Cotton breeding in Australia : meeting the challenges of the 21st century

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program’s commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century

Highly Efficient Targeted Gene Editing in Upland Cotton Using the CRISPR/Cas9 System

The clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) gene editing system has been shown to be able to induce highly efficient mutagenesis in the targeted DNA of many plants, including cotton, and has become an important tool for investigation of gene function and crop improvement. Here, we developed a simple and easy to operate CRISPR/Cas9 system and demonstrated its high editing efficiency in cotton by targeting-ALARP, a gene encoding alanine-rich protein that is preferentially expressed in cotton fibers. Based on sequence analysis of the target site in the 10 transgenic cottons containing CRISPR/Cas9, we found that the mutation frequencies of GhALARP-A and GhALARP-D target sites were 71.4–100% and 92.9–100%, respectively. The most common editing event was deletion, but deletion together with large insertion was also observed. Mosaic mutation editing events were detected in most transgenic plants. No off-target mutation event was detected in any the 15 predicted sites analyzed. This study provided mutants for further study of the function of GhALARP in cotton fiber development. Our results further demonstrated the feasibility of use of CRISPR/Cas9 as a targeted mutagenesis tool in cotton, and provided an efficient tool for targeted mutagenesis and functional genomics in cotton

Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering

In this paper, we propose a novel algorithm for medical image segmentation, which combines the density peaks clustering (DPC) with the fruit fly optimization algorithm, and it has the following advantages. Firstly, it avoids the problem of DPC that needs to artificially select parameters (such as the number of clusters) in its decision graph and thus can automatically determine their values. Secondly, our algorithm uses random step size, instead of the fixed step size as in the fruit fly optimization algorithm, which helps avoid falling into local optima. Thirdly, our algorithm selects the cut-off distance and the cluster centers using the image entropy value and can better capture the structures of the image. Experiments on benchmark dataset and proprietary dataset show that our algorithm can adaptively segment medical images with faster convergence and better robustness

Transcriptome Sequencing and Metabolome Analysis Reveal Genes Involved in Pigmentation of Green-Colored Cotton Fibers

Green-colored fiber (GCF) is the unique raw material for naturally colored cotton textile but we know little about the pigmentation process in GCF. Here we compared transcriptomes and metabolomes of 12, 18 and 24 days post-anthesis (DPA) fibers from a green fiber cotton accession and its white-colored fiber (WCF) near-isogenic line. We found a total of 2047 non-redundant metabolites in GCF and WCF that were enriched in 80 pathways, including those of biosynthesis of phenylpropanoid, cutin, suberin, and wax. Most metabolites, particularly sinapaldehyde, of the phenylpropanoid pathway had a higher level in GCF than in WCF, consistent with the significant up-regulation of the genes responsible for biosynthesis of those metabolites. Weighted gene co-expression network analysis (WGCNA) of genes differentially expressed between GCF and WCF was used to uncover gene-modules co-expressed or associated with the accumulation of green pigments. Of the 16 gene-modules co-expressed with fiber color or time points, the blue module associated with G24 (i.e., GCF at 24 DPA) was of particular importance because a large proportion of its genes were significantly up-regulated at 24 DPA when fiber color was visually distinguishable between GCF and WCF. A total of 56 hub genes, including the two homoeologous Gh4CL4 that could act in green pigment biosynthesis, were identified among the genes of the blue module that are mainly involved in lipid metabolism, phenylpropanoid biosynthesis, RNA transcription, signaling, and transport. Our results provide novel insights into the mechanisms underlying pigmentation of green fibers and clues for developing cottons with stable green colored fibers

Numerical analysis and optimization of the charging process on a shell‐and‐tube latent heat thermal energy storage unit for a solar power plant with direct steam generation

Abstract A two‐dimensional model of the charging process on a heat storage unit in a shell‐and‐tube type latent heat subsystem of a solar power plant with direct steam generation was constructed in this study. The effects of the outer diameter to inner diameter ratio, aspect ratio, phase change material (PCM) thermal conductivity, and heat transfer fluid (HTF) mass flow rate were investigated. Results show that increasing the PCM thermal conductivity, HTF mass flow rate, and aspect ratio of the heat storage unit can shorten heat storage time, but the ratio of the outer diameter to the inner diameter of the heat storage unit has an optimal value of 6 in this problem. Using response surface methodology analysis, the influence of the aspect ratio, outer‐to‐inner‐diameter ratio, PCM thermal conductivity, and HTF mass flow rate on the storage time of the phase change heat storage unit is in descending order. After a genetic algorithm optimization, the storage rate of the heat storage unit increased by 35%. The results of this study can guide the heat storage unit to achieve a better practical application performance

Transcriptome Profiling Provides New Insights into the Molecular Mechanism Underlying the Sensitivity of Cotton Varieties to Mepiquat Chloride

Mepiquat chloride (MC) is a plant growth regulator widely used in cotton production to control vegetative overgrowth of cotton plants to achieve ideal plant architecture required for high yielding. Cotton varieties respond differently to MC application, but there is little information about the molecular mechanisms underlying the varietal difference. In this study, comparative transcriptome analysis was conducted by using two Upland cotton varieties with different sensitivity (XLZ74, insensitive; SD1068, sensitive) to MC treatment, aiming to understand the molecular mechanisms responsible for varietal difference of MC sensitivity. RNA-seq data were generated from the two varieties treated with MC or water at three time points, 1, 3 and 6 days post-spray (dps). Genes differentially expressed between the MC and mock treatments of XLZ74 (6252) and SD1068 (6163) were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to compare the enriched GO terms and KEGG pathways between the two varieties. Signal transduction of phytohormones, biosynthesis of gibberellins (GAs) and brassinosteroids (BRs) and profiles of transcription factors (TFs) seemed to be differentially affected by MC in the two varieties. The transcriptomic results were further consolidated with the content changes of phytohormones in young stem. Several GA catabolic genes, GA2ox, were highly induced by MC in both varieties especially in SD1068, consistent with a more significant decrease in GA4 in SD1068. Several AUX/IAA and SAUR genes and CKX genes were induced by MC in both varieties, but with a more profound effect observed in SD1068 that showed a significant reduction in indole-3-acetic acid (IAA) and a significant increase in cytokinin (CTK) at 6 days post-spray (dps). BR biosynthesis-related genes were downregulated in SD1068, but not in XLZ74. Additionally, more downregulated TFs were observed in MC-treated SD1068 than in MC-treated XLZ74, and the two varieties had very different profiles of genes involved in starch and sucrose metabolism, with those of SD1068 and XLZ74 being downregulated and upregulated by MC treatment, respectively. Together, these results indicate that although the same or similar biological pathways are affected by MC treatment in cotton varieties showing different MC sensitivity, the extent of effect is variable, leading to their different phenotypic outcomes. How the quantitative effect of MC on the biological processes associated with growth retardation is regulated is still an open question

Data from: Molecular analysis of caffeoyl residues related to pigmentation in green cotton fibers

The pigment components in green cotton fibers were isolated and identified as 22-O-caffeoyl-22-hydroxymonodocosanoin and 22-O-caffeoyl-22-hydroxydocosanoic acid. The concentration of 22-O-caffeoyl-22-hydroxymonodocosanoin was correlated positively with the degree of green fibers of color, indicating a role of caffeoyl derivatives in pigmentation of green cotton fibers. Upland cotton (Gossypium hirsutum L.) contains four genes (Gh4CL1 to Gh4CL4) encoding 4-coumarate:CoA ligase (4CL), key enzymes of the phenylpropanoid biosynthesis pathway. In 15 to 24 days-post anthesis fibers, the expression level of Gh4CL1 was very low, Gh4CL3 had a similar expression level between the white and green cottons, Gh4CL2 had a significantly higher expression level in the green fibers than in the white fibers, whereas Gh4CL4 seemed to have a higher expression level in the white fibers than in the green fibers. According to enzyme kinetics analysis, Gh4CL1 displayed a preference for 4-coumarate, Gh4CL3 and Gh4CL4 exhibited a somewhat low but still prominent activity towards ferulate, while Gh4CL2 had a strong preference for caffeate and ferulate. These results suggest that Gh4CL2 might be involved in the metabolism of caffeoyl residues and related to pigment biosynthesis in green cotton fibers. Our findings provide insights for understanding biochemical and molecular mechanisms of pigmentation in green cotton fibers

Probing Dynamic Variation of Layered Microstructure Using Backscattering Polarization Imaging

Polarization imaging can quantitatively probe the microscopic structure of biological tissues which can be complex and consist of layered structures. In this paper, we established a fast-backscattering Mueller matrix imaging system to characterize the dynamic variation in the microstructure of single-layer and double-layer tissues as glycerin solution penetrated into the samples. The characteristic response of Mueller matrix elements, as well as polarization parameters with clearer physics meanings, show that polarization imaging can capture the dynamic variation in the layered microstructure. The experimental results are confirmed by Monte Carlo simulations. Further examination on the accuracy of Mueller matrix measurements also shows that much faster speed has to be considered when backscattering Mueller matrix imaging is applied to living samples