Plant Communications

The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.

Although the biosynthetic pathways of benzylisoquinoline alkaloids (BIAs) have been extensively investigated in several plant species, their transcriptional regulatory mechanisms remain only partially understood. Jasmonate (JA)-responsive group IX APETALA2/Ethylene Responsive Factor (AP2/ERF) transcription factors (TFs) are well-known regulators of specialized plant metabolism, including the biosynthesis of various alkaloids. However, their specific roles in BIA biosynthesis remain largely elusive. Here, we isolated five novel group IX AP2/ERF TFs, designated Benzylisoquinoline alkaloid Jasmonate-responsive AP2/ERF (BJE1-5), from Coptis japonica. Phylogenetic analysis revealed that Benzylisoquinoline alkaloid Jasmonate-responsive AP2/ERF (BJE) proteins belong to subclades distinct from group IXa, which contains well-known AP2/ERF TFs involved in alkaloid biosynthesis. Transient expression analyses in C. japonica protoplasts demonstrated that certain BJEs, particularly CjBJE3 and CjBJE5, positively regulated BIA biosynthetic genes through a mutual regulatory network among BJE members. Moreover, CjBJE3 expression was regulated by CjbHLH1, a unique-type basic helix-loop-helix (bHLH) TF specific to BIA-producing plants. Furthermore, heterologous expression of CjBJE3 and CjBJE5 in cultured Eschscholzia californica cells significantly enhanced the overall BIA production, particularly by increasing end-product benzophenanthridine BIAs, highlighting several uncharacterized biosynthetic genes clustered in the genome. Our findings suggest that BIA-producing species have developed a specific regulatory network comprised of CjbHLH1 and BJE TFs, providing valuable clues for identifying novel biosynthetic enzymes.

Aegilops peregrina is a wild allotetraploid wheat wild relative and an important source of genetic diversity for stress tolerance and agronomic traits. Here, we report a subgenome-resolved, chromosome-scale reference genome assembly of a drought tolerant and stem rust resistant Ae. peregrina accession PI 604178 generated using PacBio HiFi and Hi-C sequencing. The 10.13 Gb assembly contains 98.81% of sequence anchored to 14 pseudomolecules representing the seven S and seven U chromosomes, with contig and scaffold N50 values of 25.84 and 746.48 Mb, respectively. The assembly achieved a consensus quality value of 74.61, 97.83% k-mers completeness, and 99.9% BUSCO completeness. LTR Assembly Index values of 20.43 and 18.79 for the S and U subgenomes, respectively, further supported high continuity across repeat-rich regions. Repetitive elements comprise 85.93% of chromosome-anchored assembly. We annotated 59,910 high-confidence protein-coding genes, with comparable gene representation across the two subgenomes. This reference genome provides a high-quality genomic framework for comparative analyses, characterization of important loci regulating agronomic and resilience related traits, and sequence-guided exploitation of Ae. peregrina allelic diversity for wheat improvement.

MicroRNAs (miRNAs) are key post-transcriptional regulators of development, stress response, and secondary cell wall formation in woody plants, yet annotations for Eucalyptus grandis, the world's most widely planted hardwood, remain fragmented across studies using incompatible discovery pipelines and filtering criteria. Here we present the Eucalyptus MicroRNA Archive (EMA), a curated, locus-resolved database integrating three independent small RNA sequencing datasets spanning vegetative tissue, somatic embryogenesis, and mechanically induced tension wood formation. Applying annotation criteria aligned with current plant miRNA standards, EMA catalogs 99 curated miRNAs (31 previously described, 68 novel) organized into 34 family-level groupings under a three-tier confidence system, known-reference-supported, multi-study replicated, or single-study, that preserves study-of-origin and sample-level evidence for every entry. Cross-study comparison showed that only 9 of 99 entries (9.1%) were independently supported by all three datasets, supporting an evidence-tiered rather than binary annotation scheme. Target prediction against the E. grandis transcriptome yielded 1,773 miRNA-target interactions spanning 764 loci, integrated into a combined miRNA-target and protein-protein interaction network. This network resolved into functionally coherent, mutually isolated clusters, including an miR482-associated NBS-LRR/TIR disease-resistance hub with a substantial translational-repression component, alongside modules enriched for ribosome biogenesis and translation, DNA replication, and nitrogen and carbohydrate metabolism. EMA is publicly accessible through an interactive web dashboard, with all curated data, source code, and analysis scripts openly available, providing a reproducible, extensible framework for E. grandis miRNA research and a template for similarly structured resources in other non-model woody species.

Blumeria graminis f. sp. avenae (Bga), the causal agent of oat powdery mildew, is one of the most host-specialized members of the B. graminis species complex. Despite its agricultural importance, the lack of a high-quality reference genome has limited studies of host specialization, virulence evolution and comparative genomics in this pathogen. Here, we generated the first chromosome-scale genome assembly of Bga using an integrative approach combining long- and short-read sequencing, Hi-C scaffolding and transcriptome data. The Bga genome exhibits hallmark features of powdery mildew fungi, including extensive repeat content and low gene density. Comparative analyses revealed that genome expansion is primarily associated with historical transposable element proliferation rather than recent transpositional activity. Genome organization is consistent with a functionally stratified "one-speed" model, in which genes associated with pathogenicity, including predicted effectors and infection-responsive genes, are preferentially located in transposable element-rich regions characterized by reduced synteny conservation and extended intergenic spaces. In contrast, conserved genes are concentrated in compact genomic regions and maintain strong syntenic conservation across cereal-infecting formae speciales. Hi-C analyses demonstrated a highly structured chromatin architecture and revealed genome organization patterns associated with infection-related gene expression. Comparative genomic analyses indicated that host specialization in Bga is driven by localized diversification of a relatively small subset of genes rather than large-scale genome restructuring. These results provide the first high-quality genomic resource for Bga and offer new insights into the evolutionary mechanisms underlying host specialization in powdery mildew fungi.

Oil palm (Elaeis) is the primary source of global vegetable oil. Interspecific hybrids of Elaeis exhibit pronounced heterosis by integrating two distinct subgenomes into a single nucleus, effectively combining the high yield of African oil palm (E. guineensis) with the high unsaturated fatty acid content and disease resistance of American oil palm (E. oleifera). However, the genetic basis underlying heterosis is still unclear. Here, we combine phased genome assembly, comparative genomics, evolutionary genomics and haplotype-aware transcriptomics to unravel the genetic architecture of heterosis of hybrid oil palm. We assemble the highly heterozygous F1 genome ('Reyou 40', 3.75% heterozygosity) into a complete 1.73 Gb T2T haplotype (HapG) and a 1.84 Gb near-T2T haplotype (HapO with17 gaps). Despite 91.56% sequence identity, HapG and HapO diverged in LTR-RT occurrence and PAV affected genes, showing complementary biases in lipid metabolism and stress responses, respectively. Evolutionary genomics revealed that ancient WGDs preserved the palm family. Whereas lineage-specific lipid-related gene expansions in oil palm. Six ancient introgressed regions (~64 Mb) in HapG were reshaped by transposable elements and tandem duplication, showing an enrichment of genes related to resistance and lipid metabolism. Transcriptomically, 82.2% of allelic gene pairs maintained balanced expression, accompanied by parental functional complementarity and dosage buffering, revealing a potential regulatory basis for coordinating parental genetic differences in the hybrid genome. These haplotype-resolved genomic resources offer vital targets for understanding heterosis and accelerating oil palm molecular breeding.

Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using inexpensive, scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic selection and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza(R)), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance and indices, and cycle-specific hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance and seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little apparent loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Early-life stage leaf HSV emerged as a practical, accessible tool for germplasm thinning and early-stage prioritization in perennial breeding programs. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction.

Zinc is an essential micronutrient at low concentrations, yet it becomes toxic at slightly higher ones. This is exploited by plants as an effective defensive strategy. However, the molecular components that are involved zinc-mediated immunity remain poorly defined. Here, we show that mixed-linked {beta}-1,3/1,4-glucans naturally occurring in microbial and grass cell walls and used as an agrobiological solution, trigger zinc accumulation in the Arabidopsis apoplast and upregulate the expression of the zinc transporters HMA2 and HMA4. This response occurs independently of salicylic acid, jasmonic acid and ethylene-mediated signalling pathways, but it requires the LysM receptor kinases CERK1, LYK4 and LYK5, indicating a specific pattern triggered immunity-associated mechanism. We further demonstrate that hma2hma4 mutants display constitutive activation of a broad set of defence-related genes, yet this transcriptional reprogramming is insufficient to confer resistance against the necrotrophic fungus Plectosphaerella cucumerina BMM. Moreover, metabolomic profiling highlights the contribution of specialized metabolites to this defective defence output. Altogether, our findings reveal that zinc-mediated toxicity constitutes a defence mechanism integrated into the immune response triggered by specific microbial or damage associated molecular patterns.

Accelerating photoprotective regulation to improve carbon assimilation is a promising strategy to increase crop productivity. Although rapid non-photochemical quenching (NPQ) relaxation has been validated as a target through metabolic engineering, it remains unclear whether conventional breeding has improved this trait. Here, we investigated whether more than a century of soybean breeding enhanced NPQ relaxation alongside light-saturated carbon assimilation and seed traits. We evaluated a historical panel of 24 soybean genotypes across vegetative and reproductive developmental stages by integrating NPQ relaxation, gas exchange parameters, xanthophyll-cycle pigment profiles, expression of key photoprotective genes (VDE, PsbS, and ZEP), seed number and seed weight. NPQ relaxation parameters were not consistently associated with genotype release year, seed number, or seed weight at either developmental stage. The only exception was the amplitude of the rapidly relaxing NPQ component (AqE), which was negatively correlated with all three variables during the reproductive stage. In contrast, genotype release year was positively associated with maximum net CO2 assimilation rate (Amax), maximum carboxylation rate of Rubisco (Vcmax), maximum electron transport rate (Jmax), seed number, and seed weight, while Amax and Vcmax were positively correlated with seed number and seed weight. These findings indicate that the greater photosynthetic capacity of modern genotypes was not accompanied by faster photoprotective response. Thus, photoprotective regulation has not kept pace with gains in photosynthetic capacity under field conditions. We conclude that rapid NPQ relaxation remains an important target for synchronizing photoprotection with the high photosynthetic capacity of modern soybean lines.

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

Plant cold acclimation emerges from coordinated adjustments in photosynthesis, primary metabolism, and intracellular carbon allocation. Yet, the regulatory role of subcellular metabolite compartmentation in natural variation of cold acclimation remains insufficiently understood. Here, we investigated four Arabidopsis thaliana accessions grown either individually or in bulk to determine how growth configuration and genotype shape the metabolism of sugars and organic acids during cold exposure. Using non-aqueous fractionation, we quantified plastidial, cytosolic, and vacuolar sugar pools alongside whole-cell carbohydrates, organic acids, enzyme activities, photosynthetic parameters, and stress markers. A neural-network classifier revealed that subcellular sugar distribution together with sugar amounts and organic acids provided the strongest discriminatory power among accessions, surpassing photosynthetic traits and enzyme activities. Our findings demonstrate that natural variation in cold acclimation is strongly determined by genotype-specific subcellular metabolite architectures, and that the cultivation strategy modulates these intracellular signatures. We conclude that subcellular compartmentation of metabolites represents a cellular control layer for natural variation of cold acclimation and resilience in Arabidopsis thaliana.

Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000, an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 m local flatness, registers full rotations to a loop closure of 156 m, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.

1. Forest canopies harbour strong microclimatic gradients that shape plant performance, species interactions and ecosystem processes. Yet, despite renewed interest sparked by global change, forest canopies remain difficult-to-access experimental spaces. 2. With the goal to expand access to tree canopies as experimental arenas, we designed, built, and tested TreeTOP, a standardized experimental platform that opens canopy space for manipulative ecological experiments, specifically with potted plants. TreeTOP features lightweight aluminum frames placed in mature tree canopies non-invasively, allowing potted plants to be placed in three different heights, ground level, shade canopy, and sun canopy. 3. We implemented TreeTOP using two contrasting infrastructure concepts to demonstrate its applicability in both highly equipped canopy research facilities and forests without permanent canopy infrastructure. One installation relied on a canopy crane, grid power and fully automated irrigation, whereas the second was built by certified tree climbers and was equipped with an autonomous solar-powered, battery-operated irrigation system. At both sites, environmental sensor networks monitor the experiment. 4. TreeTOP successfully reproduced characteristic canopy microclimatic gradients, including increasing light availability, daytime air temperatures and thermal extremes with canopy height. Despite differing infrastructures, both implementations generated comparable microclimatic patterns, demonstrating that standardized canopy experiments are feasible in forests with or without permanent canopy access. By opening canopy space for manipulative experiments, TreeTOP provides a transferable framework for investigating plant performance, phenology, species interactions and microbiome assembly under realistic forest conditions.

Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle.

Linker chemistry and conformation are central determinants of PROTAC activity, shaping ternary-complex geometry, cooperativity, target-lysine presentation and cellular permeability. Existing linker generators often lack explicit control over linker flexibility, require predefined attachment sites and linker lengths, or produce structures that demand substantial geometric correction, limiting their utility in practical PROTAC design. Here we introduce FlexiTAC, a Bayesian flow network that jointly generates linker atom types and coordinates from the warhead and E3-ligase-ligand contexts. We also assemble PROTAC-3D, a quality-controlled collection of 63,554 component-resolved PROTAC structures for model training, and PROTAC-Bench, which covers molecular quality, fragment preservation, geometric fidelity, conformational stability, fragment awareness, rediscovery and sampling efficiency. Compared to the best 3D baseline models, FlexiTAC improves validity by 12.0-12.7% and achieves the highest PoseBusters pass rate of 79.5%-80.0%. A differentiable guidance module shifted generated linkers along a conformational ensemble-derived rigidity axis without retraining the generator. In silico case studies further show that the model can accept crystal-derived, redocked or predicted structural inputs. Together, FlexiTAC, PROTAC-3D and PROTAC-Bench establish an integrated and reproducible framework for data-driven PROTAC linker design, combining controllable structure-conditioned generation with standardized training data and evaluation protocols. This framework expands the linker chemical and conformational space accessible to computational exploration, provides a foundation for future method development and enables the systematic generation of structure-conditioned linker designs with tunable conformational flexibility.

Network-based analyses of molecular interactions are useful for interpreting high-throughput omics data and identifying therapeutic targets. Cytoscape is the standard platform for these tasks, but users face a trade-off between accessible graphical workflows that are difficult to document and reproducible automation in Python or R that requires programming expertise. General-purpose coding assistants can generate Cytoscape Automation scripts, but remain external to Cytoscape. We present CyChat, a Cytoscape Desktop app that integrates a chat interface and a large language model (LLM) agent into the application. CyChat translates natural language into executable Cytoscape Automation workflows, runs generated Python code, and exports chat sessions with executed code as standalone Jupyter notebooks. To reduce setup barriers, CyChat includes an embedded Python runtime and supports both cloud-based and locally hosted LLMs. CyChat was evaluated across ten Cytoscape workflows using seven LLM providers, each represented by one LLM. The strongest configuration achieves a pass rate above 99%. In a qualitative evaluation based on a published network visualization, CyChat completes the task in 1.5-5 minutes, compared with 15-20 minutes for manual GUI workflows by computational biologists. CyChat is available through the Cytoscape App Store at https://apps.cytoscape.org/apps/cychat.

Motivation: Interactive tools that let non-programmers explore an analyzed single-cell dataset, its embeddings, gene expression, cell metadata, and marker genes, have become standard laboratory infrastructure. Every actively maintained tool in this space (ShinyCell, ScRDAVis, sCIRCLE, scViewer) is built on R Shiny and requires a Seurat object as input. Laboratories whose primary analysis pipeline is Python/scanpy, the dominant framework for single-cell RNA-seq, spatial, and multi-omic analysis, therefore have no lightweight, language-native option that pairs a shareable web-based viewer with a scriptable Python API: sharing a scanpy result means either exporting to Seurat first or handing over a notebook that only a programmer can run. Results: We present scPyviewer, a web-based viewer that ingests AnnData objects directly and reproduces the core interaction patterns of the incumbent R Shiny tools without leaving the Python stack. In a feature-parity audit against three actively maintained R Shiny incumbents, scPyviewer matches or exceeds every baseline capability (7/7); among these, it uniquely offers native AnnData ingestion with no Seurat conversion, and cross-dataset comparison over shared genes and matched cell-type composition. Benchmarked head-to-head against the R/Seurat rendering substrate the incumbents are built on, identical operations, identical data, across three datasets spanning 22,315 to roughly 313,000 cells, scPyviewer renders every core view faster at every scale tested (up to 3.6x on a single view) and at a fraction of the memory (5.2x lower on the smallest dataset). At the largest scale tested, the gap becomes categorical rather than incremental: scPyviewer completes every view on a 313,000-cell dataset while the Seurat substrate exhausts an 8 GB memory budget and fails outright. Beyond the interactive app, scPyviewer installs via pip or conda and exposes a public Python API that returns Matplotlib figures and pandas tables for scripted, publication-ready output. Availability and implementation: scPyviewer is implemented in Python 3.11 (scanpy 1.11.5, anndata 0.12.19, streamlit 1.59.2, plotly 6.9.0) and distributed with a one-command reproduction interface that installs pinned dependencies, regenerates the benchmark and all figures, and launches the interactive app. Source code is available at https://github.com/xuan13hao/scPyviewer.git.

Cyclic oligosaccharides possess industrial advantages, including molecular encapsulation capability and high physicochemical stability, owing to the absence of a reducing end. Recently, a novel cyclic tetrasaccharide, cycloisomaltotetraose (CI4), consisting of four -1,6-linked glucose units, and the enzymes responsible for its synthesis, cycloisomaltotetraose glucanotransferases (CI4Tases), were discovered. Unlike known cycloisomaltooligosaccharide glucanotransferases (CITases) that yield a wide distribution of cyclic products with a degree of polymerization (DP) of 7 or higher, CI4Tases strictly produce CI4. To elucidate the molecular mechanism underlying this strict DP4 specificity, we determined the crystal structures of CI4Tase from Agreia sp. D1110, in its ligand-free form, as well as in complex with the linear hydrolysis product isomaltotetraose (IG4) and with CI4. Structural comparisons revealed that a loop (M247 to R251) blocks the region corresponding to the -5 subsite of typical CITases, narrowing the substrate-binding pocket. This "molecular ruler" mechanism ensures that only a glycan chain of exactly four glucose units is accommodated for cyclization. Among mutants of the residue positioned at the center of bound CI4, the formation of by-products other than CI4 was significantly suppressed in F245L, F245A, and F245W. While the cyclization activity of all F245 mutants decreased, the CI4 hydrolysis activity of these three mutants was also significantly reduced, resulting in an increased specificity for cyclic sugar production. These findings elucidate the strict size-control mechanism of CI4Tase and provide a structural foundation for engineering cycloisomaltooligosaccharide-producing enzymes with optimized transglycosylation efficiency and specificity for industrial applications.

Anterograde and retrograde signalling establish bidirectional communication between the nucleus and chloroplasts. Retrograde signals from chloroplasts regulate nuclear gene expression while anterograde signals from the nucleus coordinate chloroplast development and maintain cellular homeostasis. How this bidirectional signalling framework extends beyond locus-specific regulation to shape the global spatial organization of nuclear chromatin across tissues remains unclear. Although anaesthesia can alter chromatin organisation, the role of chloroplast dysfunction in these changes remains unclear. Here, we investigate how chloroplast dysfunction and anaesthesia influence euchromatin and heterochromatin organisation in Solanum lycopersicum seedlings across tissues with contrasting photosynthetic competence. Using confocal and super-resolution radial fluctuation (SRRF) imaging with quantitative multiparameter analysis, we identify distinct, tissue-specific chromatin responses to chloroplast disruption and anaesthesia. Notably, anaesthesia induces distinct spatial chromatin changes across tissues that are independent of chloroplast dysfunction, suggesting a direct nuclear response to anaesthesia rather than a chloroplast-mediated retrograde effect. These findings highlight chromatin topology as a potential quantitative biomarker of cellular disruption and provide a framework for investigating anterograde chloroplast-nucleus coordination and stress-responsive nuclear organisation in plants.

Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependencies, expected outputs, and scientific limitations. Natural-language requests are translated into structured tasks, validated against this registry, executed through scientific tools, and recorded in a provenance-aware Living Lab Notebook. The system is designed to detect invalid inputs, failed tool calls, missing dependencies, and remote timeouts, and to route them to repair, retry, or escalation. The implemented and tested scope comprises RDKit-based molecular property and rule screening, protein structure analysis, docking-pose inspection, 3D visualization, and structured reporting. We demonstrate the workflow on a PubChem-retrieved osimertinib structure and a supplied 6LU7 docking artifact: the former yields deterministic descriptors (molecular weight 499.619 Da, cLogP 4.5098, TPSA 87.55 A^2), while the latter contains 2,387 protein ATOM records, 309 residues, and nine pose records. These examples are workflow demonstrations, not efficacy or affinity studies. Beyond retrospective prediction, the manuscript specifies a prior-minimized constructive mode in which a desired function is compiled into explicit physical, chemical, and systems constraints, candidate mechanisms are simulated, and observations are reintroduced for calibration and falsification; this is a proposed extension rather than a result of the present case studies. We describe an evaluation protocol that compares BloClaw with a standard single-agent workflow and fixed-script execution using task completion, scientific correctness, recovery success, provenance completeness, reproducibility, human review time, latency, and cost. This manuscript reports the system design, verified capability boundary, deterministic software artifacts, and a reproducible evaluation protocol; it does not claim benchmark improvements before those experiments are run. BloClaw is an execution and accountability layer for AI-assisted research, complementing expert review and experimental validation rather than replacing them.