IC4R-2.0: Rice Genome Reannotation Using Massive RNA-seq Data

Efficient and multiplex gene upregulation in plants through CRISPR-Cas-mediated knockin of enhancers

Gene upregulation through genome editing is important for plant research and breeding. Targeted insertion of short transcriptional enhancers (STEs) into gene promoters may offer a universal solution akin to transgene-mediated overexpression while avoiding the drawbacks associated with transgenesis. Here, we introduce an "in locus activation" technique in rice that leverages well-characterized STEs for refined, heritable, and multiplexed gene upregulation. To address the scarcity of potent enhancers, we developed a large-scale mining approach and discovered a suite of STEs that are capable of enhancing gene expression in rice protoplasts. The in locus integration of these STEs into eight rice genes resulted in substantial transcriptional upregulation in the edited plants, with up to 869.1-fold increases in their transcript levels. Employing a variety of STEs, we achieved delicate control of gene expression, enabling the fine-tuning of key phenotypic traits such as plant height. Our approach also enabled efficient multiplexed gene upregulation, with up to four genes activated simultaneously, significantly enhancing the nicotinamide mononucleotide metabolic pathway. Importantly, heritability studies from the T0 to T3 generations confirmed the stable and heritable nature of STE-driven gene activation. Collectively, our work demonstrates that coupled with STE mining, leveraging genome editing for in locus activation and gene upregulation holds great promise to be widely adopted in fundamental plant research and crop breeding.

Potential Roles of the GRF Transcription Factors in Sorghum Internodes during Post-Reproductive Stages

Growth-regulating factor (GRF) is a plant-specific family of transcription factors crucial for meristem development and plant growth. Sorghum (Sorghum bicolor L. Moench) is a cereal species widely used for food, feed and fuel. While sorghum stems are important biomass components, the regulation of stem development and the carbohydrate composition of the stem tissues remain largely unknown. Here, we identified 11 SbGRF-encoding genes and found the SbGRF expansion driven by whole-genome duplication events. By comparative analyses of GRFs between rice and sorghum, we demonstrated the divergence of whole-genome duplication (WGD)-derived OsGRFs and SbGRFs. A comparison of SbGRFs' expression profiles supports that the WGD-duplicated OsGRFs and SbGRFs experienced distinct evolutionary trajectories, possibly leading to diverged functions. RNA-seq analysis of the internode tissues identified several SbGRFs involved in internode elongation, maturation and cell wall metabolism. We constructed co-expression networks with the RNA-seq data of sorghum internodes. Network analysis discovered that SbGRF1, 5 and 7 could be involved in the down-regulation of the biosynthesis of cell wall components, while SbGRF4, 6, 8 and 9 could be associated with the regulation of cell wall loosening, reassembly and/or starch biosynthesis. In summary, our genome-wide analysis of SbGRFs reveals the distinct evolutionary trajectories of WGD-derived SbGRF pairs. Importantly, expression analyses highlight previously unknown functions of several SbGRFs in internode elongation, maturation and the potential involvement in the metabolism of the cell wall and starch during post-anthesis stages.

Divergent Roles of the Auxin Response Factors in Lemongrass (Cymbopogon flexuosus (Nees ex Steud.) W. Watson) during Plant Growth

Auxin Response Factors (ARFs) make up a plant-specific transcription factor family that mainly couples perception of the phytohormone, auxin, and gene expression programs and plays an important and multi-faceted role during plant growth and development. Lemongrass (Cymbopogon flexuosus) is a representative Cymbopogon species widely used in gardening, beverages, fragrances, traditional medicine, and heavy metal phytoremediation. Biomass yield is an important trait for several agro-economic purposes of lemongrass, such as landscaping, essential oil production, and phytoremediation. Therefore, we performed gene mining of CfARFs and identified 26 and 27 CfARF-encoding genes in each of the haplotype genomes of lemongrass, respectively. Phylogenetic and domain architecture analyses showed that CfARFs can be divided into four groups, among which groups 1, 2, and 3 correspond to activator, repressor, and ETTN-like ARFs, respectively. To identify the CfARFs that may play major roles during the growth of lemongrass plants, RNA-seq was performed on three tissues (leaf, stem, and root) and four developmental stages (3-leaf, 4-leaf, 5-leaf. and mature stages). The expression profiling of CfARFs identified several highly expressed activator and repressor CfARFs and three CfARFs (CfARF3, 18, and 35) with gradually increased levels during leaf growth. Haplotype-resolved transcriptome analysis revealed that biallelic expression dominance is frequent among CfARFs and contributes to their gene expression patterns. In addition, co-expression network analysis identified the modules enriched with CfARFs. By establishing orthologous relationships among CfARFs, sorghum ARFs, and maize ARFs, we showed that CfARFs were mainly expanded by whole-genome duplications, and that the duplicated CfARFs might have been divergent due to differential expression and variations in domains and motifs. Our work provides a detailed catalog of CfARFs in lemongrass, representing a first step toward characterizing CfARF functions, and may be useful in molecular breeding to enhance lemongrass plant growth.

Updated Gene Prediction of the Cucumber (9930) Genome through Manual Annotation

Thorough and precise gene structure annotations are essential for maximizing the benefits of genomic data and unveiling valuable genetic insights. The cucumber genome was first released in 2009 and updated in 2019. To increase the accuracy of the predicted gene models, 64 published RNA-seq data and 9 new strand-specific RNA-seq data from multiple tissues were used for manual comparison with the gene models. The updated annotation file (V3.1) contains an increased number (24,145) of predicted genes compared to the previous version (24,317 genes), with a higher BUSCO value of 96.9%. A total of 6231 and 1490 transcripts were adjusted and newly added, respectively, accounting for 31.99% of the overall gene tally. These newly added and adjusted genes were renamed (CsaV3.1_XGXXXXX), while genes remaining unaltered preserved their original designations. A random selection of 21 modified/added genes were validated using RT-PCR analyses. Additionally, tissue-specific patterns of gene expression were examined using the newly obtained transcriptome data with the revised gene prediction model. This improved annotation of the cucumber genome will provide essential and accurate resources for studies in cucumber.

Genome-wide identification and characterization of protein phosphatase 2C (PP2C) gene family in sunflower (Helianthus annuus L.) and their expression profiles in response to multiple abiotic stresses

Plant protein phosphatase 2C (PP2C) plays vital roles in responding to various stresses, stimulating growth factors, phytohormones, and metabolic activities in many important plant species. However, the PP2C gene family has not been investigated in the economically valuable plant species sunflower (Helianthus annuus L.). This study used comprehensive bioinformatics tools to identify and characterize the PP2C gene family members in the sunflower genome (H. annuus r1.2). Additionally, we analyzed the expression profiles of these genes using RNA-seq data under four different stress conditions in both leaf and root tissues. A total of 121 PP2C genes were identified in the sunflower genome distributed unevenly across the 17 chromosomes, all containing the Type-2C phosphatase domain. HanPP2C genes are divided into 15 subgroups (A-L) based on phylogenetic tree analysis. Analyses of conserved domains, gene structures, and motifs revealed higher structural and functional similarities within various subgroups. Gene duplication and collinearity analysis showed that among the 53 HanPP2C gene pairs, 48 demonstrated segmental duplications under strong purifying selection pressure, with only five gene pairs showing tandem duplications. The abundant segmental duplication was observed compared to tandem duplication, which was the major factor underlying the dispersion of the PP2C gene family in sunflowers. Most HanPP2C proteins were localized in the nucleus, cytoplasm, and chloroplast. Among the 121 HanPP2C genes, we identified 71 miRNAs targeting 86 HanPP2C genes involved in plant developmental processes and response to abiotic stresses. By analyzing cis-elements, we identified 63 cis-regulatory elements in the promoter regions of HanPP2C genes associated with light responsiveness, tissue-specificity, phytohormone, and stress responses. Based on RNA-seq data from two sunflower tissues (leaf and root), 47 HanPP2C genes exhibited varying expression levels in leaf tissue, while 49 HanPP2C genes showed differential expression patterns in root tissue across all stress conditions. Transcriptome profiling revealed that nine HanPP2C genes (HanPP2C12, HanPP2C36, HanPP2C38, HanPP2C47, HanPP2C48, HanPP2C53, HanPP2C54, HanPP2C59, and HanPP2C73) exhibited higher expression in leaf tissue, and five HanPP2C genes (HanPP2C13, HanPP2C47, HanPP2C48, HanPP2C54, and HanPP2C95) showed enhanced expression in root tissue in response to the four stress treatments, compared to the control conditions. These results suggest that these HanPP2C genes may be potential candidates for conferring tolerance to multiple stresses and further detailed characterization to elucidate their functions. From these candidates, 3D structures were predicted for six HanPP2C proteins (HanPP2C47, HanPP2C48, HanPP2C53, HanPP2C54, HanPP2C59, and HanPP2C73), which provided satisfactory models. Our findings provide valuable insights into the PP2C gene family in the sunflower genome, which could play a crucial role in responding to various stresses. This information can be exploited in sunflower breeding programs to develop improved cultivars with increased abiotic stress tolerance.

Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00134-4.

Overexpression of OsNF-YB4 leads to flowering early, improving photosynthesis and better grain yield in hybrid rice

For cereal crops, such as rice, the grain yield mainly comes from the accumulation of carbohydrates in the seed, which depends ultimately on photosynthesis during the growth period. To create early ripen variety, higher efficiency of photosynthesis is thus necessary to get higher grain yield with shorter growth period. In this study, flowering early was observed in the hybrid rice with overexpression of OsNF-YB4. Along with the flowering early, the hybrid rice also was shorter in plant height with less of leaves and internodes, but no changes of panicle length and leaf emergence. The grain yield was kept or even increased in the hybrid rice with shorter growth period. Transcription analysis revealed that Ghd7-Ehd1-Hd3a/RFT1 was activated early to promote the flowering transition in the overexpression hybrids. RNA-Seq study further showed that carbohydrate-related pathways were significantly altered in addition to circadian pathway. Notably, up-regulation of three pathways related to plant photosynthesis was observed, as well. Increased carbon assimilation with alteration of chlorophyll contents was subsequently detected in the following physiological experiments. All these results demonstrate that overexpression of OsNF-YB4 in the hybrid rice activates flowering early and improves photosynthesis resulting in better grain yield with shorter growth period.

MDSi: Multi-omics Database for Setaria italica

BACKGROUND

Foxtail millet (Setaria italica) harbors the small diploid genome (~ 450 Mb) and shows the high inbreeding rate and close relationship to several major foods, feed, fuel and bioenergy grasses. Previously, we created a mini foxtail millet, xiaomi, with an Arabidopsis-like life cycle. The de novo assembled genome data with high-quality and an efficient Agrobacterium-mediated genetic transformation system made xiaomi an ideal C₄ model system. The mini foxtail millet has been widely shared in the research community and as a result there is a growing need for a user-friendly portal and intuitive interface to perform exploratory analysis of the data.

RESULTS

Here, we built a Multi-omics Database for Setaria italica (MDSi, http://sky.sxau.edu.cn/MDSi.htm ), that contains xiaomi genome of 161,844 annotations, 34,436 protein-coding genes and their expression information in 29 different tissues of xiaomi (6) and JG21 (23) samples that can be showed as an Electronic Fluorescent Pictograph (xEFP) in-situ. Moreover, the whole-genome resequencing (WGS) data of 398 germplasms, including 360 foxtail millets and 38 green foxtails and the corresponding metabolic data were available in MDSi. The SNPs and Indels of these germplasms were called in advance and can be searched and compared in an interactive manner. Common tools including BLAST, GBrowse, JBrowse, map viewer, and data downloads were implemented in MDSi.

CONCLUSION

The MDSi constructed in this study integrated and visualized data from three levels of genomics, transcriptomics and metabolomics, and also provides information on the variation of hundreds of germplasm resources that can satisfies the mainstream requirements and supports the corresponding research community.

GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

Genomics and transcriptomics to protect rice (Oryza sativa. L.) from abiotic stressors: -pathways to achieving zero hunger

More over half of the world's population depends on rice as a major food crop. Rice (Oryza sativa L.) is vulnerable to abiotic challenges including drought, cold, and salinity since it grown in semi-aquatic, tropical, or subtropical settings. Abiotic stress resistance has bred into rice plants since the earliest rice cultivation techniques. Prior to the discovery of the genome, abiotic stress-related genes were identified using forward genetic methods, and abiotic stress-tolerant lines have developed using traditional breeding methods. Dynamic transcriptome expression represents the degree of gene expression in a specific cell, tissue, or organ of an individual organism at a specific point in its growth and development. Transcriptomics can reveal the expression at the entire genome level during stressful conditions from the entire transcriptional level, which can be helpful in understanding the intricate regulatory network relating to the stress tolerance and adaptability of plants. Rice (Oryza sativa L.) gene families found comparatively using the reference genome sequences of other plant species, allowing for genome-wide identification. Transcriptomics via gene expression profiling which have recently dominated by RNA-seq complements genomic techniques. The identification of numerous important qtl,s genes, promoter elements, transcription factors and miRNAs involved in rice response to abiotic stress was made possible by all of these genomic and transcriptomic techniques. The use of several genomes and transcriptome methodologies to comprehend rice (Oryza sativa, L.) ability to withstand abiotic stress have been discussed in this review.

Expression of CYP76C6 Facilitates Isoproturon Metabolism and Detoxification in Rice

Agricultural chemical residues in farmland and crops is one of the serious public issues that constantly threatens crop production, food security, and human health. Understanding their decay mechanism in crops for accelerating their degradative metabolism is important. In this study, a rice uncharacterized cytochrome P450 gene encoding CYP76C6 was functionally identified in rice exposed to isoproturon (IPU). To verify the role of CYP76C6 in rice resistance to IPU toxicity, CYP76C6 overexpression (OEs) and knockout mutant rice by CRISPR/Cas9 were generated through genetic transformation and gene-editing technologies. Assessment of growth and physiological responses revealed that the growth of OE lines was improved, the IPU-induced cellular damage was attenuated, and IPU accumulation was significantly repressed, whereas the Cas9 lines displayed a contrasting phenotype compared to the wild-type. Both relative contents of IPU metabolites and conjugates in OE lines were reduced and those in Cas9 line were increased, suggesting that CYP76C6 plays a critical role in IPU degradation. Our study unveils a new regulator, together with its mechanism for IPU decay in rice crops, which will be used in reality to reduce environmental risks in food safety and human health.

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

Genome Variation Map: a worldwide collection of genome variations across multiple species

The Genome Variation Map (GVM; http://bigd.big.ac.cn/gvm/) is a public data repository of genome variations. It aims to collect and integrate genome variations for a wide range of species, accepts submissions of different variation types from all over the world and provides free open access to all publicly available data in support of worldwide research activities. Compared with the previous version, particularly, a total of 22 species, 115 projects, 55 935 samples, 463 429 609 variants, 66 220 associations and 56 submissions (as of 7 September 2020) were newly added in the current version of GVM. In the current release, GVM houses a total of ∼960 million variants from 41 species, including 13 animals, 25 plants and 3 viruses. Moreover, it incorporates 64 819 individual genotypes and 260 393 manually curated high-quality genotype-to-phenotype associations. Since its inception, GVM has archived genomic variation data of 43 754 samples submitted by worldwide users and served >1 million data download requests. Collectively, as a core resource in the National Genomics Data Center, GVM provides valuable genome variations for a diversity of species and thus plays an important role in both functional genomics studies and molecular breeding.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.