Halorhodospira (Hlr.) halochloris is a triply extremophilic phototrophic purple sulfur bacterium, as it is thermophilic, alkaliphilic, and extremely halophilic. The light-harvesting-reaction center (LH1-RC) core complex of this bacterium displays an LH1-Q_y transition at 1,016 nm, which is the lowest-energy wavelength absorption among all known phototrophs. Here we report the cryo-EM structure of the LH1-RC at 2.42 Å resolution. The LH1 complex forms a tricyclic ring structure composed of 16 αβγ-polypeptides and one αβ-heterodimer around the RC. From the cryo-EM density map, two previously unrecognized integral membrane proteins, referred to as protein G and protein Q, were identified. Both of these proteins are single transmembrane-spanning helices located between the LH1 ring and the RC L- subunit and are absent from the LH1-RC complexes of all other purple bacteria of which the structures have been determined so far. Besides bacteriochlorophyll b molecules (B1020) located on the periplasmic side of the Hlr. halochloris membrane, there are also two arrays of bacteriochlorophyll b molecules (B800 and B820) located on the cytoplasmic side. Only a single copy of a carotenoid (lycopene) was resolved in the Hlr. halochloris LH1-α3β3 and this was positioned within the complex. The potential quinone channel should be the space between the LH1-α3β3 that accommodates the single lycopene but does not contain a γ-polypeptide, B800 and B820. Our results provide a structural explanation for the unusual Q_y red shift and carotenoid absorption in the Hlr. halochloris spectrum and reveal new insights into photosynthetic mechanisms employed by a species that thrives under the harshest conditions of any phototrophic microorganism known.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

Carbon assimilation is a crucial part of the photosynthetic process, wherein inorganic carbon, typically in the form of CO₂, is converted into organic compounds by living organisms, including plants, algae, and a subset of bacteria. Although several carbon fixation pathways have been elucidated, the Calvin-Benson-Bassham (CBB) cycle remains fundamental to carbon metabolism, playing a pivotal role in the biosynthesis of starch and sucrose in plants, algae, and cyanobacteria. However, Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), the key carboxylase enzyme of the CBB cycle, exhibits low kinetic efficiency, low substrate specificity, and high temperature sensitivity, all of which have the potential to limit flux through this pathway. Consequently, RuBisCO needs to be present at very high concentrations, which is one of the factors contributing to its status as the most prevalent protein on Earth. Numerous attempts have been made to optimize the catalytic efficiency of RuBisCO and thereby promote plant growth. Furthermore, the limitations of this process highlight the potential benefits of engineering or discovering more efficient carbon fixation mechanisms, either by improving RuBisCO itself or by introducing alternative pathways. Here, we review advances in artificial carbon assimilation engineering, including the integration of synthetic biology, genetic engineering, metabolic pathway optimization, and artificial intelligence in order to create plants capable of performing more efficient photosynthesis. We additionally provide a perspective of current challenges and potential solutions alongside a personal opinion of the most promising future directions of this emerging field.

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein–protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

The modern cultivated tomato (Solanum lycopersicum) was domesticated from Solanum pimpinellifolium native to the Andes Mountains of South America through a “two-step domestication” process. It was introduced to Europe in the 16th century and later widely cultivated worldwide. Since the late 19th century, breeders, guided by modern genetics, breeding science, and statistical theory, have improved tomatoes into an important fruit and vegetable crop that serves both fresh consumption and processing needs, satisfying diverse consumer demands. Over the past three decades, advancements in modern crop molecular breeding technologies, represented by molecular marker technology, genome sequencing, and genome editing, have significantly transformed tomato breeding paradigms. This article reviews the research progress in the field of tomato molecular breeding, encompassing genome sequencing of germplasm resources, the identification of functional genes for agronomic traits, and the development of key molecular breeding technologies. Based on these advancements, we also discuss the major challenges and perspectives in this field.

Hidden hunger, caused by chronic micronutrient deficiencies, affects billions of people worldwide and remains a critical public health issue despite progress in food production. Biofortification offers a promising solution by enhancing nutrient levels within plant tissues through traditional breeding or advanced biotechnologies. Recent advancements in plant synthetic biology have significantly improved biofortification strategies, enabling precise and targeted nutrient enrichment. This mini-review outlines five core strategies in synthetic biology-based biofortification: overexpression of endogenous biosynthetic genes, introduction of heterologous biosynthetic pathways, expression of nutrient-specific transporters, optimization of transcriptional regulation, and protein (directed) evolution. Vitamin B₁ biofortification serves as a primary illustrative example due to its historical importance and ongoing relevance. Recent breakthroughs, particularly from Chinese research teams, are also highlighted. Together, these strategies offer transformative potential for addressing global nutritional challenges through precise, sustainable and innovative plant-based approaches.

Transcription factors (TFs) function as master regulators in multiple signaling pathways and govern diverse developmental and adaptive processes in plants. Some TFs identified in crop plants play critical roles in regulating yield through changes in plant architecture, including roots, stems, leaves, flowers, fruits, and grains. Although altering crop architecture can increase yields, the extent of yield enhancement is frequently hampered by diseases. Developing new crop varieties with improved yields and enhanced disease resistance remains challenging because immune system activation often impairs plant growth. Recently, approaches using TF engineering have made substantial progress in elevating both growth performance and disease resistance. However, most of these techniques rely on traditional transgenic methods. This review highlights discoveries in the last decade demonstrating improvements in growth performance, yield and immunity through TF engineering. We focus mainly on changes in plant architecture related to improved yield and disease resistance. We conclude with perspectives on the potential application of these discoveries for generating desirable crop traits by merging the most noteworthy biotechnology approaches, such as clustered regularly interspaced small palindromic repeats (CRISPR)/CRISPR-associated protein 9-mediated genome editing, with canonical molecular biology.