. The segregation of the resistant and susceptible plants in BC2F2 in 1:3 ratio (χ2=0.009, χ2 0.05, 1=3.84, P>0.05), suggested that a recessive gene confers BLS resistance in DP3. In bulked segregant analysis (BSA), two SSR markers RM8116 and RM584 were identified to be polymorphic in resistant and susceptible DNA bulks. For further mapping the resistance gene, six polymorphic markers around the target region were applied to analyze the genotypes of the BC2F2 individuals. As a result, the BLS-resistant gene, designated as bls1, was mapped in a 4.0-cM region flanked by RM587 and RM510 on chromosome 6.