Plant Direct

Small signaling peptides (SSPs) are critical regulators of plant growth, development, and responses to biotic and abiotic stress, yet their role in the C4 grass Sorghum bicolor is largely uncharacterized. To help fill this knowledge gap, 219 S. bicolor genes that encode SSPs were identified based on SSP sequences previously identified in Arabidopsis thaliana, Oryza sativa, Zea mays, Triticum aestivum, and Brachypodium distachyon. The 219 sorghum genes were assigned to 19 gene families, analyzed for the presence of motifs, and aligned with genes that encode SSPs in other plants using phylogenetic analysis. Expression of the 219 SSP encoding genes in sorghum organs, during stem development, and in stem tissues and cell types revealed distinct spatial, temporal and developmental patterns of expression. Genes associated with the SbCEP and SbRGF families were preferentially expressed in roots, whereas SbEPF genes were expressed in stems and panicles. The expression of genes during bioenergy sorghum stem growth and development was investigated because stems account for [~]80% of harvested biomass and serve as conduits for water and nutrient transport between leaves and roots. During stem development, 28 SSP encoding sorghum genes in several families (CLE, EPF, CEP, GASS, PSY, ES, PSK, CAPE, POE) were expressed at higher levels in zones of cell proliferation. For example, the TDIF homologs SbCLE41 and SbCLE42 were expressed at high levels in nascent stem nodes where they may regulate cambial activity and vascular bundle cell differentiation. A different set of 15 genes in the CIF, POE, CAPE, PSY, CEP, RALF, and CLE families were expressed at higher levels in zones of stem tissue differentiation highlighted by elevated expression of 5 SbRALFs in the stem nodal plexus. Cell type specific expression of many SSP encoding sorghum genes was also observed in fully elongated internodes indicating gene expression is regulated with high spatial resolution. Overall, the results provide a foundation of information for analysis of SSP functions in sorghum that can be integrated with knowledge of sorghum gene regulatory networks to modulate traits important for production of sorghum crops.

Plant-specific membrane receptor kinases with structurally diverse extracellular domains regulate key processes in plant growth, development, immunity and symbiosis. Structural studies of these glycoproteins are often hampered by the limited quantities in which they can be obtained. Here, we describe the LRR crystallization screen, which has enabled the successful crystallization and structure determination of multiple receptor kinase ectodomains, including ligand-and co-receptor-bound complexes. As an example, we report the 1.5 [A] resolution crystal structure of the leucine-rich repeat (LRR) domain of STRUBBELIG-RECEPTOR FAMILY 6 (SRF6) from Arabidopsis thaliana. The SRF6 ectodomain contains seven LRRs and a disulfide-bond-stabilised N-terminal capping domain but lacks the canonical C-terminal cap and the N-glycosylation pattern typically observed in other family members. Previously reported protein-protein interactions between the SRF6 and SRF7 ectodomains and the receptor kinases BRI1, BRL1, BRL3, SERK3 and BIR1-3 could not be confirmed by quantitative isothermal titration calorimetry and grating-coupled interferometry assays, suggesting that these structurally conserved LRR receptor kinases may have signalling functions outside the brassinosteroid pathway. SynopsisA crystallisation screen that has enabled the structural analysis of various extracellular domains of plant membrane receptor kinases is described together.

Course-based Undergraduate Research Experiences (CUREs) have emerged as a transformative approach to science education, expanding access to authentic research opportunities beyond the traditional undergraduate research assistant (URA) training. By embedding research into a curriculum, CUREs engage a broad and diverse population of students in a classroom environment that emphasizes experimental design, data analysis, and scientific communication. However, this has been difficult to develop for fields such as plant synthetic biology due to the long timescales of plant transformation. One avenue around this problem is to utilize a recent innovation that enables high throughput and rapid screening of gRNA efficacy by leveraging viral-based delivery of guide RNAs (gRNAs). In this work, we develop and validate a CURE with undergraduate students at Colorado State University (CSU). Students worked in teams to design and test efficacy of gRNAs targeting a Cas9-based transcriptional repressor to different regions of the promoters of the three GIBBERELLIN INSENSITIVE 1 genes (GID1a, GID1b, and GID1c) in Arabidopsis thaliana. Over the semester, students generated and analyzed gene expression data to understand the efficiency of twelve new gRNAs. We further validated CURE student-identified gRNAs with an undergraduate research assistant (URA) that assessed target gene expression and phenotypic outcomes in stable transgenic lines expressing SynTF constructs with the strongest gRNAs from the class. We further describe the curriculum structure to facilitate adoption at other institutions and present student-generated datasets demonstrating the utility of ViN-based screening for identifying effective SynTF gRNAs for plant functional genomics and engineering. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/715601v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@13869f5org.highwire.dtl.DTLVardef@b469feorg.highwire.dtl.DTLVardef@9aa51borg.highwire.dtl.DTLVardef@cdc129_HPS_FORMAT_FIGEXP M_FIG C_FIG

Understanding the regulation of enzyme activity involved in photosynthesis is essential for engineering enhanced carbon fixation in crops. In C4 plants, the enzyme phosphoenolpyruvate carboxylase (PEPC, EC 4.1.1.31) is one of the most abundant leaf enzymes and plays an essential role in photosynthetic carbon dioxide (CO2) fixation. The enzyme also plays a key role in central metabolism (e.g., providing intermediates to the citric acid cycle) and therefore must be highly regulated to coordinate its activity. The regulation of PEPC activity can occur allosterically by glucose 6-phosphate activation and malate inhibition, which is in part influenced by reversible phosphorylation. A specific light-dependent phosphorylation of PEPC at an N-terminal serine residue by the PEPC-protein kinase (PEPC-PK) can regulate its sensitivity to this allosteric regulation. However, the impact of this PEPC phosphorylation has not been tested in a C4 crop. Therefore, we created PEPC-PK mutant lines in Zea mays to assess the impact of PEPC phosphorylation on its allosteric regulation, photosynthesis, and growth. While the maximum PEPC activity was unchanged, PEPC in the PEPC-PK mutant plants was not phosphorylated under light and was more sensitive to malate inhibition. However, gas exchange, electron transport, and field biomass analyses showed no differences in the PEPC-PK mutant plants. These results demonstrate that in Z. mays PEPC phosphorylation affects enzyme sensitivity to malate in vitro but does not substantially alert photosynthetic performance or growth under field conditions suggesting additional regulation of PEPC activity in planta.

Self-incompatibility (SI), a reproductive mechanism that prevents self-pollen from fertilizing the ovule, is widespread in flowering plants, including the Brassicaceae family, where it promotes outcrossing, genetic diversity, and hybrid vigor. Although prevalent in Brassica rapa, an economically vital crop, it remains poorly characterized in widely grown varieties, such as toria and yellow sarson, with prior studies primarily focused on Brassica napus. Given its potential for hybrid breeding and crop improvement in rapeseed (B. rapa), we characterized key SI-regulatory genes, analyzing their phylogenetic relationships, structure-function dynamics, and expression patterns. Our results indicate sequence, structural, and functional homology as well as conservation with previously known candidates. This study identifies SRK, FER, and ARC1 as essential, while MLPK plays a minor role in SI for the varieties under study. Furthermore, we identified that SRK, FER, and MLPK activate ROS during the SI response, while ARC1 does not. Our findings establish a foundation for harnessing this natural system to integrate agriculturally important traits and sustain them across generations via outcrossing.

Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method. Short AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.

Pseudomonas putida strain KT2440 is a crucial model organism for synthetic biology and bioengineering applications, yet there currently exists no comprehensive metabolomics database comparable to those available for other model organisms. This gap hinders the use of untargeted metabolomics for exploratory analyses in this system. We developed the P. putida metabolome reference database (PPMDB v1) to address this limitation by consolidating metabolite information from multiple sources and expanding coverage through computational predictions. The database was constructed by curating metabolites from BioCyc, BiGG, and other literature sources, then computationally expanding this collection using BioTransformer environmental transformation predictions to generate additional predicted metabolites. We enhanced the databases utility for molecular annotation in metabolomics studies by incorporating analytical properties including collision cross-sections, tandem mass spectra, and gas-phase infrared spectra. These analytical properties were gathered from existing measurement data or predicted using computational tools. We further augmented the database through inclusion of reaction information and pathway annotations, facilitating biological interpretation of metabolomics data. This publicly available resource fills a critical gap in P. putida research infrastructure, supporting metabolite annotation and biological interpretation in untargeted metabolomics studies and enabling in-depth exploratory analyses of this important synthetic biology platform at the molecular level. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/713193v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@c8828forg.highwire.dtl.DTLVardef@1f3a5c5org.highwire.dtl.DTLVardef@1084535org.highwire.dtl.DTLVardef@1f7ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

During drought, numerous compounds accumulate in plant tissues, but their physiological roles remain unclear - they may function as osmolytes, osmoprotectants, or merely arise as by-products of stress-induced metabolic shifts. We developed an experimental approach to link accumulation patterns with specific functions, using Scots pine (Pinus sylvestris L.) saplings subjected to water deprivation and subsequent rewatering as a model system. We monitored changes in relative water content (RWC) and osmotic adjustment dynamics, employed untargeted primary metabolite profiling for preliminary screening of compounds correlated with water status, and performed quantitative GC-MS and LC-MS analyses of selected metabolites. Major inorganic cations (K, Ca{superscript 2}, Mg{superscript 2}) were also quantified to assess their potential roles. Our results revealed that tryptophan, valine, and lysine - though generally present in low abundance - exhibited selective accumulation under severely reduced RWC ([≤] 70%), suggesting their involvement as osmoprotectants. Major organic acids, particularly shikimic acid, showed trends consistent with osmotic adjustment. Notably, neither sucrose nor inorganic cations appeared to function as primary osmolytes in this context. The proposed approach offers a viable strategy for identifying compounds involved in plant adaptation to water deficit, with potential applications in breeding programs aimed at improving drought tolerance. HighlightsAn approach to identify osmolytes and osmoprotectants was implemented Accumulation of Trp, Val and Lys was consistent with their role in osmoprotection Osmotic adjustment relied predominantly on organic acids than on inorganic ions Monosaccharides but not sucrose correlates with changes in needle water status

The chloroplast NADH dehydrogenase-like (NDH) complex mediates cyclic electron transport (CET) around photosystem I (PSI) and contributes to photosynthetic regulation and photoprotection under various environmental stresses. Although NDH function has been extensively characterized under controlled conditions, NDH-deficient mutants often show only subtle phenotypes in such environments, leaving its physiological importance under naturally fluctuating field conditions poorly understood. Here, we evaluated growth, yield, and photosynthetic performance of NDH-deficient rice cultivated under field conditions. Mutant plants exhibited reduced biomass accumulation and grain yield compared with wild type. Detailed physiological analyses revealed that NDH deficiency markedly decreased PSI electron transport and CO2 assimilation, particularly under low temperature and sub-saturating irradiance. At moderate and high temperatures, reductions in carbon fixation were largely confined to low-light conditions, whereas at low temperatures, impairment extended across nearly the entire light response range. Under repetitive fluctuating light regimes, NDH-deficient plants showed progressive declines in photosynthesis accompanied by a selective decrease in PSI photochemical capacity without changes in PSII maximum efficiency, indicating PSI-specific photoinhibition. These findings demonstrate that NDH-dependent CET plays a crucial role in sustaining photosynthetic efficiency and crop productivity in dynamic field environments by stabilizing PSI redox balance and maintaining long-term carbon gain. Summary StatementNDH-dependent cyclic electron transport supports photosynthesis and yield in field-grown rice by maintaining PSI function under fluctuating light, low temperature, and sub-saturating irradiance.

Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R{superscript 2} = 0.53), berry diameter (R{superscript 2} = 0.79), and total acidity (R{superscript 2} = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. HighlightSpectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

The RETINOBLASTOMA-RELATED (RBR) protein in plants functions as a cell-cycle inhibitor, regulating cell numbers in developing organs and establishing cellular quiescence during growth. Although the role of RBR counterparts in animals also involves regulating cell size, this potential function remains unexplored in plants. We investigated transgenic Arabidopsis plants with altered RBR levels and observed corresponding changes in cell size from embryogenesis through organ development. In addition, stomatal meristemoid cells with reduced RBR levels divided beyond the size threshold, whereas elevated RBR levels increased their size. RBR stimulated terminal differentiation in the stomatal lineage by inducing MUTE and CYCLIN D5;1 expression, whereas reduced RBR levels maintained asymmetric divisions through high SPEECHLESS and CYCLIN D3;1 expression. Interestingly, the cell proliferation-dependent phosphorylation of RBR at the conserved 911Ser site positively correlated with RBR protein levels in the transgenic lines and aligned with the effect of RBR on cell size. This study discusses the potential link between RBRs control of cell proliferation and cell size, providing new insights into the coordinated regulation of plant development.

Light wavelengths modulate plant growth, metabolism, and physiology. Amaranthus, a C4 underutilized climate resilient crop with promising nutritional properties remained unexplored in terms of metabolite enrichment under monochromatic light wavelengths of visible spectrum. In current study, two cultivars of Amaranthus tricolor (green and red) were exposed to seven light regimes of photosynthetically active radiation (PAR; 400-700 nm): deep blue, blue, green, amber, red, deep red, far red, and their metabolic responses were captured using Gas Chromatography-Mass Spectrometry. The metabolic analysis revealed wavelength-specific reprogramming in the levels of organic acids, sugars, amino acids, fatty acids as well as phenolics. In both the green and red Amaranthus, branched-chain amino acids and phenylalanine, which are nutritionally essential, were significantly elevated under far-red light. While the phenolics such as caffeic acid and ferulic acid were elevated under green and deep blue light respectively in green Amaranthus, amber light wavelengths enhanced these phenolics in red Amaranthus. The study highlighted cultivar-specific metabolic rewiring triggered by specific wavelengths. Altogether, these findings provides insights into metabolic adaptation and demonstrate the ability of light wavelength to specifically enrich the targeted metabolite of nutritional relevance in Amaranthus. It offers strategies to improve the nutritional value of crops in controlled agriculture systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=167 HEIGHT=200 SRC="FIGDIR/small/714947v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1a4477dorg.highwire.dtl.DTLVardef@518550org.highwire.dtl.DTLVardef@7682dorg.highwire.dtl.DTLVardef@4876e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Genomic imprinting is a rare epigenetic phenomenon in plants and animals, defined by parent-of-origin specific gene expression. Its molecular mechanisms and evolutionary significance remain incompletely understood. In this study, we investigated whether genomic imprinting occurs in Scots pine and, by extension, in other conifers to gain insight into the evolutionary origins of imprinting. We performed reciprocal crosses to assess imprinting in seed embryos and applied a unique approach that used exome-capture data from the haploid, maternally inherited megagametophyte tissue to identify maternal alleles, thereby allowing us to infer paternal alleles in the embryos of the same seeds. Our findings show that maternally inherited haploid megagametophyte tissue offers an effective strategy for resolving parental alleles in offspring while simultaneously removing extensive paralogous variation from the dataset. This framework is broadly applicable to other conifer species and to taxa that possess comparable maternally derived haploid tissues. No evidence of genomic imprinting was detected. Although the limited overlap between the exome-capture and RNA-sequencing datasets and the stringent paralog filtering reduced the amount of analyzable data considerably, the absence of detectable imprinting may also reflect genuinely weak or absent imprinting signals in conifers. We identified several limitations in this preliminary study and outline recommendations for future work to overcome them, and additional research will be necessary to determine whether genomic imprinting occurs in conifers

Photosynthesis accounts for most of the final grain yield in rice, making improvements in radiation use efficiency (RUE) a key strategy for enhancing productivity. Agronomically, RUE is defined as the biomass produced per unit of total solar radiation or photosynthetically active radiation intercepted by the canopy. However, the interaction between carbon and nitrogen metabolism plays a critical role in determining plant growth and grain yield. Assimilated nitrogen is required for the synthesis of photosynthetic pigments and enzymes, while the reduction of nitrate (NOLL) and nitrite (NOLL), as well as the assimilation of ammonium (NHLL), depend on the reducing power and carbon skeletons generated by photosynthesis. In this study, two high-yielding rice (Oryza sativa) cultivars--an indica-type (El Paso 144) and a japonica-type (INIA Parao) were subjected to two nitrogen treatments (3 mM and 9 mM NOLL/NHLL) and two light intensities (850 and 1500 mol mL{superscript 2} sL{superscript 1}). A strong interaction between light intensity and nitrogen metabolism was observed, with contrasting responses between subspecies. These differences reflect a coordinated regulation of carbon assimilation and primary nitrogen metabolism. The results provide new insights into the metabolic strategies underlying nitrogen compound accumulation under variable irradiance. Such knowledge is essential for improving nitrogen fertilizer use efficiency and yield performance in elite rice genotypes cultivated under commercial field conditions.

Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance (R2 > 0.6 for soil pH metrics and H2). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H2 = 0.18 vs. filtered H2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHTStress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.

AO_SCPLOWBSTRACTC_SCPLOWNitrogen fertilizers are essential for crop productivity but cause environmental harm, necessitating the development of cultivars that thrive under limited nitrogen. This study investigates the transcriptomic response to nitrate in Arabidopsis thaliana (a model dicot), Brachypodium distachyon (a model Pooideae), and Hordeum vulgare (barley, a domesticated Pooideae) to identify conserved and species-specific molecular mechanisms. Using RNA-seq after 1.5 and 3 hours of nitrate treatment, we found that core nitrate-responsive biological processes - such as nitrate transport, assimilation, carbon metabolism, and hormone signaling - are largely conserved across species. However, comparative analysis at gene level based on orthology revealed specificities between the species. For instance, rRNA processing was uniquely stimulated in Arabidopsis, while cysteine biosynthesis from serine and gibberellin biosynthesis were specifically regulated in Brachypodium and barley. Orthologs of key nitrate-responsive genes (e.g., NRT, NLP, TCP20) exhibited variable regulation, reflecting potential adaptations linked to domestication or nutrient acquisition strategies. These findings highlight the importance of integrating model and crop species to uncover targets for improving nitrogen use efficiency in cereals. The study provides a pipeline integrating gene ontology and orthology analyses to compare transcriptomic responses between species.

In cereal-legume intercrops, weed suppression is primarily driven by cereals, whose competitiveness is shaped by trait plasticity--morphological adjustments in response to the intercrop environment. However, how individual cereal traits respond plastically and contribute to system performance remains unclear, hampering improvements through breeding or system design. We combined field experiments with functional-structural plant modelling to quantify plastic responses of four cereal traits (tiller number, tiller angle, specific leaf area (SLA), and specific internode length (SIL)) and their effects on weed suppression and crop productivity. Field measurements revealed plasticity in tiller number, tiller angle, and SIL between sole crops and intercrops, while SLA showed minimal differences. Simulations showed that intermediate tiller numbers resulted in the strongest weed suppression and highest productivity, indicating an optimum, while more horizontal tillers suppressed weeds slightly better than vertical ones. Weed suppression increased with higher SLA values, while SIL showed a saturating response, increasing to intermediate SIL values and plateauing thereafter. In simulations with short-statured cereal phenotypes (low SIL), the reduction in cereal weed suppression was compensated by the legume component. This study demonstrates how FSP modelling can be used to investigate trait plasticity mechanisms and generate testable hypotheses about trait effects in complex intercrop systems. HighlightCereal trait plasticity shapes weed suppression in cereal-legume intercrops, with distinct response patterns per trait, while legumes can compensate for weakly competitive cereals, suggesting balanced competition over cereal dominance.

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned Aerial Vehicle (UAV) derived metrics offer a potential high-throughput, cost-effective alternative for lodging phenotyping. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest residual mean square error (RMSE=16-18), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Tacca chantrieri, black bat flower, has showy flowers often appearing almost black. Here, we present the genome sequence and corresponding annotation to identify the genetic basis of the pigmentation. Candidate genes associated with the anthocyanin biosynthesis were identified based on this genome sequence and investigated with respect to their properties. The best dihydroflavonol 4-reductase (DFR) candidate, which harbours all amino acid residues believed to be required for DFR activity, shows a threonine in the substrate preference determining position where most characterized DFRs display asparagine or aspartate. This amino acid residue appears to be frequent in the Dioscoreaceae family as a comprehensive investigation revealed.