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bioRxiv2022

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants

Predictive breeding is now widely practised in crop improvement programs and has accelerated selection response (i.e., the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increase further to meet the demands of the growing human population. The prediction of complex traits with current methods can be inconsistent across different genetic, environmental, and agronomic management contexts because the complex relationships between genomic and phenotypic variation are not well accounted for. Therefore, developing gene-to-phenotype network models for traits that integrate the knowledge of networks from systems biology, plant and crop physiology with population genomics has been proposed to close this gap in predictive modelling. Here, we develop a gene-to-phenotype network for shoot branching, a critical developmental pathway underpinning harvestable yield for many crop species, as a case study to explore the value of developing gene-to-phenotype networks to enhance understanding of selection responses. We observed that genetic canalization is an emergent property of the complex interactions among shoot branching gene-to-phenotype network components, leading to the accumulation of cryptic genetic variation, reduced selection responses, and large variation in selection trajectories across populations. As genetic canalization is expected to be pervasive in traits, such as grain yield, that result from interactions among multiple genes, traits, environments, and agronomic management practices, the need to model traits in crop improvement programs as outcomes of gene-to-phenotype networks is highlighted as an emerging opportunity to advance our understanding of selection response and the efficiency of developing resilient crops for future climates.

genetics↗

bioRxiv2026

Agrochemical-responsive gene expression control systems for modulating plant development and metabolism

Precise temporal control of gene expression is critical for studying plant biology and engineering complex crop traits. Current systems enable chemically inducible regulation but rely on costly or agriculturally impractical inducers and lack the flexibility needed to regulate combinations of native loci and transgenes. In this work, we elucidate the design rules for control systems, based on Cas9 and Cre recombinase fused to the ecdysone receptor (EcR), which respond to a widely used agrochemical methoxyfenozide (MF). First, we validated the function of both circuits in transient assays and explored how transduction properties can be modulated by engineering nuclear trafficking dynamics. We next characterized both the Cas9-based and recombinase-based systems by using them to regulate fluorescent reporters in transgenic Arabidopsis thaliana plants. Here, we demonstrate systemic activation following root application of MF, validating the use of an agriculturally compatible inducer for whole-plant gene regulation. Finally, we validated the utility of the inducible Cas-based SynTF system to regulate multigene pathways and control both metabolic flux and developmental circuits. Together, these results establish design principles for agrochemical-inducible control systems and demonstrate their utility for temporally regulating plant phenotypes. These synthetic circuits provide a versatile framework for engineering complex traits using an agriculturally compatible inducer.

synthetic biology↗

bioRxiv2021

Nectar bacteria stimulate pollen germination and bursting to enhance their fitness

For many flower visitors, pollen is the primary source of non-carbon nutrition, but pollen has physical defenses that make it difficult for consumers to access nutrients. Nectar-dwelling microbes are nearly ubiquitous among flowers and can reach high densities, despite the fact that floral nectar is nitrogen limited, containing only very low concentrations of non-carbon nutrients. Pollen contains trace micronutrients and high protein content but is protected by a recalcitrant outer shell. Here, we report that a common genus of nectar-dwelling bacteria, Acinetobacter, exploits pollen nutrition by inducing pollen germination and bursting. We use time course germination assays to quantify the effect of Acinetobacter species on pollen germination and pollen bursting. Inoculation with Acinetobacter species resulted in increased germination rates within 15 minutes, and bursting by 45 minutes, as compared to uninoculated pollen. The pollen germination and bursting phenotype is density-dependent, with lower concentrations of A. pollinis SCC477 resulting in a longer lag time before the spike in germination, which is then closely followed by a spike in bursting. Lastly, A. pollinis grows to nearly twice the density with germinable pollen vs ungerminable pollen, indicating that their ability to induce and exploit germination plays an important role in rapid growth. To our knowledge, this is the first direct test of non-plant biological induction of pollen germination, as well as the first evidence of induced germination as a method of nutrient procurement, as the microbes appear to hijack the pollens normally tightly controlled germination mechanisms for their benefit. Our results suggest that further study of microbe-pollen interactions may inform many aspects of pollination ecology, including microbial ecology in flowers, the mechanisms of pollinator nutrient acquisition from pollen, and cues of pollen germination for plant reproduction.

ecology↗

bioRxiv2021

Function of AtPGAP1 in GPI anchor lipid remodeling and transport to the cell surface of GPI-anchored proteins

GPI-anchored proteins (GPI-APs) play an important role in a variety of plant biological processes including growth, stress response, morphogenesis, signalling and cell wall biosynthesis. The GPI-anchor contains a lipid-linked glycan backbone that is synthesized in the endoplasmic reticulum (ER) where it is subsequently transferred to the C-terminus of proteins containing a GPI signal peptide by a GPI transamidase. Once the GPI anchor is attached to the protein, the glycan and lipid moieties are remodelled. In mammals and yeast, this remodelling is required for GPI-APs to be included in Coat Protein II (COPII) coated vesicles for their ER export and subsequent transport to the cell surface. The first reaction of lipid remodelling is the removal of the acyl chain from the inositol group by Bst1p (yeast) and PGAP1 (mammals). In this work, we have used a loss-of-function approach to study the role of PGAP1/Bst1 like genes in plants. We have found that Arabidopsis PGAP1 localizes to the ER and probably functions as the GPI inositol-deacylase which cleaves the acyl chain from the inositol ring of the GPI anchor. In addition, we show that PGAP1 function is required for efficient ER export and transport to the cell surface of GPI-APs. One sentence summaryGPI anchor lipid remodeling in GPI-anchored proteins is required for their transport to the cell surface in Arabidopsis.

cell biology↗

bioRxiv2023

Modeling soybean growth: A mixed model approach

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics. Author summaryNonlinear models are useful for modeling animal and plant growth; however, their parameters are influenced by both genetic and environmental factors. If the same model can be applied to data with different genetic and environmental factors by allowing parameter variations, it can be used to understand, predict, and control the genetic and environmental influences of growth models based on parameter variation. In this study, we propose a statistical method based on nonlinear mixed-effects modeling. The simulation and real data analysis results show that the proposed method was effective in modeling the growth of genetically different soybean varieties under different drought conditions. The usefulness of the proposed method is expected to increase, as high-throughput measurements provide growth data for a large number of genotypes in various environments.

genetics↗

bioRxiv2018

Tracking transient fluorescent events in structured point clouds

BackgroundParticle-tracking in 3D is an indispensable computational tool to extract critical information on dynamical processes from raw time-lapse imaging. This is particularly true with in vivo time-lapse fluorescence imaging in cell and developmental biology, where complex dynamics are observed at high temporal resolution. Common tracking algorithms used with time-lapse data in fluorescence microscopy typically assume a continuous signal where background, recognisable keypoints and independently moving objects of interest are permanently visible. Under these conditions, simple registration and identity management algorithms can track the objects of interest over time. In contrast, here we consider the case of transient signals and objects whose movements are constrained within a tissue, where standard algorithms fail to provide robust tracking. ResultsTo optimize 3D tracking in these conditions, we propose the merging of registration and tracking tasks into a fuzzy registration algorithm to solve the identity management problem. We describe the design and application of such an algorithm, illustrated in the domain of plant biology, and make it available as an open-source software implementation. The algorithm is tested on mitotic events in 4D data-sets obtained with light-sheet fluorescence microscopy on growing Arabidopsis thaliana roots expressing CYCB::GFP. We validate the method by comparing the algorithm performance against both surrogate data and manual tracking. ConclusionThis method fills a gap in existing tracking techniques, following mitotic events in challenging data-sets using transient fluorescent markers in unregistered images.

bioinformatics↗

bioRxiv2026

From Parametric Guessing to Graph-Grounded Answers: Building Reliable ChatGPT-like tools for Plant Science

Large language models (LLMs) are increasingly used by plant biologists to summarize literature, generate hypotheses, and interpret experimental results. However, LLMs are unreliable sources of exhaustive, source-attributed facts, a critical limitation for the list-style queries that pervade plant biology (e.g., "list all transcription factors regulating secondary cell wall (SCW) biosynthesis in Arabidopsis"). Here, we query ChatGPT, Claude, and Gemini with such queries and demonstrate that none return complete gene lists with reliable citations. We trace these failures to how LLMs store knowledge: as statistical patterns distributed across billions of internal parameters, with no mechanism to guarantee completeness, provenance, or reproducibility. We also review fine-tuning mitigation strategies, including multi-task instruction tuning, parameter-efficient methods, and context engineering, that alleviate but do not resolve these limitations. We then discuss retrieval-augmented generation (RAG), which feeds relevant documents to the LLM at query time, and argue that while it improves source attribution, it remains impractical when answers require synthesizing information scattered across hundreds of papers. As an alternative, we advocate graph retrieval-augmented generation (GraphRAG), in which the LLM serves as a reasoning and language interface over a structured, provenance-linked knowledge graph (KG) that returns complete result sets reproducibly. We outline a practical GraphRAG architecture and survey existing plant KG resources. Finally, we discuss open challenges, including entity disambiguation, relation normalization and evidence grading, and propose a roadmap for building open, continuously updated plant KGs that can turn "read 1,000 papers" into a single reproducible query.

bioinformatics↗

bioRxiv2025

Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning

Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs), which interpret cis-regulatory DNA sequences to determine the timing and location of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs and identifying transcription factor binding motifs (TFBMs) for spatio-temporal gene expression remain major challenges in plant biology. To decipher the conditional networks governing TF-Target gene interactions, we developed CTF-BIND, a novel computational framework designed to reason about the spatio-temporal dynamics of TF activity. Leveraging over [~]23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction data), we constructed Bayesian causal networks capable of explaining TF activity across diverse conditions. These networks, validated against extensive experimental data, were then integrated into a Graph Transformer deep learning system. This system uses expression information of network components to quantitatively determine TF activity levels. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, eliminating the need for separate ChIP-seq experiments. CTF-BIND achieved a high average accuracy of [~]93% when tested against experimentally established data from various conditions. It is implemented as an interactive, open-access web server, it not only provides TF binding profiles but also facilitates downstream functional analysis. Furthermore, we developed CTF-BIND-DB, (https://hichicob.ihbt.res.in/ctfbind/) a database capturing dynamic shifts in regulatory pathways, providing information on TGs, network ontology, and binding motifs. CTF-BIND and CTF-BIND-DB represent a transformative approach for understanding and determining TF activity in plant stress responses, offering a powerful tool for crop improvement and bypassing the limitations of traditional methods and extensive experimental validation.

bioinformatics↗

bioRxiv2019

Efficient data management infrastructure for the integration of imaging and omics data in life science research

As technical developments in omics and biomedical imaging drive the increase in quality, modality, and throughput of data generation in life sciences, the need for information systems capable of integrative, long-term storage and management of these heterogeneous digital assets is also increasing. Here, we propose an approach based on principles of Service Oriented Architecture design, to allow the integrated management and analysis of multi-omics and biomedical imaging data. The proposed architecture introduces an interoperable image management system, the OMERO server, into the backend of qPortal, a FAIR-compliant web-based platform for omics data management. The implementation of an integrated metadata model, the development of software components to enable the communication with the OMERO server, and an extension to the data management operations of established software, allows for FAIR management of heterogeneous omics and biomedical imaging data within an integrated system, which facilitates metadata queries from web-based scientific applications. The applicability of the proposed architecture is demonstrated in two prototypical use cases, a plant biology study using confocal scanning microscopy, and a clinical study on hepatocellular carcinoma, with data from a variety of medical imaging and omics modalities. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, as the joint analysis of omics and imaging data becomes not only desirable but necessary to derive novel insights into biological processes. In particular for powerful machine learning applications where the availability of large datasets with high quality phenotypic annotations is a requirement.

bioinformatics↗

bioRxiv2020

Single-cell transcriptome analyses recapitulate the cellular and developmental responses to abiotic stresses in rice

The metabolism and reproduction of plants depend on the division of labors among cells. However, cells with various functions are often studied as a bulk where their specificities were diluted. Here, we apply single-cell RNA sequencing to the aerial part of rice seedlings growing in various environments. We capture the transcriptomes of thousands of cells, identify all major cell types, and reconstruct their developmental trajectories. We find that abiotic stresses not only affect gene expression in a cell-type-specific manner but also have impacts on the physical size of cells and the composition of cell populations. We validate some of these conclusions with microscopy and provide developmental mechanisms with computational analyses. Collectively, our study represents a benchmark-setting data resource of single-cell transcriptome atlas in rice and an illustration of exploiting such resource to drive discoveries in plant biology.

genomics↗

bioRxiv2020

Development of transient expression assay for Cannabis sativa which revealed differential Agrobacterium susceptibility among cannabis cultivars

In plant biology, transient expression analysis plays a vital role to provide a fast method to study the gene of interest and subsequently leads the path to develop an improved crop variety with better agronomic traits. In this study, we have reported a rapid and efficient method for transient expression in Cannabis sativa seedlings using Agrobacterium tumefaciens-mediated transformation. A. tumefaciens strain EHA105 carrying the pCAMBIA1301 construct with uidA gene was used to transform cannabis seedlings and the GUS assay was used to detect the uidA expression. A 1% hydrogen peroxide (H2O2) solution was used for both seed sterilization and rapid germination steps. Transient transformation revealed that both cotyledons and young true leaves are amenable to transformation. Comparison to Nicotiana tabacum (tobacco) showed that cannabis seedlings were less susceptible to transformation with Agrobacterium tumefaciens. The susceptibility to Agrobacterium infection also varied with the different cannabis cultivars. The method established in this study has potential to be an important tool for gene-function studies and genetic improvement in cannabis.

molecular biology↗

bioRxiv2020

Hybrid origin of Populus tomentosa Carr. identified through genome sequencing and phylogenomic analysis

Populus tomentosa is widely distributed and cultivated in the Northern and Central China, where it is of great economic and ecological importance. However, the origin of P. tomentosa remains controversial. Here, we used a PacBio+Hi-C+Illumina strategy to sequence and assemble its 740.2 Mb (2n) genome. The assembly accounts for greater than 92.1% of the 800-megabase genome, comprises 38 chromosomes, and contains 59,124 annotated protein-coding genes. Phylogenomic analyses elucidated dynamic genome evolution events among its closely related white poplars, and revealed that tomentosa is comprised of two subgenomes, which we deomonstrate is likely to have resulted from hybridization between Populus adenopoda as the female, and Populus alba var. pyramidalis as the male, around 3.93 Mya. We also detected structural variations and allele-indels across genome. Our study presents a high quality and well assembled genome, unveils the origin of the widely distributed and planted P. tomentosa, and provides a powerful resource for comparative plant biology, breeding, and biotechnology.

genomics↗

bioRxiv2020

Pocket MUSE: an affordable, versatile and high performance fluorescence microscope using a smartphone

Smartphone microscopes can be effective tools for a broad range of imaging applications. In this manuscript, we demonstrate the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone and enables high quality multichannel fluorescence microscopy with submicron resolution over a 10X equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.

bioengineering↗

bioRxiv2020

Dysfunction of the histone demethylase IBM1 in Arabidopsis reshapes the root-associated bacteria community

Root-associated bacteria communities are influential to plant growth and stress tolerance. Epigenetic regulation plays important roles in many plant biological processes, but its potential impacts on the assembly of root microbiota remain unclear. Here we report that dysfunction of the histone demethylase IBM1 in Arabidopsis substantially alters root-associated soil bacteria community. We compared two alleles of ibm1 mutant (ibm1-1 and ibm1-4) with wild type Arabidopsis regarding the root-associated bacteria community by 16S rRNA gene sequencing. The constrained principal coordinate analysis (PCoA) showed that the ibm1 mutants are both clearly separated from Col-0 along the major coordinates. Among the 29 families which have a relative abundance more than 0.5% in at least one sample, 10 and 11 families were commonly affected by ibm1-1 and ibm1-4 alleles in the rhizosphere and the endosphere compartment, respectively. Notably, the ACMs (Abundant Community members) belonging Pseudomonadaceae showed increased relative abundance in the ibm1 mutant alleles compared to Col-0 in both the rhizosphere and the endosphere compartments. The ACMs belonging to Oxalobacteraceae mostly showed decreased relative abundance in ibm1 mutants compared to Col-0 in endosphere compartment. These findings demonstrate an influential function of IBM1-mediated epigenetic regulation in shaping the root-associated microbiota.

microbiology↗

bioRxiv2021

VAL genes regulate vegetative phase change via miR156-dependent and independent mechanisms

How organisms control when to transition between different stages of development is a key question in biology. In plants, epigenetic silencing by Polycomb repressive complex 1 (PRC1) and PRC2 plays a crucial role in promoting developmental transitions, including from juvenile-to-adult phases of vegetative growth. PRC1/2 are known to repress the master regulator of vegetative phase change, miR156, leading to the transition to adult growth, but how this process is regulated temporally is unknown. Here we investigate whether transcription factors in the VIVIPAROUS/ABI3-LIKE (VAL) gene family provide the temporal signal for the epigenetic repression of miR156. Exploiting a novel val1 allele, we found that VAL1 and VAL2 redundantly regulate vegetative phase change by controlling the overall level, rather than temporal dynamics, of miR156 expression. Furthermore, we discovered that VAL1 and VAL2 also act independently of miR156 to control this important developmental transition.

plant biology↗

bioRxiv2021

The temperature-regulated DEAD-box RNA helicase CrhR interactome: Autoregulation and photosynthesis-related transcripts

RNA helicases play crucial functions in RNA biology. In plants, RNA helicases are encoded by large gene families, performing roles in abiotic stress responses, development, the post-transcriptional regulation of gene expression as well as house-keeping functions. Several of these RNA helicases are targeted to the organelles, mitochondria and chloroplasts. Cyanobacteria are the direct evolutionary ancestors of plant chloroplasts. The cyanobacterium Synechocystis 6803 encodes a single DEAD-box RNA helicase, CrhR, that is induced by a range of abiotic stresses, including low temperature. Though the {Delta}crhR mutant exhibits a severe cold-sensitive phenotype, the physiological function(s) performed by CrhR have not been described. To identify transcripts interacting with CrhR, we performed RNA co-immunoprecipitation with extracts from a Synechocystis crhR deletion mutant expressing the FLAG-tagged native CrhR or a K57A mutated version with an anticipated enhanced RNA binding. The composition of the interactome was strikingly biased towards photosynthesis-associated and redox-controlled transcripts. A transcript highly enriched in all experiments was the crhR mRNA, suggesting an auto-regulatory molecular mechanism. The identified interactome explains the described physiological role of CrhR in response to the redox poise of the photosynthetic electron transport chain and characterizes CrhR as an enzyme with a diverse range of transcripts as molecular targets. HighlightThe cyanobacterial DEAD-box RNA helicase CrhR binds mainly photosynthesis-associated and redox-controlled transcripts connecting its regulation, localization and phenotypes of mutants for the first time with a set of potential RNA targets.

plant biology↗

bioRxiv2021

Assessing the Effectiveness of Mycorrhizal Inoculation on the Biochemical Composition of the Different Species of Echinacea

The information regarding the effect of the mycorrhizal inoculation on different Echinacea species is not available in detail. Therefore, here we determined the changes in the biochemical composition of echinacea as a result of mycorrhizal inoculation. This experiment was undertaken to assess the effect of the mycorrhizal association on biochemical properties of different echinacea species (E. angustifolia,E. purpurea, E. pallida). Here various echinacea species were inoculated with mycorrhiza to examine the species richness in different traits. The results established that biological traits (plant dry matter, chlorophyll content, carotenoid, N content, P content, K content) and physiological and mycorrhization characteristics (Root essential oil, ABTS Antioxidant, Ferric-Reducing Antioxidant, Total phenolic, AM Spore No., AM Root Colonization) both are higher under mycorrhizal association than the control plants of different echinacea species. E. purpurea showed greater results than the E. angustifolia and E. pallida. Among biochemical properties chlorophyll content, carotenoid and N, P, K were significantly higher underE. Purpurea than the E. angustifolia and E.pallida. Total dry matter was higher under E. angustifolia (49.23 g) and minimum dry matter was found under E. pallida (40.07 g). Physiological and mycorrhizal traits were significantly higher under E. purpurea than the other species. E. purpurea showed higher AM Spore No., AM Root Colonization 231.30, 78.70% respectively. Lowest physiological and mycorrhization characteristics were found under E. pallida. The result of mycorrhizal association was very effective for plant growth and increased bio-physicochemical properties than the control plants.

plant biology↗

bioRxiv2021

Sequence characterization of eccDNA content in glyphosate sensitive and resistant Palmer amaranth from geographically distant populations

The discovery of non-chromosomal circular DNA offers new directions in linking genome structure with function in plant biology. Glyphosate resistance through EPSPS gene copy amplification in Palmer amaranth was due to an autonomously replicating extra-chromosomal circular DNA mechanism (eccDNA). CIDER-Seq analysis of geographically distant glyphosate sensitive (GS) and resistant (GR) Palmer Amaranth (Amaranthus palmeri) revealed the presence of numerous small extra-chromosomal circular DNAs varying in size and with degrees of repetitive content, coding sequence, and motifs associated with autonomous replication. In GS biotypes, only a small portion of these aligned to the 399 kb eccDNA replicon, the vehicle underlying gene amplification and genetic resistance to the herbicide glyphosate. The aligned eccDNAs from GS were separated from one another by large gaps in sequence. In GR biotypes, the eccDNAs were present in both abundance and diversity to assemble into a nearly complete eccDNA replicon. Mean sizes of eccDNAs were similar in both biotypes and were around 5kb with larger eccDNAs near 25kb. Gene content for eccDNAs ranged from 0 to 3 with functions that include ribosomal proteins, transport, metabolism, and general stress response genetic elements. Repeat content among smaller eccDNAs indicate a potential for recombination into larger structures. Genomic hotspots were also identified in the Palmer amaranth genome with a disposition for gene focal amplifications as eccDNA. The presence of eccDNA may serve as a reservoir of genetic heterogeneity in this species and may be functionally important for survival.

genomics↗