Cotton Research Institute

Yuxian Zhu and colleagues report the draft genome of a diploid cotton Gossypium raimondii. This species is a wild South American cotton, whose progenitor is thought to have been the contributor of the D subgenome of the allotetraploid commercial species Gossypium hirsutum and Gossypium barbadense, which account for ~95% of the worldwide cotton crop. We have sequenced and assembled a draft genome of G. raimondii, whose progenitor is the putative contributor of the D subgenome to the economically important fiber-producing cotton species Gossypium hirsutum and Gossypium barbadense. Over 73% of the assembled sequences were anchored on 13 G. raimondii chromosomes. The genome contains 40,976 protein-coding genes, with 92.2% of these further confirmed by transcriptome data. Evidence of the hexaploidization event shared by the eudicots as well as of a cotton-specific whole-genome duplication approximately 13–20 million years ago was observed. We identified 2,355 syntenic blocks in the G. raimondii genome, and we found that approximately 40% of the paralogous genes were present in more than 1 block, which suggests that this genome has undergone substantial chromosome rearrangement during its evolution. Cotton, and probably Theobroma cacao, are the only sequenced plant species that possess an authentic CDN1 gene family for gossypol biosynthesis, as revealed by phylogenetic analysis.

Long non-coding (lnc) RNAs are non-coding RNAs longer than 200 nt. lncRNAs primarily interact with mRNA, DNA, protein, and miRNA and consequently regulate gene expression at the epigenetic, transcriptional, post-transcriptional, translational, and post-translational levels in a variety of ways. They play important roles in biological processes such as chromatin remodeling, transcriptional activation, transcriptional interference, RNA processing, and mRNA translation. lncRNAs have important functions in plant growth and development; biotic and abiotic stress responses; and in regulation of cell differentiation, the cell cycle, and the occurrence of many diseases in humans and animals. In this review, we summarize the functions and mechanisms of lncRNAs in plants, humans, and animals at different regulatory levels.

Soil heavy metal pollution has become a worldwide environmental issue that has attracted considerable public attention, largely from the increasing concern for the security of agricultural products. Heavy metals refer to some metals and metalloids possessing biological toxicity, such as cadmium, mercury, arsenic, lead, and chromium. These elements enter the soil agro-ecosystem through natural processes derived from parent materials, and through anthropogenic activities. Heavy metal pollution poses a great threat to the health and well-being of organisms and human beings due to potential accumulation risk through the food chain. Remediation using chemical, physical, and biological methods has been adopted to solve the problem. Phytoremediation has proven to be a promising alternative to conventional approaches as it is cost effective, environmentally friendly, and aesthetically pleasing. To date, based on the natural ability of extraction, approximately 500 taxa have been identified as hyperaccumulators of one or more metals. In addition, further research integrating biotechnological approaches with comprehensive multidisciplinary research is needed to improve plant tolerance and reduce the accumulation of toxic metals in soils. This review discusses harmful effects, sources of heavy metals, and the remediation technologies for soil contaminated by heavy metals.

Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation: http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. Supplementary information: Supplementary data are available at Bioinformatics online.

The use of chemicals around the globe in different industries has been increased tremendously affecting the health of people. The modern world is intending towards the replacement of these noxious chemicals with environmentally friendly products for the betterment of life on the planet. This has been a prime objective for the scientist to establish enzymatic processes in spite of chemical processes. Various enzymes specifically microbial proteases are considered the most essentially used in different corporate sectors like textile, detergent, leather, feed, waste and others. Proteases with respect to physiological and commercial roles hold a pivotal position. As they are performing synthetic and degradative functions, proteases are found ubiquitously such as in plants, animals and microbes. Among different producers of proteases, Bacillus sp. are mostly commercially exploited microbe for proteases. Proteases are successfully considered as an alternative to the chemicals and an eco-friendly indicator for nature or surrounding. Evolutionary relationship among acidic, neutral and alkaline proteases has been analyzed based on their protein sequences but still there is a paucity of information that regulates the diversity in their specificity. Researchers are looking for microbial proteases as they can tolerate harsh conditions, ways to prevent autoproteolytic activity, stability in optimum pH and substrate specificity. The current review is focusing on the comparison among different proteases and the current problems facing during production and application at industrial level. Deciphering these issues would enable us to promote microbial proteases economically and commercially around the world.

Despite rapidly decreasing costs and innovative technologies, sequencing of angiosperm genomes is not yet undertaken lightly. Generating larger amounts of sequence data more quickly does not address the difficulties of sequencing and assembling complex genomes de novo. The cotton ( Gossypium spp.)

With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

Drought stress restricts plant growth and development by altering metabolic activity and biological functions. However, plants have evolved several cellular and molecular mechanisms to overcome drought stress. Drought tolerance is a multiplex trait involving the activation of signaling mechanisms and differentially expressed molecular responses. Broadly, drought tolerance comprises two steps: stress sensing/signaling and activation of various parallel stress responses (including physiological, molecular, and biochemical mechanisms) in plants. At the cellular level, drought induces oxidative stress by overproduction of reactive oxygen species (ROS), ultimately causing the cell membrane to rupture and stimulating various stress signaling pathways (ROS, mitogen-activated-protein-kinase, Ca2+, and hormone-mediated signaling). Drought-induced transcription factors activation and abscisic acid concentration co-ordinate the stress signaling and responses in cotton. The key responses against drought stress, are root development, stomatal closure, photosynthesis, hormone production, and ROS scavenging. The genetic basis, quantitative trait loci and genes of cotton drought tolerance are presented as examples of genetic resources in plants. Sustainable genetic improvements could be achieved through functional genomic approaches and genome modification techniques such as the CRISPR/Cas9 system aid the characterization of genes, sorted out from stress-related candidate single nucleotide polymorphisms, quantitative trait loci, and genes. Exploration of the genetic basis for superior candidate genes linked to stress physiology can be facilitated by integrated functional genomic approaches. We propose a third-generation sequencing approach coupled with genome-wide studies and functional genomic tools, including a comparative sequenced data (transcriptomics, proteomics, and epigenomic) analysis, which offer a platform to identify and characterize novel genes. This will provide information for better understanding the complex stress cellular biology of plants.

Background: Seed is vital for plant survival and dispersion, however, its development and germination are influenced by various internal and external factors. Abscisic acid (ABA) is one of the most important phytohormones that influence seed development and germination. Until now, impressive progresses in ABA metabolism and signaling pathways during seed development and germination have been achieved. At the molecular level, ABA biosynthesis, degradation, and signaling genes were identified to play important roles in seed development and germination. Additionally, the crosstalk between ABA and other hormones such as gibberellins (GA), ethylene (ET), Brassinolide (BR), and auxin also play critical roles. Although these studies explored some actions and mechanisms by which ABA-related factors regulate seed morphogenesis, dormancy, and germination, the complete network of ABA in seed traits is still unclear. Aim of review: Presently, seed faces challenges in survival and viability. Due to the vital positive roles in dormancy induction and maintenance, as well as a vibrant negative role in the seed germination of ABA, there is a need to understand the mechanisms of various ABA regulators that are involved in seed dormancy and germination with the updated knowledge and draw a better network for the underlying mechanisms of the ABA, which would advance the understanding and artificial modification of the seed vigor and longevity regulation. Key scientific concept of review: Here, we review functions and mechanisms of ABA in different seed development stages and seed germination, discuss the current progresses especially on the crosstalk between ABA and other hormones and signaling molecules, address novel points and key challenges (e.g., exploring more regulators, more cofactors involved in the crosstalk between ABA and other phytohormones, and visualization of active ABA in the plant), and outline future perspectives for ABA regulating seed associated traits.

Cotton (Gossypium hirsutum L.) production in China has developed rapidly during the last 60 years. In 2012, the planting area and total output in the country were 5.3 million hectares and 7.62 million tons, respectively, and the unit yield was 85% higher than the world average. China currently accounts for about 30% of the world's cotton output with only 15% of the world's cotton land. Enhanced cotton production, particularly the high unit yield is largely due to adoption of a series of intensive farming technologies and cultural practices. The intensive farming technologies for cotton production in China mainly include seedling transplanting, plastic mulching, double cropping, plant training and super-high plant density technique, which have played important roles in promoting unit yield and total output. Although such intensive farming technologies meet the need of a growing population under limited arable land in China, they are labor-intensive and involve large input of various kinds of chemical products like fertilizers, pesticides, and plastic films. Thus, there are increasing challenges from soil pollution and labor shortage. Here, the achievements, challenges, countermeasures and prospects for intensive cotton cultivation in China are reviewed. An important conclusion from this review is that the establishment of a new farming technology through reform of the current intensive technology is inevitable to support sustainable cotton production in the nation. A series of comprehensive countermeasures should be taken to reduce soil pollution through rational use of plastic film and chemicals, labor saving through simplifying field managements and mechanization and increasing benefits by reforming the cropping system and management mode. China's cotton production would be sustainable with a bright prospect if supported by new farming technologies.

Multiple cotton genomes (diploid and tetraploid) have been assembled. However, genomic variations between cultivars of allotetraploid upland cotton (Gossypium hirsutum L.), the most widely planted cotton species in the world, remain unexplored. Here, we use single-molecule long read and Hi-C sequencing technologies to assemble genomes of the two upland cotton cultivars TM-1 and zhongmiansuo24 (ZM24). Comparisons among TM-1 and ZM24 assemblies and the genomes of the diploid ancestors reveal a large amount of genetic variations. Among them, the top three longest structural variations are located on chromosome A08 of the tetraploid upland cotton, which account for ~30% total length of this chromosome. Haplotype analyses of the mapping population derived from these two cultivars and the germplasm panel show suppressed recombination rates in this region. This study provides additional genomic resources for the community, and the identified genetic variations, especially the reduced meiotic recombination on chromosome A08, will help future breeding.

BACKGROUND: Late embryogenesis abundant (LEA) proteins are large groups of hydrophilic proteins with major role in drought and other abiotic stresses tolerance in plants. In-depth study and characterization of LEA protein families have been carried out in other plants, but not in upland cotton. The main aim of this research work was to characterize the late embryogenesis abundant (LEA) protein families and to carry out gene expression analysis to determine their potential role in drought stress tolerance in upland cotton. Increased cotton production in the face of declining precipitation and availability of fresh water for agriculture use is the focus for breeders, cotton being the backbone of textile industries and a cash crop for many countries globally. RESULTS: In this work, a total of 242, 136 and 142 LEA genes were identified in G. hirsutum, G. arboreum and G. raimondii respectively. The identified genes were classified into eight groups based on their conserved domain and phylogenetic tree analysis. LEA 2 were the most abundant, this could be attributed to their hydrophobic character. Upland cotton LEA genes have fewer introns and are distributed in all chromosomes. Majority of the duplicated LEA genes were segmental. Syntenic analysis showed that greater percentages of LEA genes are conserved. Segmental gene duplication played a key role in the expansion of LEA genes. Sixty three miRNAs were found to target 89 genes, such as miR164, ghr-miR394 among others. Gene ontology analysis revealed that LEA genes are involved in desiccation and defense responses. Almost all the LEA genes in their promoters contained ABRE, MBS, W-Box and TAC-elements, functionally known to be involved in drought stress and other stress responses. Majority of the LEA genes were involved in secretory pathways. Expression profile analysis indicated that most of the LEA genes were highly expressed in drought tolerant cultivars Gossypium tomentosum as opposed to drought susceptible, G. hirsutum. The tolerant genotypes have a greater ability to modulate genes under drought stress than the more susceptible upland cotton cultivars. CONCLUSION: The finding provides comprehensive information on LEA genes in upland cotton, G. hirsutum and possible function in plants under drought stress.

The mapping of functional genes plays an important role in studies of genome structure, function, and evolution, as well as allowing gene cloning and marker-assisted selection to improve agriculturally important traits. Simple sequence repeats (SSRs) developed from expressed sequence tags (ESTs), EST-SSR (eSSR), can be employed as putative functional marker loci to easily tag corresponding functional genes. In this paper, 2218 eSSRs, 1554 from G. raimondii-derived and 754 from G. hirsutum-derived ESTs, were developed and used to screen polymorphisms to enhance our backbone genetic map in allotetraploid cotton. Of the 1554 G. raimondii-derived eSSRs, 744 eSSRs were able to successfully amplify polymorphisms between our two mapping parents, TM-1 and Hai7124, presenting a polymorphic rate of 47.9%. However, only a 23.9% (159/754) polymorphic rate was produced from G. hirsutum-derived eSSRs. No relationship was observed between the level of polymorphism, motif type, and tissue origin, but the polymorphism appeared to be correlated with repeat type. After integrating these new eSSRs, our enhanced genetic map consists of 1790 loci in 26 linkage groups and covers 3425.8 cM with an average intermarker distance of 1.91 cM. This microsatellite-based, gene-rich linkage map contains 71.96% functional marker loci, of which 87.11% are eSSR loci. There were 132 duplicated loci bridging 13 homeologous At/Dt chromosome pairs. Two reciprocal translocations after polyploidization between A2 and A3, and between A4 and A5, chromosomes were further confirmed. A functional analysis of 975 ESTs producing 1122 eSSR loci tagged in the map revealed that 60% had clear BLASTX hits (<1e(-10)) to the Uniprot database and that 475 were associated mainly with genes belonging to the three major gene ontology categories of biological process, cellular component, and molecular function; many of the ESTs were associated with two or more category functions. The results presented here will provide new insights for future investigations of functional and evolutionary genomics, especially those associated with cotton fiber improvement.

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.

BACKGROUND: Sesame is an important oil crop due to its high oil, antioxidant, and protein content. Drought stress is a major abiotic stress that affects sesame production as well as the quality of sesame seed. To reveal the adaptive mechanism of sesame in response to water deficient conditions, transcriptomic and metabolomics were applied in drought-tolerant (DT) and drought-susceptible (DS) sesame genotypes. RESULTS: Transcriptomic analysis reveals a set of core drought-responsive genes (684 up-regulated and 1346 down-regulated) in sesame that was robustly differently expressed in both genotypes. Most enriched drought-responsive genes are mainly involved in protein processing in endoplasmic reticulum, plant hormone signal transduction photosynthesis, lipid metabolism, and amino acid metabolism. Drought-susceptible genotype was more disturbed by drought stress at both transcriptional and metabolic levels, since more drought-responsive genes/metabolites were identified in DS. Drought-responsive genes associated with stress response, amino acid metabolism, and reactive oxygen species scavenging were more enriched or activated in DT. According to the partial least-squares discriminate analysis, the most important metabolites which were accumulated under drought stress in both genotypes includes ABA, amino acids, and organic acids. Especially, higher levels of ABA, proline, arginine, lysine, aromatic and branched chain amino acids, GABA, saccharopine, 2-aminoadipate, and allantoin were found in DT under stress condition. Combination of transcriptomic and metabolomic analysis highlights the important role of amino acid metabolism (especially saccharopine pathway) and ABA metabolism and signaling pathway for drought tolerance in sesame. CONCLUSION: The results of the present study provide valuable information for better understanding the molecular mechanism underlying drought tolerance of sesame, and also provide useful clues for the genetic improvement of drought tolerance in sesame.

Genome editing is an important tool for gene functional studies as well as crop improvement. The recent development of the CRISPR/Cas9 system using single guide RNA molecules (sgRNAs) to direct precise double strand breaks in the genome has the potential to revolutionize agriculture. Unfortunately, not all sgRNAs are equally efficient and it is difficult to predict their efficiency by bioinformatics. In crops such as cotton (Gossypium hirsutum L.), with labor-intensive and lengthy transformation procedures, it is essential to minimize the risk of using an ineffective sgRNA that could result in the production of transgenic plants without the desired CRISPR-induced mutations. In this study, we have developed a fast and efficient method to validate the functionality of sgRNAs in cotton using a transient expression system. We have used this method to validate target sites for three different genes GhPDS, GhCLA1, and GhEF1 and analyzed the nature of the CRISPR/Cas9-induced mutations. In our experiments, the most frequent type of mutations observed in cotton cotyledons were deletions (~64%). We prove that the CRISPR/Cas9 system can effectively produce mutations in homeologous cotton genes, an important requisite in this allotetraploid crop. We also show that multiple gene targeting can be achieved in cotton with the simultaneous expression of several sgRNAs and have generated mutations in GhPDS and GhEF1 at two target sites. Additionally, we have used the CRISPR/Cas9 system to produce targeted gene fragment deletions in the GhPDS locus. Finally, we obtained transgenic cotton plants containing CRISPR/Cas9-induced gene editing mutations in the GhCLA1 gene. The mutation efficiency was very high, with 80.6% of the transgenic lines containing mutations in the GhCLA1 target site resulting in an intense albino phenotype due to interference with chloroplast biogenesis.

Plants are constantly exposed to various phytopathogens such as fungi, Oomycetes, nematodes, bacteria, and viruses. These pathogens can significantly reduce the productivity of important crops worldwide, with annual crop yield losses ranging from 20% to 40% caused by various pathogenic diseases. While the use of chemical pesticides has been effective at controlling multiple diseases in major crops, excessive use of synthetic chemicals has detrimental effects on the environment and human health, which discourages pesticide application in the agriculture sector. As a result, researchers worldwide have shifted their focus towards alternative eco-friendly strategies to prevent plant diseases. Biocontrol of phytopathogens is a less toxic and safer method that reduces the severity of various crop diseases. A variety of biological control agents (BCAs) are available for use, but further research is needed to identify potential microbes and their natural products with a broad-spectrum antagonistic activity to control crop diseases. This review aims to highlight the importance of biocontrol strategies for managing crop diseases. Furthermore, the role of beneficial microbes in controlling plant diseases and the current status of their biocontrol mechanisms will be summarized. The review will also cover the challenges and the need for the future development of biocontrol methods to ensure efficient crop disease management for sustainable agriculture.

Plant roots and soil microorganisms interact with each other mainly in the rhizosphere. Changes in the community structure of the rhizosphere microbiome are influenced by many factors. In this study, we determined the community structure of rhizosphere bacteria in cotton, and studied the variation of rhizosphere bacterial community structure in different soil types and developmental stages using TM-1, an upland cotton cultivar (Gossypium hirsutum L.) and Hai 7124, a sea island cotton cultivar (G. barbadense L.) by high-throughput sequencing technology. Six bacterial phyla were found dominantly in cotton rhizosphere bacterial community including Acidobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Proteobacteria, and Verrucomicrobia. The abundance of Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes and Proteobacteria were largely influenced by cotton root. Bacterial α-diversity in rhizosphere was lower than that of bulk soil in nutrient-rich soil, but higher in cotton continuous cropping field soil. The β-diversity in nutrient-rich soil was greater than that in continuous cropping field soil. The community structure of the rhizosphere bacteria varied significantly during different developmental stages. Our results provided insights into the dynamics of cotton rhizosphere bacterial community and would facilitate to improve cotton growth and development through adjusting soil bacterial community structure artificially.

Endophytic fungi (EF) are a group of fascinating host-associated fungal communities that colonize the intercellular or intracellular spaces of host tissues, providing beneficial effects to their hosts while gaining advantages. In recent decades, accumulated research on endophytic fungi has revealed their biodiversity, wide-ranging ecological distribution, and multidimensional interactions with host plants and other microbiomes in the symbiotic continuum. In this review, we highlight the role of secondary metabolites (SMs) as effectors in these multidimensional interactions, and the biosynthesis of SMs in symbiosis via complex gene expression regulation mechanisms in the symbiotic continuum and via the mimicry or alteration of phytochemical production in host plants. Alternative biological applications of SMs in modern medicine, agriculture, and industry and their major classes are also discussed. This review recapitulates an introduction to the research background, progress, and prospects of endophytic biology, and discusses problems and substantive challenges that need further study.

Salinization usually plays a primary role in soil degradation, which consequently reduces agricultural productivity. In this study, the effects of salinity on growth parameters, ion, chlorophyll, and proline content, photosynthesis, antioxidant enzyme activities, and lipid peroxidation of two cotton cultivars, [CCRI-79 (salt tolerant) and Simian 3 (salt sensitive)], were evaluated. Salinity was investigated at 0 mM, 80 mM, 160 mM, and 240 mM NaCl for 7 days. Salinity induced morphological and physiological changes, including a reduction in the dry weight of leaves and roots, root length, root volume, average root diameter, chlorophyll and proline contents, net photosynthesis and stomatal conductance. In addition, salinity caused ion imbalance in plants as shown by higher Na+ and Cl- contents and lower K+, Ca2+, and Mg2+ concentrations. Ion imbalance was more pronounced in CCRI-79 than in Simian3. In the leaves and roots of the salt-tolerant cultivar CCRI-79, increasing levels of salinity increased the activities of superoxide dismutase (SOD), ascorbate peroxidase (APX), and glutathione reductase (GR), but reduced catalase (CAT) activity. The activities of SOD, CAT, APX, and GR in the leaves and roots of CCRI-79 were higher than those in Simian 3. CAT and APX showed the greatest H2O2 scavenging activity in both leaves and roots. Moreover, CAT and APX activities in conjunction with SOD seem to play an essential protective role in the scavenging process. These results indicate that CCRI-79 has a more effective protection mechanism and mitigated oxidative stress and lipid peroxidation by maintaining higher antioxidant activities than those in Simian 3. Overall, the chlorophyll a, chlorophyll b, and Chl (a+b) contents, net photosynthetic rate and stomatal conductance, SOD, CAT, APX, and GR activities showed the most significant variation between the two cotton cultivars.