Biology subjects

Luebbert, C.

Publications and source records attributed to Luebbert, C..

6 recordsLinked to original sources

bioRxiv2026

Heritability of leaf stable carbon isotope signature in a diversity panel of the C4 plant Sorghum bicolor

A strategy to improve agricultural water productivity is to increase water use efficiency (WUE) at the level of plant transpiration through genetic selection. This requires detectable genetic variability in WUE and the ability to phenotype and select plants with higher WUE within a population. A proxy for phenotyping leaf level WUE by measuring carbon isotope signature ({delta}13Cleaf) has been supported by theory and data in C4 species. However, the functional relationship of {delta}13Cleaf and WUE in C4 species can be driven by genetics and environment. Therefore, a wide survey of existing natural variation is needed to quantify the heritability and identify various genetic factors that influence {delta}13Cleaf and WUE. In this study a genome-wide association panel was used to quantify the heritability of {delta}13Cleaf. We measured {delta}13Cleaf across a population of 360 genetically diverse lines of the C4 species Sorghum bicolor with single nucleotide polymorphic (SNP) markers determined from whole-genome resequencing. This analysis was conducted on two independent field environments where heritability of {delta}13Cleaf was evident and was driven by small genetic effects from loci that were consistently identified across environments. Candidate genes are presented that offer insights on future targets to manipulate and explore the functional relationship between {delta}13Cleaf and WUEi in C4 plants.

plant biology↗

bioRxiv2026

The carbon-nitrogen metabolism gene E1OGDH1 influences maize root system architecture and nitrogen plasticity

Synthetic nitrogen fertilizers have greatly increased crop yields, yet much of the applied nitrogen is lost from agroecosystems and contributes to environmental pollution and higher economic costs. Improving nitrogen uptake efficiency (NUpE) benefits from understanding how root system architecture (RSA) governs soil nitrogen capture. Although root traits have seldom been explicit breeding targets, selection for variation in above-ground nitrogen accumulation has also likely shaped differences in RSA. The Illinois Protein Strain Recombinant Inbred population, derived from more than a century of divergent selection for seed protein concentration, offers a powerful resource for dissecting RSA variation. Using multi-year field phenotyping of excavated root crowns and genome-wide association analysis, we identified a quantitative trait locus on chromosome 10 containing E1OGDH1, which encodes the E1 subunit of the 2-oxoglutarate dehydrogenase (OGDH) complex. OGDH performs a key step in the tricarboxylic acid cycle that also modulates 2-oxoglutarate, an important entry point into nitrogen metabolism and a co-factor for enzymes involved in hormone and secondary product synthesis. Long-read sequencing of inbreds derived from the divergent IHP and ILP parental populations revealed promoter polymorphisms defining E1OGDH1 alleles and differed in E1OGDH1 expression in root tissue. Field experiments in IPSRI lines carrying IHP- or ILP-associated E1OGDH1 alleles showed differences in root architectural traits over two years. CRISPR-Cas9 knockout mutants confirmed a functional role for E1OGDH1 in whole-plant performance and nitrogen-responsive root development. Mutants were shorter, had reduced biomass, and exhibited altered architectural responses to soil nitrogen levels. Transcriptome analysis further showed that loss of E1OGDH1 altered basal and nitrogen-responsive expression of genes associated with root development and nitrogen uptake and metabolism. Together, these findings identify E1OGDH1 as a strong candidate quantitative regulator of maize RSA and nitrogen plasticity, suggesting that central carbon-nitrogen metabolic genes can contribute to root developmental responses relevant to NUpE.

plant biology↗

bioRxiv2026

PanvaR: An R package for fine-mapping and visualizing results from genome-wide association studies

Genome-wide association studies (GWAS) use statistical models to correlate single nucleotide polymorphisms (SNPs) to a phenotype of interest. This scan of the entire genome identifies regions of association with a phenotype, but due to linkage disequilibrium (LD), GWAS on their own cannot identify single genes responsible for phenotypic variation. Rather, fine-mapping of GWAS regions is required, necessitating the use of additional tools and software. With the introduction of more pangenomic resources in a number of crops (Guo et al. 2025; Hufford et al. 2021), the fidelity of these fine-mapping efforts is growing, presenting the opportunity to leverage new information about allelic variation towards gene discovery (Shi et al. 2023; Della Coletta et al. 2021). Panvar is a tool developed to integrate existing software and resources to perform GWAS and fine-mapping in one seamless step. For each identified GWAS peak, panvaR outputs information about LD and SNP effect prediction for each SNP and by layering locations of nearby genes, creates a refined list of possible candidate genes. We have implemented Panvar as an R package, "panvaR", which runs the analysis functions, creates interactive and static visualizations, and outputs results tables. This tool seeks to bridge the gap between GWAS and gene speeding up an important step of quantitative genetic studies.

genomics↗

bioRxiv2025

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗

bioRxiv2023

A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to causative genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, only 5% of loci overlap genes known to control the ionome - indicating that many causal genes are still unknown. To identify candidates for the remaining 95% loci, we developed a method that uses GWAS studies across multiple species to identify conserved causative genes. By Filtering the Results of Multi-species, Analogous, GWAS Experiments (FiREMAGE) we were able to take the GWAS of 19 ionomic traits in Arabidopsis, soybean, rice, maize, and sorghum, and identify alleles affecting trait variation at conserved genes. Permutation testing demonstrated that GWAS loci affecting the same trait contained homologs more often than expected by chance. Most of the top 10% most significant conserved candidate sets encoded alleles in all five species, highlighting the conservation of ionomic genetic regulators in flowering plants. The candidates include proteins with known biochemical functions that regulate the ionome, validating the approach. In addition to genes with known functions, this approach also identified many conserved genes underlying GWAS loci affecting the same trait in multiple species that have no previously identified function in regulating the ionome, providing a path to discover the as yet unknown mechanisms of element accumulation in plants. This method enables the identification of conserved genes of previously unknown function via GWAS. Author summaryQuantitative genetics identifies a genomic region of interest but not the causal gene. We developed an approach to narrow these gene lists using genetic loci affecting elemental (i.e., calcium, iron, zinc) accumulation. Comparative genomics and GWAS demonstrates that alleles at evolutionarily conserved genes alter the same phenotype in multiple species. This produced a list of conserved candidate genes including previously known elemental regulators and genes whose elemental accumulation mechanism has yet to be determined. Combining datasets across species boosted the signal at these loci. This approach accelerates the discovery of new functional roles of genes.

genetics↗

bioRxiv2017

Simultaneous CRISPR/Cas9-mediated editing of cassava eIF4E isoforms nCBP-1 and nCBP-2 confers elevated resistance to cassava brown streak disease

Cassava brown streak disease (CBSD) is a major constraint on cassava yields in East and Central Africa and threatens production in West Africa. CBSD is caused by two species of positive sense RNA viruses belonging to the family Potyviridae, genus Ipomovirus: Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV). Diseases caused by the family Potyviridae require the interaction of viral genome-linked protein (VPg) and host eukaryotic translation initiation factor 4E (eIF4E) isoforms. Cassava encodes five eIF4E proteins: eIF4E, eIF(iso)4E-1, eIF(iso)4E-2, novel cap-binding protein-1 (nCBP-1), and nCBP-2. Protein-protein interaction experiments consistently found that VPg proteins associate with cassava nCBPs. CRISPR/Cas9-mediated genome editing was employed to generate ncbp-1, ncbp-2, and ncbp-1/ncbp-2 mutants in cassava cultivar 60444. Challenge with CBSV showed that ncbp-1/ncbp-2 mutants displayed delayed and attenuated CBSD aerial symptoms, as well as reduced severity and incidence of storage root necrosis. Suppressed disease symptoms were correlated with reduced virus titer in storage roots relative to wild-type controls. Our results demonstrate the ability to modify multiple genes simultaneously in cassava to achieve tolerance to CBSD. Future studies will investigate the contribution of remaining eIF4E isoforms on CBSD and translate this knowledge into an optimized strategy for protecting cassava from disease.

plant biology↗