aBIOTECH最新文献

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.

The garden pea (Pisum sativum L.) is a significant cool-season legume, serving as crucial food sources, animal feed, and industrial raw materials. The advancement of functional genomics over the past two decades has provided substantial theoretical foundations and progress to pea breeding. Notably, the release of the pea reference genome has enhanced our understanding of plant architecture, symbiotic nitrogen fixation (SNF), flowering time, floral organ development, seed development, and stress resistance. However, a considerable gap remains between pea functional genomics and molecular breeding. This review summarizes the current advancements in pea functional genomics and breeding while highlighting the future challenges in pea molecular breeding.

The CRISPR/Cas9 technology revolutionizes targeted gene knockout in diverse organisms including plants. However, screening edited alleles, particularly those with multiplex editing, from herbicide- or antibiotic-resistant transgenic plants and segregating out the Cas9 transgene represent two laborious processes. Current solutions to facilitate these processes rely on different selection markers. Here, by taking advantage of the opposite functions of a d-amino acid oxidase (DAO) in detoxifying d-serine and in metabolizing non-toxic d-valine to a cytotoxic product, we develop a DAO-based selection system that simultaneously enables the enrichment of multigene edited alleles and elimination of Cas9-containing progeny in Arabidopsis thaliana. Among five DAOs tested in Escherichia coli, the one encoded by Trigonopsis variabilis (TvDAO) could confer slightly stronger d-serine resistance than other homologs. Transgenic expression of TvDAO in Arabidopsis allowed a clear distinction between transgenic and non-transgenic plants in both d-serine-conditioned positive selection and d-valine-conditioned negative selection. As a proof of concept, we combined CRISPR-induced single-strand annealing repair of a dead TvDAO with d-serine-based positive selection to help identify transgenic plants with multiplex editing, where d-serine-resistant plants exhibited considerably higher co-editing frequencies at three endogenous target genes than those selected by hygromycin. Subsequently, d-valine-based negative selection successfully removed Cas9 and TvDAO transgenes from the survival offspring carrying inherited mutations. Collectively, this work provides a novel strategy to ease CRISPR mutant identification and Cas9 transgene elimination using a single selection marker, which promises more efficient and simplified multiplex CRISPR editing in plants.

Some 20 years ago, the EU introduced complex regulatory rules for the growth of transgenic crops, which resulted in a de facto ban to grow these plants in fields within most European countries. With the rise of novel genome editing technologies, it has become possible to improve crops genetically in a directed way without the need for incorporation of foreign genes. Unfortunately, in 2018, the European Court of Justice ruled that such gene-edited plants are to be regulated like transgenic plants. Since then, European scientists and breeders have challenged this decision and requested a revision of this outdated law. Finally, after 5 years, the European Commission has now published a proposal on how, in the future, to regulate crops produced by new breeding technologies. The proposal tries to find a balance between the different interest groups in Europe. On one side, genetically modified plants, which cannot be discerned from their natural counterparts, will exclusively be used for food and feed and are—besides a registration step—not to be regulated at all. On the other side, plants expressing herbicide resistance are to be excluded from this regulation, a concession to the strong environmental associations and NGOs in Europe. Moreover, edited crops are to be excluded from organic farming to protect the business interests of the strong organic sector in Europe. Nevertheless, if this law passes European parliament and council, unchanged, it will present a big step forward toward establishing a more sustainable European agricultural system. Thus, it might soon be possible to develop and grow crops that are more adapted to global warming and whose cultivation will require lower amounts of pesticides. However, there is still a long way to go until the law is passed. Too often, the storm of arguments raised by the opponents, based on irrational fears of mutations and a naive understanding of nature, has fallen on fruitful ground in Europe.

Di- and tri-methylation of lysine 36 on histone H3 (H3K36me2/3) is catalysed by histone methyltransferase Set2, which plays an essential role in transcriptional regulation. Although there is a single H3K36 methyltransferase in yeast and higher eukaryotes, two H3K36 methyltransferases, Ash1 and Set2, were present in many filamentous fungi. However, their roles in H3K36 methylation and transcriptional regulation remained unclear. Combined with methods of RNA-seq and ChIP-seq, we revealed that both Ash1 and Set2 are redundantly required for the full H3K36me2/3 activity in Magnaporthe oryzae, which causes the devastating worldwide rice blast disease. Ash1 and Set2 distinguish genomic H3K36me2/3-marked regions and are differentially associated with repressed and activated transcription, respectively. Furthermore, Ash1-catalysed H3K36me2 was co-localized with H3K27me3 at the chromatin, and Ash1 was required for the enrichment and transcriptional silencing of H3K27me3-occupied genes. With the different roles of Ash1 and Set2, in H3K36me2/3 enrichment and transcriptional regulation on the stress-responsive genes, they differentially respond to various stresses in M. oryzae. Overall, we reveal a novel mechanism by which two H3K36 methyltransferases catalyze H3K36me2/3 that differentially associate with transcriptional activities and contribute to enrichment of facultative heterochromatin in eukaryotes.

With the increasing number of sequenced species, phylogenetic profiling (PP) has become a powerful method to predict functional genes based on co-evolutionary information. However, its potential in plant genomics has not yet been fully explored. In this context, we combined the power of machine learning and PP to identify salt stress-related genes in a halophytic grass, Spartina alterniflora, using evolutionary information generated from 365 plant species. Our results showed that the genes highly co-evolved with known salt stress-related genes are enriched in biological processes of ion transport, detoxification and metabolic pathways. For ion transport, five identified genes coding two sodium and three potassium transporters were validated to be able to uptake Na⁺. In addition, we identified two orthologs of trichome-related AtR3-MYB genes, SaCPC1 and SaCPC2, which may be involved in salinity responses. Genes co-evolved with SaCPCs were enriched in functions related to the circadian rhythm and abiotic stress responses. Overall, this work demonstrates the feasibility of mining salt stress-related genes using evolutionary information, highlighting the potential of PP as a valuable tool for plant functional genomics.

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.