Plants use light both as a resource for photosynthesis and as a signal about their environment. In response to light cues, plants can move their organs via directional growth driven by cell expansion. In dense vegetation where light is available in spatially heterogeneous patterns, plants need to navigate this space to improve the position of their photosynthetic tissues. In canopies blue light irradiance and red to far-red light ratio decrease due to absorption by chloroplasts, and these changes regulate distinct processes within the plant. Changes in light environment are detected by cryptochrome and phytochrome photoreceptors, both regulating Phytochrome Interacting Factors (PIFs) and thereby enhancing elongation in hypocotyls, stems and leaves, and inducing upward leaf movement (hyponasty). An additional class of photoreceptors, phototropins, decode horizontal light gradients to produce directional growth towards the light source (phototropism). Here we review the current state of knowledge on these differential growth responses to light cues, with specific emphasis on the regulatory pathways that translate light signaling in differential cell expansion. Downstream of the photoreceptors, the phytohormone auxin induces cell growth in shoot tissues, but also other phytohormones contribute to balancing light responses. Cell expansion is regulated primarily at the level of cell walls and a comparison of different transcriptome datasets reveals that only a small group of cell wall modifying genes are tightly regulated by shade cues. It remains poorly understood which cell layers are causal to the initiation of cellular expansion. Here we combine insights from different differential growth behaviors in different species and organs to generate different hypotheses for the cellular underpinnings of light-driven leaf movements.

Spatially regulated membrane constriction is an important milestone in reconstituting minimal cell division. In giant lipid vesicles, bottom-up approaches have reproduced the assembly, mid-cell positioning, and the initial constriction of an FtsZ-based minimal divisome. However, progressive deformation towards giant vesicle scission by near-equatorial Z rings could so far never be observed. One obvious major limitation has been the scale mismatch, as pure reconstituted FtsZ rings typically exhibit bacterial diameters, too small to constrict typical cell-sized vesicles. Therefore, we explore the potential of other key divisome factors to scale up FtsZ-ring functionality in vitro to match the dimensions required for synthetic cell division. We here focus on cytoFtsN, the cytosolic domain of FtsN, and its effect on FtsZ self-organization. Remarkably, a molar excess of cytoFtsN promotes the formation of large, closed equatorial FtsZ rings on giant vesicle membranes, which are able to constrict to almost full closure. By fluorescence imaging and biochemical analysis, we show that cytoFtsN regulates the spatial organization of the FtsZ network primarily by aligning FtsZ filaments while reducing filament depolymerization. Our findings help to define key requirements in a minimal filament-based system for progressive membrane constriction and thus represent a major step forward towards constructing synthetic cells capable of self-division. Spatially regulated membrane constriction is an important milestone in reconstituting minimal cell division. Here the authors engineer a truncated system where the cytosolic domain of FtsN forms large, closed equatorial FtsZ rings on giant vesicle membranes and regulate the FtsZ network formation.

Bio-pesticides constitute a category of natural products and organisms effective against pests. Given their advantages, such as easier clearance and degradation compared to chemical pesticides, along with their lack of detrimental effects on ecosystem health, they represent a promising alternative for sustainable pest management and crop protection. The development of natural biopesticides to substitute synthetic pesticides is therefore of paramount importance for securing food safety and advancing agricultural production. Monoterpenes, primarily present in plant volatile oils, are characterized by a strong aroma and a range of biological activities-including insecticidal, antimicrobial, antiviral, anti-inflammatory, antioxidant, antitumor, and anticancer effects. This profile renders them a promising platform for the research and development of new, more effective pesticides. This article reviews the biosynthetic pathways of monoterpenes and contemporary engineering strategies for their microbial synthesis. Additionally, an evolutionary analysis database for monoterpene synthases (MTPS) has been established. Finally, the biological activities of monoterpenes are summarized and analyzed, with particular emphasis on their potential and advantages as biopesticides, thereby providing direction for the future development of monoterpene-based biopesticides.

The advent of spatial and quantitative biology has led to immense advances in understanding the complex inner workings of plants, down to the molecular scale. Functional imaging of live plants, which enables the spatial and quantitative mapping of biochemical cues, physicochemical properties of cellular structures, and the dynamics of physical and chemical signals with unprecedented resolution, has become a key technology for advancing the mechanistic understanding of plant cell biology. In this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes, which enable plant functional imaging without requiring genetic manipulation of the study object. We explain how probes sense, target, and report on functional features within the plant cell; discuss their limitations, including toxicity; and provide case studies to exemplify how these tools can complement biological studies to unravel the complex machinery that makes plants work. We conclude by outlining the expected future development of this field and identifying key challenges that lie ahead.

Marine viruses impact biogeochemical cycles through cell lysis, releasing organic matter and nutrients that fuel ocean productivity. Identifying and quantifying the specific viruses active in these processes remains a priority in the field. Here, we introduce a click-chemistry method to fluorescently label, sort, and sequence the genomes of newly produced viral particles (viral progeny) released from transcriptionally active host microbial cells, alongside the analysis of co-occurring inactive cells and pre-existing viruses in environmental samples. This approach, called viral BONCAT-FACS, combines biorthogonal non-canonical amino acid tagging (BONCAT) with environmental sample incubation, followed by single-virus and single-cell sorting by flow cytometry (FACS). Genomic analysis of translationally-active cells and new viral progeny in coastal seawater incubations confirmed BONCAT labelling and successful sorting of diverse marine bacteria, microeukaryotic cells, and virioplankton, with stark differences in the predicted turnover of specific groups of infecting viruses, including Pelagiphages, Methylophages, a Flavobacteriales-associated novel “Far-T4” clade, non-canonical DNA viruses of Naomiviridae using dU instead of dT, algae-infecting giant NCLDV viruses, and parasitic virophages. Sequenced BONCAT-active cells showed a strong enrichment in viral contigs relative to the inactive cell fraction, suggestive of a large proportion of translationally-active virocells. This study illustrates the effectiveness of viral BONCAT-FACS for uncovering genome-resolved virus-host dynamics. By providing a direct approach for tracking active viral infections in natural environments, this method enhances our ability to investigate behavior and interactions of these nanoscale predators, expanding our understanding of their role in ecosystem dynamics.

Whole-genome sequencing (WGS) data are an invaluable resource for understanding antimicrobial resistance (AMR) mechanisms. However, WGS data are high-dimensional and the lack of standardized genomic representations is a key barrier to AMR phenotype prediction. To fully explore these high-resolution data, we propose AMR-GNN, a graph deep learning-based framework that integrates multiple genomic representations with graph neural networks (GNN) to enable AMR phenotype prediction from genomic sequence data. We test AMR-GNN with Pseudomonas aeruginosa, a clinically relevant Gram-negative bacterial pathogen known for its complex AMR mechanisms. We present AMR-GNN as a proof-of-concept framework designed to address several key problems in AMR phenotype prediction with data-driven machine learning (ML) approaches, including using multiple genomic representations to enhance performance, to mitigate the influence of clonal relationships and to identify informative biomarkers to provide explainability. Follow-up validation on the largest publicly available dataset spanning both Gram-negative and Gram-positive pathogens highlights AMR-GNN’s broad applicability in detecting AMR in diverse and clinically relevant pathogen-drug combinations. Predicting antimicrobial resistance from bacterial genomic data is challenging. Here, the authors introduce AMR-GNN, a graph neural network that integrates multiple genomic representations to improve prediction, reduce clonal bias, and identify biomarkers.

Third-generation long-read sequencing technologies, significantly improve metagenome assemblies. Highly accurate PacBio HiFi reads can yield hundreds of near-complete metagenome-assembled genomes (MAGs) from a single sample. Recently, the accuracy of the more cost-effective Oxford Nanopore Technologies (ONT) platform has increased to a per-base error rate of 1-2%. However, current metagenome assemblers are optimized for HiFi and do not scale to the large data sets that ONT enables. We present nanoMDBG, an evolution of metaMDBG, which supports the latest ONT reads through an error correction pre-processing step in minimizer-space. Across a range of ONT datasets, including a large 400 Gbp soil sample, nanoMDBG reconstructs up to twice as many high-quality MAGs as the next best ONT assembler, metaFlye, while requiring a third of the CPU time and memory. Critically, the latest ONT technology can now produce comparable MAG construction results as those obtained using PacBio HiFi at the same sequencing depth. Benoit et al. present nanoMDBG, a scalable metagenome assembler for the latest ONT long reads, developed by adding error correction to metaMDBG. They demonstrate superior performance to the state-of-the-art and equivalent results to PacBio HiFi reads.

Plant hormones are essential small molecules that regulate plant growth, development, and systemic responses to environmental stimuli. These processes are mediated by complex signaling networks involving structurally diverse receptors, regulatory proteins, and dynamic protein–protein interactions. Advances in structural and functional biology over the past two decades have revealed how hormone receptors recognize their ligands and how they mediate responses from perception to signaling through transduction pathways and feedback regulation. In this review, we summarize the current knowledge of plant hormone receptors with experimentally determined structures and highlight their central roles in shaping plant biology. Finally, we discuss outstanding questions in the field and how emerging computational tools may help address these gaps.

Soils are the primary environmental reservoir of plant pathogens impacting food production and ecosystem productivity worldwide. Yet, some soils can also suppress pathogens through environmental and microbial regulation. Here we integrate 1602 soil metagenomes from 59 countries with a greenhouse experiment to identify 32 dominant pathogens, including Ralstonia solanacearum, Clavibacter michiganensis, and Streptomyces europaeiscabiei. Pathogen hotspots occur primarily in warm ecosystems and agricultural soils, whereas higher soil microbial diversity, increased soil organic carbon and colder climatic conditions are associated with lower pathogen prevalence. Non-pathogenic Streptomyces spp., arbuscular mycorrhizal fungi AMF, and biosynthetic gene clusters BGC encoding terpenes and polyketides are associated with reduced pathogen prevalence. Predictive modelling suggests that several dominant bacterial pathogens are likely to increase in prevalence under future climate scenarios, particularly in tropical and subtropical regions. By identifying global drivers of dominant pathogens and their suppression, this study provides a foundation for improved surveillance and management of plant disease risks under climate change. A global study of 1602 soil samples identifies dominant bacterial plant pathogens and reveals microbial traits, soil carbon and climate that promote natural suppression, while climate change may increase disease risks in many regional hotspots.