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bioRxiv2026

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

plant biology↗

bioRxiv2022

Rice GLUCAN SYNTHASE-LIKE5 promotes Callose deposition in Anthers to maintain proper Male Meiosis Initiation and Progression

Callose is a plant cell-wall polysaccharide whose deposition is spatiotemporally regulated in various developmental processes and environmental stress responses. Appearance of callose in premeiotic anthers is a prominent histological hallmark for the onset of meiosis in flowering plants, whose biological role in meiosis is unknown till date. Here we show that rice GLUCAN SYNTHASE LIKE5 (OsGSL5), a callose synthase, localizes on the plasma membrane of pollen mother cells (PMCs), and is responsible for biogenesis of callose in anther locules through premeiotic and meiotic stages. In osgsl5 mutant anthers mostly lacking callose deposition, aberrant PMCs accompanied by aggregated, unpaired or multivalent chromosomes were frequently observed, and furthermore, a considerable number of mutant PMCs untimely progress into meiosis compared to wild type PMCs. Immunostaining of meiosis-specific protein PAIR2 in premeiotic PMCs revealed precocious meiosis entry in osgsl5 anthers. The findings of this study bestows new knowledge on function of callose in controlling timing of male meiosis initiation and progression, in addition to roles in microsporogenesis, in flowering plants.

developmental biology↗

bioRxiv2019

A Novel Antenna Protein Complex in the Life Cycle of Cyanobacterial Photosystem II

In oxygenic photosynthetic organisms, photosystem II (PSII) is a unique membrane protein complex that catalyzes light-driven oxidation of water. PSII undergoes frequent damage due to its demanding photochemistry. However, many facets of its repair and reassembly following photodamage remain unknown. We have discovered a novel PSII subcomplex that lacks five key PSII core reaction center polypeptides: D1, D2, PsbE, PsbF, and PsbI. This pigment-protein complex does contain the PSII core antenna proteins CP47 and CP43, as well as most of their associated low-molecular-mass subunits, and the assembly factor Psb27. Immunoblotting analysis, multiple mass spectrometry techniques, and ultrafast spectroscopic results supported the absence of a functional reaction center in this chlorophyll-protein complex. We therefore refer to it as the no reaction center complex (NRC). Additionally, genetic deletion of PsbO on the PSII lumenal side resulted in an increased NRC population, indicative of a faulty PSII repair scheme at the cellular level. Analytical ultracentrifugation studies and clear native acrylamide gel analysis showed that the NRC complex is a stable pigment-protein complex and not a mixture of free CP47 and CP43 proteins. Our finding challenges the current model of the PSII repair cycle and implies an alternative PSII repair strategy. We propose that formation of this pigment-protein complex maximizes PSII repair economy by preserving an intact PSII core antenna shell in a single complex that is available for PSII reassembly, thus minimizing the risk of randomly diluting multiple recycling components in the thylakoid membrane following a photodamage event at the RC.\n\nSignificance statementPhotosystem II (PSII) converts sunlight into chemical energy, powering nearly all life on Earth. The efficiency of this process is maximized under various environmental conditions by a frequent repair and reassembly cycle that follows inevitable PSII damage even during normal oxygenic photosynthesis. We have isolated a novel pigment protein PSII subcomplex in which, surprisingly, the reaction center (RC) components of PSII are absent. Formation of this stable chlorophyll-protein complex suggests a protective mechanism whereby longer-lived PSII subunits are unplugged from the damaged RC to prevent harmful, aberrant photochemistry during RC repair. This finding provides intriguing new insight into how PSII is assembled and rebuilt to optimize its performance to optimally catalyze one of the most challenging reactions in biology.

plant biology↗

bioRxiv2026

Rethinking yield stability through phenotypic plasticity and its link to modern statistical methods

In the context of advocacy for yield stability, trade-offs between yield and yield stability, the frequent lack of definitions, and the variation in methods when they are explicit, we connect two perspectives: phenotypic plasticity and factor analytics. Phenotypic plasticity brings over a century of research in developmental biology, ecology and evolution, and is gaining traction in crop science. Factor analytics is an advanced linear mixed model of multi-environment data with factor analytic variance structures for the variety-by-environment interaction effect. We (1) review phenotypic plasticity, define agronomically adaptive plasticity where varieties (or practices) consistently return superior yield (or other traits) across environments with no trade-off, and describe percentile-plasticity plots to assess the agronomic value of plasticity; (2) outline factor analytic models, and (3) link plasticity and factor analytic models mathematically and empirically. We show that phenotypic plasticity of cereal yield correlates positively with overall performance obtained from factor analytic models when plasticity is adaptive and negatively when it is maladaptive. We conclude that phenotypic plasticity contributes biological meaning to opaque analytical approaches, and that biologically grounded statistics are needed to challenge weak agronomic narratives, such as advocacy for stability that might reflect decision biases rather than critical consideration of its benefits. HighlightUncritical advocacy for crop yield stability is common. Here we advance a biological-statistical synthesis of yield stability and test theoretical predictions with actual wheat and oat yield data.

plant biology↗

bioRxiv2025

Phylogenomic challenges in polyploid-rich lineages: Insights from orthology inference and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

AO_SCPLOWBSTRACTC_SCPLOWPhylogenomic discordance is pervasive and cannot always be resolved by increasing the amount of sequencing data alone. Biological processes such as polyploidy, hybridization, and incomplete lineage sorting are major contributors to discordance and must be accounted for to avoid misleading evolutionary interpretations. To better understand how these processes influence phylogenetic reconstruction, we conducted a comprehensive phylogenomic study in the complex genus Packera. With over 90 species and varieties, 40% of which exhibit polyploidy, aneuploidy, or other cytological complexities, Packera presents significant challenges for phylogenetic reconstruction. Given these complexities, we assessed different published paralog processing methods on the resulting evolutionary relationships and phylogenetic support of this group. We then applied three of these methods to evaluate their impact on tree topology and our understanding of Packeras evolutionary history by constructing a time-calibrated phylogeny, reconstructing historical biogeography, and testing for ancient reticulation. Phylogenetic outcomes varied based on the paralog processing method used, with no method performing the best over others. Our findings highlight the large impact of orthology inference and paralog processing on phylogenomic analyses, particularly in polyploid-rich groups such as Packera, and we offer guidance on methodological impacts along with practical recommendations. We note that gaining a robust understanding of Packeras evolutionary history requires more than computational approaches alone. While technological advancements have greatly expanded our ability to analyze genomic data, effective phylogenomic research still relies on strong taxon sampling and detailed species knowledge. Without careful attention to the biological context, such as reproductive boundaries, cytological variation, ecological interactions, and historical biogeographic processes, phylogenomic studies risk misinterpreting evolutionary history and processes. By accounting for these factors, we can begin to improve the accuracy of evolutionary reconstructions and gain deeper insights into the complex history of plant diversification.

plant biology↗

bioRxiv2023

Antisense transcription interferes with heterologous expression of a codon-optimised mNeonGreen fluorescent reporter in the chloroplast of Chlamydomonas reinhardtii.

Although over 30 years have passed since Chlamydomonas reinhardtii chloroplast transformation was first achieved, robust genetic engineering of the chloroplast still remains a challenging task. The glass-bead transformation method has enabled simple and accessible chloroplast transformation of the C. reinhardtii TN72 strain, allowing generation of marker-free transplastomic strains for low-cost experimentation. However, lack of functional in vivo fluorescent reporters limit research and widespread development of chloroplast engineering. Here, we developed a chloroplast codon-optimised mNeonGreen fluorescent reporter, which can be detected in vivo through fluorescence microscopy and fluorometry, by context-aware construct engineering, leading up to a [~]6-fold increase in fluorescence. We found evidence for chloroplast post-transcriptional regulation of gene expression derived from formation of antisense pairing of mRNAs due to transcriptional readthrough of the convergent adjacent gene, which was validated through detection of the double-stranded RNA. In addition, engineering approaches were used to modulate transcriptional readthrough, allowing a better understanding of context effects that are relevant for heterologous expression. Finally, we characterised a suite of regulatory parts for transgene expression in a sense-transcriptional context regarding to the selectable marker, achieving up to [~]2-fold increase in mNeonGreen fluorescence levels regarding to the control, with the use of PrrnS, 5atpA and 3rbcL endogenous regulatory sequences from the chloroplast of C. reinhardtii. This work provides new tools for studying basic aspects of the molecular biology in the chloroplast in C. reinhardtii, as well as evidence for fundamental processes of gene regulation that may enable developing rules for more efficient chloroplast engineering.

plant biology↗

bioRxiv2025

Single-nucleus transcriptome analyses uncover a dynamic transcriptional landscape of soybean roots in response to soybean cyst nematode infection

Soybean cyst nematode (SCN, Heterodera glycines) causes substantial yield losses by inducing the formation of specialized feeding structure called syncytia, which arise from extensive reprogramming of host root cells. Despite their pivotal role in SCN parasitism, the molecular mechanisms governing syncytium formation and function remain poorly understood, largely due to the technique challenges in precisely isolating these rare cell types. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to generate a comprehensive, high-resolution transcriptomic atlas of SCN-infected soybean roots, comparing two resistant cultivars (PI 88788 and Forrest) with a susceptible cultivar (Williams 82) across multiple infection stages. By leveraging the well-characterized syncytium marker GmSNAP18, we successfully identified and transcriptionally profiled syncytial cells, and validate the spatial expression of known SCN-resistant genes. Our analyses revealed strong induction of resistance-associated genes specifically enriched in syncytia from resistant cultivars. Functional validation further demonstrated that overexpression of three novel candidate genes significantly suppressed SCN development. Pseudotime trajectory analyses traced syncytium development back to procambial cell origins, uncovering distinct, cultivar-specific developmental pathways. Collectively, this work provides a foundational single cell transcriptomic resource, advances our understanding of syncytial biology and SCN resistance mechanisms, and identifies promising molecular targets for engineering nematode-resistant soybean varieties.

plant biology↗

bioRxiv2024

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

Senescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: RCC and TNDGR. Convolutional neural networks (CNNs) trained on temporally concatenated, or "sandwiched," UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6). Model performance was strongest for FPC1 scores from VSR (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to- moderate for RCC (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

plant biology↗

bioRxiv2020

Branched-chain amino acid catabolism depends on GRXS15 through mitochondrial lipoyl cofactor homeostasis

Iron-sulfur (Fe-S) clusters are ubiquitous cofactors in all life and are used in a wide array of diverse biological processes, including electron transfer chains and several metabolic pathways. Biosynthesis machineries for Fe-S clusters exist in plastids, the cytosol and mitochondria. A single monothiol glutaredoxin (GRX) has been shown to be involved in Fe-S cluster assembly in mitochondria of yeast and mammals. In plants, the role of the mitochondrial homologue GRXS15 has only partially been characterized. Arabidopsis grxs15 null mutants are not viable, but mutants complemented with the variant GRXS15 K83A develop with a dwarf phenotype. In an in-depth metabolic analysis, we show that most Fe-S cluster-dependent processes are not affected, including biotin biosynthesis, molybdenum cofactor biosynthesis and the electron transport chain. Instead, we observed an increase in most TCA cycle intermediates and amino acids, especially pyruvate, 2-oxoglutarate, glycine and branched-chain amino acids (BCAAs). The most pronounced accumulation occurred in branched-chain -keto acids (BCKAs), the first degradation products resulting from deamination of BCAAs. In wild-type plants, pyruvate, 2-oxoglutarate, glycine and BCKAs are all metabolized through decarboxylation by four mitochondrial lipoyl cofactor-dependent dehydrogenase complexes. Because these enzyme complexes are very abundant and the biosynthesis of the lipoyl cofactor depends on continuous Fe-S cluster supply to lipoyl synthase, this could explain why lipoyl cofactor-dependent processes are most sensitive to restricted Fe-S supply in GRXS15 K83A mutants. One-sentence summaryDeficiency in GRXS15 restricts protein lipoylation and causes metabolic defects in lipoyl cofactor-dependent dehydrogenase complexes, with branched-chain amino acid catabolism as dominant bottleneck.

plant biology↗

bioRxiv2026

Isoform-resolved spatial transcriptomics on a lab-made high-density array via a single-chip NGS-TGS workflow

High-resolution spatial transcriptomics is still cost-prohibitive and dominated by short-read sequencing, limiting in situ detection of transcript structures. Here we present a low-cost, lab-made, high-density bead-in-microwell chip assembled by routine centrifuge and decoded by a tri-part combinatorial long-barcode strategy. The design provides 5.6x107 barcode combinations across 4.1x10{square} capture sites, reducing collisions while remaining tolerant to third-generation sequencing raw errors. Using a single-chip, dual-platform workflow, we split full-length cDNA for next-generation and third-generation sequencing within the same spatial coordinates. In an incompatible tomato-pepper graft, we identified interface-associated splicing reprogramming, including thousands of unannotated isoforms, enriched novel CaGRP1 isoforms, and SlPIP2 intron retention, supported by individual long-read sequences. In mouse embryos, we further demonstrate cross-species applicability by resolving unannotated Col1a2 isoforms in oligodendrocyte progenitors and spatially restricted Dalrd3 intron retention. This cost-efficient system broadens access to spatial full-length transcriptomics and may support diverse biological studies.

plant biology↗

bioRxiv2022

miRNA408 and its encoded peptide, miPEP408, regulate arsenic stress response in Arabidopsis

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in regulating various developmental and biological processes. The expression of miRNAs is differentially modulated in response to various stresses. Based on the recent findings, it has been shown that some of the pri-miRNAs encode small regulatory peptides, microRNA-encoded peptides (miPEP). miPEPs are reported to regulate the growth and development of plants by modulating corresponding miRNA expression; however, the role of these peptides in different stresses has not been explored yet. Here, we reported that pri-miR408 encodes a small peptide, miPEP408, that regulates the expression of miR408, its targets, and associated phenotype in Arabidopsis. Plants overexpressing miR408 showed severe sensitivity under low sulphur (LS), Arsenite As(III) and LS+As(III) stress, while miR408 mutant developed through the CRISPR/Cas9 approach showed tolerance. Transgenic lines showed phenotypic alteration and modulation in the expression of genes involved in the sulphur reduction pathway and affect sulphate and glutathione accumulation. Similar to miR408 overexpressing lines, the exogenous application of synthetic miPEP408 or miPEP408 overexpression led to sensitivity in plants under LS, As(III) and combined LS+As(III) stress compared to control. This study suggests the involvement of miR408 and miPEP408 in heavy metal and nutrient deficiency responses. One-sentence summarymiR408 and peptide encoded by miR408, miPEP408, regulate arsenic stress and low sulphur responses in Arabidopsis.

plant biology↗

bioRxiv2024

Improved tests for the origin of allometric scaling across tree architectures

The scaling of organismal metabolic rates with body size is one of the most prominent empirical patterns in biology. For over a century, the nature and causes of metabolic scaling have been the subject of much focus and debate. West, Brown, and Enquist (WBE) proposed a general model for the origin of metabolic scaling from branching vascular networks. However, recent empirical tests of WBE vascular scaling predictions in plants and animals have reported deviations caused by variability in network geometry. After clarifying the core assumptions of the WBE model, we revisit the methods and conclusions of recent tests conducted in trees, finding support for key WBE predictions in woody plant architecture. To do this, we apply an approach that better captures: i) network branching self-similarity and ii) leaf area as a proxy of plant metabolic capacity. The WBE model also predicts curvature in metabolic scaling in smaller organisms, and we introduce a novel method that accounts for curvature in plant branching geometry. Together, these advances allow more direct measurements of metabolic scaling than previous work, and we apply them to a dataset of diverse laser-scanned tree architectures. Analyses reveal the predicted interspecific [3/4] metabolic scaling across tree crowns, with intraspecific variation within individual tree crowns. Scaling variability is consistent with WBE predictions for curvature from asymptotic growth and underlying variation in branching geometry. We conclude that linking fine-scale branching variation to metabolic scaling allometries remains a challenge, while our results support the foundational hypotheses of the WBE model. Author summaryTrees survive in a variety of habitats and lifestyles across Earth. They are also characterized by a stunning array of sizes and shapes that make trees objects of vast cultural, economic, and ecological importance. At the same time, the need to link vascular plant function with traits and environment is more pressing than ever. Size (body mass) is fundamentally linked to plant functioning within ecosystems through allometric relationships. Allometric relationships emerge from the geometry of branch networks in trees, which are increasingly well-characterized with remote-sensing data. We use a dataset of laser-scanned tree crowns to test allometric predictions that link size to key traits, particularly metabolic capacity, understood as total leaf area. Our results indicate that i) scanning technology can provide accurate assessments of branch allometry with proper data preparation, and ii) studying branch allometries provides an organizing framework for interpreting natural variation in tree architecture.

plant biology↗

bioRxiv2020

Genomic evidence reveals SPA-regulated developmental and metabolic pathways in dark-grown Arabidopsis seedlings

Photomorphogenesis is repressed in the dark mainly by an E3 ubiquitin ligase complex comprising CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1) and four homologous proteins called SUPPRESSOR OF PHYA-105 (SPA1-SPA4) in Arabidopsis. This complex induces the ubiquitination and subsequent degradation of positively acting transcription factors (e.g., HY5, HFR1, PAP1 and others) in the dark to repress photomorphogenesis. Genomic evidence showed a large number of genes regulated by COP1 in the dark, of which many are direct targets of HY5. However, the genomic basis for the constitute photomorphogenic phenotype of spaQ remains unknown. Here, we show that >7200 genes are differentially expressed in the spaQ background compared to wild-type in the dark. Comparison of the RNA Sequencing (RNA-Seq) data between cop1 and spaQ revealed a large overlapping set of genes regulated by the COP1-SPA complex. In addition, many of the genes coordinately regulated by the COP1-SPA complex are also regulated by HY5 directly and indirectly. Taken together, our data reveal that SPA proteins repress photomorphogenesis by controlling gene expression in concert with COP1, likely through regulating the abundance of downstream transcription factors in light signaling pathways. Moreover, SPA proteins may function both in a COP1-dependent and -independent manner in regulating many biological processes and developmental pathways in Arabidopsis. Summary statementComparison of transcriptome analyses between cop1 and spaQ mutants reveal overlapping pathways regulated by COP1 and SPAs.

plant biology↗

bioRxiv2024

Recruitment of SH3P2-DRP1A by HYCCIN2 Drives Membrane Tubulation in Arabidopsis Embryonic Cell Plate Formation

Cytokinesis marks the culmination of cell division when the cytoplasm undergoes division to yield two daughter cells. This intricate process encompasses various biological phenomena, including the organization of the cytoskeleton and the dynamics of vesicles. The development of the cell plate aligns with alterations in the cytoskeleton structure and vesicles derived from the trans-Golgi, ultimately resulting in the formation of the planar cell plate. Nevertheless, the coordination of these processes in plants remains to be determined. Here, we introduce HYCCIN-CONTAINING2 (HYC2) as a pivotal cytoskeleton cross-linking protein that accumulates at phragmoplasts and plays a crucial role in cell plate formation. A genetic study involving the depletion of HYC2 post-anaphase in Arabidopsis revealed HYC2s function in cell plate formation. HYC2 interacted with dynamin-related protein 1A (DRP1A) and SH3 domain-containing protein 2 (SH3P2), essential for membrane tubulation during cell plate formation. The recruitment of SH3P2 and DRP1A to the cell plate and phragmoplast organization was compromised in homozygous hyc2-2 mutant globular embryos. Our results shed light on the cytoskeletal function of HYC2 in assembling the cell plate, potentially by guiding vesicles containing SH3P2-DRP1A to the planar cell plate. Significance statementDuring plant cell division, a new membrane is constructed at the division plane, and vesicles fuse to form a transient compartment known as the cell plate. This structure undergoes stabilization via membrane remodeling events, including tubulation, to generate robust membrane intermediates. Apart from DRP1A and SH3P2, our understanding of other protein components involved in membrane remodeling still needs to be completed. HYC2 is a plant counterpart to the human Hyccin protein involved in neuronal myelination and displaying bundling capabilities for microtubules and microfilaments. Our study demonstrates that HYC2 is crucial in recruiting proteins during membrane tubulation in the cell plate formation in developing Arabidopsis embryos. These findings underscore the molecular function and vital role of the Hyccin protein in cell plate formation. One Sentence SummaryPlant HYC2 displays bundling capabilities for microtubules and microfilaments to recruit DRP1A and SH3P2 during membrane tubulation in the cell plate formation in developing Arabidopsis embryos.

plant biology↗

bioRxiv2023

A Citrullus genus super-pangenome reveals extensive variations in wild and cultivated watermelons and sheds light on watermelon evolution and domestication

Sweet watermelon (Citrullus lanatus subsp. vulgaris) is among the most important vegetable crops in the world. Wild relatives are important resources for watermelon breeding. Here we report high-quality reference genomes of three wild watermelons, C. mucosospermus, C. amarus and C. colocynthis, and the divergence and genome evolution of different Citrullus species. Using genomic data from 547 watermelon accessions spanning four Citrullus species, we construct a super-pangenome to represent the Citrullus gene repertoire and provide a catalogue of orthologous relationships among species. Gene presence/absence variation analysis uncovers many disease resistance genes that are missing in cultivated watermelons, as well as genes with significantly different occurrence frequencies between populations that might underlie watermelon evolution and domestication. We revisit watermelon domestication using the recently identified wild progenitor, Kordofan melon, which provides insights into the domestication of fruit bitterness, sweetness and flesh coloration. The Citrullus super-pangenome provides a valuable resource for breeding and biological discovery, and our comparative genomic analyses shed additional light on watermelon evolution and domestication.

plant biology↗

bioRxiv2024

Robotic Imaging and Machine Learning Analysis of Seed Germination: Dissecting the Influence of ABA and DOG1 on Germination Uniformity

Seed germination research has evolved over the years, increasingly incorporating technology. Recent advances in phenotyping platforms have increased the accessibility of high throughput phenotyping technologies to more labs, leading to valuable insights into germination biology. These platforms benefit researchers by limiting manual labor and increasing the temporal resolution of imaging. Each of the platforms developed presents unique benefits and challenges, from scalability to price to computing resources. Performing experiments involving thousands of seeds remains a daunting task due to the limitations of current phenotyping platforms and image analysis pipelines. To overcome these challenges, we introduce SPENCER (Seed Phenotype Evaluation and Germination Curve Estimation Robot), a high-throughput phenotyping platform. SPENCER accommodates 32 rectangular petri plates, capable of assessing up to 8000 Arabidopsis seeds per experiment. Our design allows for high quality images while maintaining optimal humidity, crucial for precise germination assessment over longer experiments. The image analysis workflow incorporates advanced image analysis using semantic segmentation models trained for Arabidopsis and lettuce, providing researchers with accessible, reproducible, and efficient tools. We applied SPENCER to investigate the relative roles of DELAY OF GERMINATION 1 (DOG1) and abscisic acid (ABA) in Arabidopsis dormancy. DOG1 mutants exhibited rapid germination, whereas ANT application had a greater impact on the slower-germinating Ler ecotype. Our findings suggest that DOG1 plays a significant role in dormancy, particularly in non-dormant accessions, while ABAs influence is more pronounced under stress conditions. Additionally, we explored germination uniformity, another agriculurally relevant trait, observing parallels with germination timing. SPENCER offers a powerful and accessible tool for dissecting complex biological traits in conjunction with chemical and genetic manipulations. Its scalability and versatility make it suitable for large-scale genetic and chemical germination screens.

plant biology↗

bioRxiv2025

From Synthetic to Biological Nitrification Inhibition: Advancing Stabilization of Organic Fertilizers

Fertilizer type plays a critical role in nitrogen (N) cycling, influencing nitrous oxide (N2O) emissions, soil mineral N dynamics, and microbial communities. Understanding these interactions is essential for developing sustainable fertilization strategies that balance agricultural productivity with environmental protection. This study examined the effects of mineral and organic fertilizers (OFs) on N transformations and evaluated the efficiency of the nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) in mitigating N2O losses. Results showed that OFs exhibited variable impacts on N2O emissions depending on their composition and C/N ratio. DMPP effectively reduced nitrification-driven N2O emissions, particularly in treatments with high ammoniacal N content. However, its efficiency was limited with animal-based OFs, suggesting a complex interaction between fertilizer properties and inhibitor effectiveness. DMPP had not direct impact on soil microbial diversity but specifically targeted the Nitrosomonaceae family and Nitrospira class. Beyond synthetic inhibitors, biological nitrification inhibition (BNI) emerged as a promising alternative, which we explored using rhizospheric soils from wheat landrace Persia 44 and white mustard (cv. Pole Position, Verdi). These soils significantly reduced N2O emissions, particularly when combined with OFs. The integration of BNI with organic fertilizers, especially liquid digestate, represents a promising strategy for reducing N losses while maintaining soil fertility. This study underscores the need for tailored fertilization strategies that combine chemical and biological tools to optimize N use efficiency and support environmentally sustainable agriculture.

plant biology↗

bioRxiv2017

Morphometrics Reveals Complex And Heritable Apple Leaf Shapes

Apple (Malus spp.) is a widely grown and valuable fruit crop. Leaf shape and size are important for flowering in apple and may also be early indicators for other agriculturally valuable traits. We examined 9,000 leaves from 869 unique apple accessions using linear measurements and comprehensive morphometric techniques. We identified allometric variation in the length-to-width aspect ratio between accessions and species of apple. The allometric variation was due to variation in the width of the leaf blade, not length. Aspect ratio was highly correlated with the primary axis of morphometric variation (PC1) quantified using elliptical Fourier descriptors (EFDs) and persistent homology (PH). While the primary source of variation was aspect ratio, subsequent PCs corresponded to complex shape variation not captured by linear measurements. After linking the morphometric information with over 122,000 genome-wide SNPs, we found high narrow-sense heritability values even at later PCs, indicating that comprehensive morphometrics can capture complex, heritable phenotypes. Thus, techniques such as EFDs and PH are capturing heritable biological variation that would be missed using linear measurements alone, and which could potentially be used to select for a hidden phenotype only detectable using comprehensive morphometrics.

plant biology↗