aBIOTECH最新文献

Innovations in DNA sequencing technologies have greatly boosted population-level genomic studies in plants, facilitating the identification of key genetic variations for investigating population diversity and accelerating the molecular breeding of crops. Conventional methods for genomic analysis typically rely on small variants, such as SNPs and indels, and use single linear reference genomes, which introduces biases and reduces performance in highly divergent genomic regions. By integrating the population level of sequences, pangenomes, particularly graph pangenomes, offer a promising solution to these challenges. To date, numerous algorithms have been developed for constructing pangenome graphs, aligning reads to these graphs, and performing variant genotyping based on these graphs. As demonstrated in various plant pangenomic studies, these advancements allow for the detection of previously hidden variants, especially structural variants, thereby enhancing applications such as genetic mapping of agronomically important genes. However, noteworthy challenges remain to be overcome in applying pangenome graph approaches to plants. Addressing these issues will require the development of more sophisticated algorithms tailored specifically to plants. Such improvements will contribute to the scalability of this approach, facilitating the production of super-pangenomes, in which hundreds or even thousands of de novo–assembled genomes from one species or genus can be integrated. This, in turn, will promote broader pan-omic studies, further advancing our understanding of genetic diversity and driving innovations in crop breeding.

Symbiotic nitrogen fixation is predominantly observed in legumes, which form specialized structures termed nodules on their roots that contain symbiotic rhizobia. This mutualistic association provides reciprocal benefits: rhizobia convert atmospheric nitrogen into bioavailable forms, supplying essential nitrogen to their host plants, while obtaining reduced carbon in return. The increasing reliance on nitrogen fertilizers to satisfy escalating demands for food has prompted various approaches aimed at unravelling the mechanisms underlying symbiotic nodulation, seeking to transfer this capacity to non-nodulating crops. Transcriptome-based analyses have revealed that nodulation is a complex developmental program involving many genes. To comprehensively investigate this phenomenon, multiple omics technologies have been deployed and integrated, yielding exciting breakthroughs. In this review, we outline how omics have accelerated research in this area and discuss how advancements in technologies, such as artificial intelligence, could further deepen our understanding of nodulation.

Bioinformatics analysis often requires the filtering of multi-datasets, based on frequency or frequency of occurrence, for decisions on retention or deletion. Existing tools for this purpose often present a challenge with complex installation, which necessitate custom coding, thereby impeding efficient data processing activities. To address this issue, Filterx, a user-friendly command line tool that written in C language, was developed that supports multi-condition filtering, based on frequency or occurrence. This tool enables users to complete the data processing tasks through a simple command line, greatly reducing both workload and data processing time. In addition, future development of this tool could facilitate its integration into various bioinformatics data analysis pipelines.

In environmental biosafety assessments of glyphosate-tolerant crops, it is essential to evaluate the effects of cultivating these crops and applying glyphosate on the microbial community in the rhizosphere soil, which play a critical role in maintaining soil health, plant growth, and crop productivity. Maize (Zea mays) line GG2 was previously generated by transforming wild-type maize with the gat and gr79-epsps genes, endowing GG2 with both active and passive resistance to glyphosate. However, the ecological risk of introducing these two new glyphosate-tolerance genes into maize, as well as glyphosate treatment, to rhizosphere microorganisms remain unclear. In this study, we used high-throughput sequencing to analyze the diversity and composition of the bacterial and fungal communities in the rhizosphere soil around biotech maize GG2, with (GG2-H) and without glyphosate treatment (GG2-N), compared with the near-isogenic, non-biotech maize line ZD958 at seven stages of growth. The structure and diversity of the bacterial and fungal communities of GG2-H were similar to those of ZD958, whereas glyphosate treatment had temporary effects on bacterial and fungal diversity and richness. The differences in the bacterial and fungal communities were associated with changes in soil properties such as pH, available phosphorus and organic matter, and seasonal changes. These factors, rather than maize lines, made the greatest contributions to the shifts in bacterial and fungal community structure. This study provides a comprehensive analysis of the effects of biotech crop cultivation, glyphosate treatment, soil physicochemical properties of soil, and maize growth stages on soil microbial communities, offering valuable insights for the large-scale adoption of biotech crops in China.

The key factors for genome-editing in plants using CRISPR/Cas9, such as the Cas9 nuclease and guide RNA (gRNA) are typically expressed from a construct that is integrated into the plant genome. However, the presence of foreign DNA in the host genome causes genetic and regulatory concerns, particularly for commercialization. To address this issue, we developed an accelerated pipeline for generating transgene-free genome-edited sorghum (Sorghum bicolor) in the T₀ generation. For proof-of-concept, we selected the Phytoene desaturase (PDS) gene as the target due to its visible phenotype (albinism) upon mutation. Following microprojectile-mediated co-transformation with a maize (Zea mays)-optimized Cas9 vector and a guide RNA (gRNA) cassette with a geneticin (G418) resistance gene, we divided tissue derived from immature embryos into two groups (with and without antibiotic selection) and cultured them separately as parallel experiments. In regenerated plants cultured on medium containing MS basal nutrition (to allow albino plants to survive), we detected higher rates of albinism in the non-selection group, achieving editing rates of 11.1–14.3% compared with 4.2–8.3% in the antibiotic selection group. In the T₀ generation, 22.2–38.1% of albino plants from the non-selection group were identified as transgene-free, whereas only 0–5.9% from the selection group were transgene-free. Therefore, our strategy efficiently produced transgene-free genome-edited plants without the need for self-crossing or outcrossing, demonstrating the feasibility of achieving transgene-free genome-edited sorghum plants within a single generation. These findings pave the way for commercializing transgene-free genome-edited lines, particularly for vegetatively propagated crops like pineapple, sugarcane, and banana.

Iron (Fe) homeostasis in plant cells is crucial for crop productivity and quality. An intricate transcriptional network involving numerous basic Helix-Loop-Helix (bHLH) transcription factors has been proposed to control Fe homeostasis. In the present study, we characterized rice (Oryza sativa) OsbHLH062, a member of the IVb subgroup of the bHLH family, demonstrating that it negatively regulates Fe-deficiency responses. OsbHLH062 represses transcription by recruiting TOPLESS/TOPLESS-RELATED co-repressors (TPL/TPRs) through its ethylene-responsive element binding factor-associated amphiphilic repression (EAR) motif. Under Fe deficiency, the expression of OsbHLH062 is upregulated in roots and downregulated in shoots. Overexpression of OsbHLH062 leads to decreased Fe accumulation in the shoot. Furthermore, OsbHLH062 interacts with POSITIVE REGULATOR OF IRON HOMEOSTASIS 1 (OsPRI1) and inhibits its transactivation activity, thereby negatively regulating the expression of many Fe homeostasis-related genes. These results indicate an important role for OsbHLH062 in regulating Fe homeostasis by negatively regulating Fe deficiency responses in rice. This knowledge will aid in the design of Fe-biofortified rice plants that can help to address the global issue of Fe deficiency.

Brassinosteroids (BRs), a class of plant-specific steroidal hormones, play crucial roles in regulating various plant physiological functions, such as growth, development, and adaptability to the environment. Despite this broader role of BRs, previously published reviews mainly focused on the molecular mechanisms of BR-mediated regulation of vegetative and reproductive growth of model plants like Arabidopsis and some food crops, such as rice, maize, and wheat. While horticultural plants hold significant economic importance in modern agriculture, less attention has been paid to understanding the role of BRs in regulating the physiological functions of these plants. Given the lack of relevant reviews, this article aims to discuss the major roles of BRs in horticultural plants, particularly fruit and leaf development, whole plant architecture, and adaptive stress response. We also highlight key challenges and provide some future research directions for genetically improving horticultural plants by altering the BR signaling pathway.

Single-cell RNA sequencing (scRNA-seq) technology enables a deep understanding of cellular differentiation during plant development and reveals heterogeneity among the cells of a given tissue. However, the computational characterization of such cellular heterogeneity is complicated by the high dimensionality, sparsity, and biological noise inherent to the raw data. Here, we introduce PhytoCluster, an unsupervised deep learning algorithm, to cluster scRNA-seq data by extracting latent features. We benchmarked PhytoCluster against four simulated datasets and five real scRNA-seq datasets with varying protocols and data quality levels. A comprehensive evaluation indicated that PhytoCluster outperforms other methods in clustering accuracy, noise removal, and signal retention. Additionally, we evaluated the performance of the latent features extracted by PhytoCluster across four machine learning models. The computational results highlight the ability of PhytoCluster to extract meaningful information from plant scRNA-seq data, with machine learning models achieving accuracy comparable to that of raw features. We believe that PhytoCluster will be a valuable tool for disentangling complex cellular heterogeneity based on scRNA-seq data.

Potato common scab (CS) is a worldwide disease, caused by Streptomyces spp., and its presence reduces the market value of potatoes. A nontoxic and potentially effective approach in many control strategies is the use of antagonistic microbes as biocontrol agents. In this study, Bacillus atrophaeus DX­9 was isolated and assessed for its ability to protect against CS. Through integrated metagenomic and metabolomic analyses, changes in the soil microbial community structure and soil properties were analyzed to understand the effects of Bacillus atrophaeus DX­9 on CS. These studies revealed that DX­9 inoculation could significantly decrease CS disease rate, disease index, and the number of CS pathogens, along with an increase in soil N and P content. Our metagenomic assays identified 102 phyla and 1154 genera, and DX­9 inoculation increased the relative abundances of the phyla Pseudomonadota, Chloroflexota and Gemmatimonadota. Additionally, an increase in the relative abundance of genera, such as Bradyrhizobium, Agrobacterium, and Nitrobacter, were significantly and positively correlated with soil N and P. Metabolomic analysis revealed that DX­9 inoculation significantly increased the soil levels of phytolaccoside A, 7,8­dihydropteroic acid, novobiocin, and azafrin. These compounds were enriched in microbe pathway metabolites, including xenobiotic biodegradation and metabolism, biosynthesis of other secondary metabolites, and metabolism of cofactors and vitamins. In summary, the use of Bacillus atrophaeus DX­9 against potato CS offers an alternative biocontrol method that can improve both soil microbial community and properties. This study provides insight into the potential mechanisms by which microbial inoculants can control CS disease.

The accurate chromatin states are essential for maintaining genome integrity and ensuring the normal transcription of genes. Polycomb group (PcG) proteins regulate chromatin states not only by modifying the chromatin, but also by influencing the chromatin three-dimensional (3D) structure. The core components of Polycomb repressive complex 1 (PRC1), B LYMPHOMA MOLONEY MURINE LEUKEMIA VIRUS INSERTION REGION 1 HOMOLOG 1A/B/C (BMI1s), have been reported to maintain the compartment domains (CDs) generally, but the mechanism by which they function remains elusive. Here, we reveal that condensin complexes, whose function are related to chromatin or chromosome, can interact with BMI1s. Removal of condensin I or II also leads to global impairment of CDs. The significantly impaired CDs in bmi1a/b/c and condensin mutants are basically the same and the CDs co-regulated by BMI1s and condensin complexes have higher strength in the wild-type (WT, Col-0) plant, indicating that BMI1s and condensin complexes cooperate to maintain CDs. This regulatory function is parallel to the function of histone modifications deposited by PcG in maintaining CDs, since removal of either condensin I or II does not obviously disrupt the genome-wide level of H3K27me3 and H2AK121ub. Moreover, we discovered that BMI1s and condensin complexes jointly influence the expression of a portion of genes to enable normal plant growth and may maintain the genome integrity under stress conditions. Thus, our work proides a perspective for the gene expression and epigenetic regulatory mechanism of PRC1, in Arabidopsis, in addition to histone modifications.