The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower.

Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance.

GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data.

Premise: The functional annotation of genes is a crucial component of genomic analyses. A common way to summarize functional annotations is with hierarchical gene ontologies, such as the Gene Ontology (GO) Resource. GO includes information about the cellular location, molecular function(s), and products/processes that genes produce or are involved in. For a set of genes, summarizing GO annotations using pre-defined, higher-order terms (GO slims) is often desirable in order to characterize the overall function of the data set, and it is impractical to do this manually. Methods and Results: The GOgetter pipeline consists of bash and Python scripts. From an input FASTA file of nucleotide gene sequences, it outputs text and image files that list (1) the best hit for each input gene in a set of reference gene models, (2) all GO terms and annotations associated with those hits, and (3) a summary and visualization of GO slim categories for the data set. These output files can be queried further and analyzed statistically, depending on the downstream need(s). Conclusions: GO annotations are a widely used "universal language" for describing gene functions and products. GOgetter is a fast and easy-to-implement pipeline for obtaining, summarizing, and visualizing GO slim categories associated with a set of genes.

Phenotypic and transcriptomic responses of cultivated sunflower seedlings (Helianthus annuus L.) to four abiotic stresses.

Plants encounter and respond to numerous abiotic stresses during their lifetimes. These stresses are often related and could therefore elicit related responses. There are, however, relatively few detailed comparisons between multiple different stresses at the molecular level. Here, we investigated the phenotypic and transcriptomic response of cultivated sunflower (Helianthus annuus L.) seedlings to three water-related stresses (i.e., dry-down, an osmotic challenge, and salt stress), as well as a generalized low-nutrient stress. All four stresses negatively impacted seedling growth, with the nutrient stress having a more divergent response from control as compared to the water-related stresses. Phenotypic responses were consistent with expectations for growth in low-resource environments, including increased (i.e., less negative) carbon fractionation values and leaf C:N ratios, as well as increased belowground biomass allocation. The number of differentially expressed genes (DEGs) under stress was greater in leaf tissue, but roots exhibited a higher proportion of DEGs unique to individual stresses. Overall, the three water-related stresses had a more similar transcriptomic response to each other vs. nutrient stress, though this pattern was more pronounced in root vs. leaf tissue. In contrast to our DEG analyses, co-expression network analysis revealed that there was little indication of a shared response between the four stresses in despite the majority of DEGs being shared between multiple stresses. Importantly, osmotic stress, which is often used to simulate drought stress in experimental settings, had little transcriptomic resemblance to true water limitation (i.e., dry-down) in our study, calling into question its utility as a means for simulating drought.

Key Traits and Genes Associate with Salinity Tolerance Independent from Vigor in Cultivated Sunflower.

With rising food demands, crop production on salinized lands is increasingly necessary. Sunflower (Helianthus annuus), a moderately salt-tolerant crop, exhibits a tradeoff where more vigorous, high-performing genotypes have a greater proportional decline in biomass under salinity stress. Prior research has found deviations from this relationship across genotypes. Here, we identified the traits and genomic regions underlying variation in this expectation-deviation tolerance (the magnitude and direction of deviations from the expected effect of salinity). We grew a sunflower diversity panel under control and salt-stressed conditions and measured a suite of morphological (growth, mass allocation, plant and leaf morphology) and leaf ionomic traits. The genetic basis of variation and plasticity in these traits was investigated via genome-wide association, which also enabled the identification of genomic regions (i.e. haplotypic blocks) influencing multiple traits. We found that the magnitude and direction of plasticity in whole-root mass fraction, fine root mass fraction, and chlorophyll content, as well as leaf sodium and potassium content under saline conditions, were most strongly correlated with expectation-deviation tolerance. We identified multiple genomic regions underlying these traits as well as a single alpha-mannosidase gene directly associated with this tolerance metric. Our results show that, by taking the vigor-salinity effect tradeoff into account, we can identify unique traits and genes associated with salinity tolerance. Since these traits and genomic regions are distinct from those associated with high vigor (i.e. growth in benign conditions), they provide an avenue for increasing salinity tolerance in high-performing sunflower genotypes without compromising vigor.

Sunflower pan-genome analysis shows that hybridization altered gene content and disease resistance.

Domesticated plants and animals often display dramatic responses to selection, but the origins of the genetic diversity underlying these responses remain poorly understood. Despite domestication and improvement bottlenecks, the cultivated sunflower remains highly variable genetically, possibly due to hybridization with wild relatives. To characterize genetic diversity in the sunflower and to quantify contributions from wild relatives, we sequenced 287 cultivated lines, 17 Native American landraces and 189 wild accessions representing 11 compatible wild species. Cultivar sequences failing to map to the sunflower reference were assembled de novo for each genotype to determine the gene repertoire, or 'pan-genome', of the cultivated sunflower. Assembled genes were then compared to the wild species to estimate origins. Results indicate that the cultivated sunflower pan-genome comprises 61,205 genes, of which 27% vary across genotypes. Approximately 10% of the cultivated sunflower pan-genome is derived through introgression from wild sunflower species, and 1.5% of genes originated solely through introgression. Gene ontology functional analyses further indicate that genes associated with biotic resistance are over-represented among introgressed regions, an observation consistent with breeding records. Analyses of allelic variation associated with downy mildew resistance provide an example in which such introgressions have contributed to resistance to a globally challenging disease.

Multiple genomic regions influence root morphology and seedling growth in cultivated sunflower (Helianthus annuus L.) under well-watered and water-limited conditions.

With climate change and an ever-increasing human population threatening food security, developing a better understanding of the genetic basis of crop performance under stressful conditions has become increasingly important. Here, we used genome-wide association studies to genetically dissect variation in seedling growth traits in cultivated sunflower (Helianthus annuus L.) under well-watered and water-limited (i.e., osmotic stress) conditions, with a particular focus on root morphology. Water limitation reduced seedling size and produced a shift toward deeper rooting. These effects varied across genotypes, and we identified 13 genomic regions that were associated with traits of interest across the two environments. These regions varied in size from a single marker to 186.2 Mbp and harbored numerous genes, some of which are known to be involved in the plant growth/development as well as the response to osmotic stress. In many cases, these associations corresponded to growth traits where the common allele outperformed the rare variant, suggesting that selection for increased vigor during the evolution of cultivated sunflower might be responsible for the relatively high frequency of these alleles. We also found evidence of pleiotropy across multiple traits, as well as numerous environmentally independent genetic effects. Overall, our results indicate the existence of genetic variation in root morphology and allocation and further suggest that the majority of alleles associated with these traits have consistent effects across environments.

Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants.

Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (Arabidopsis thaliana, Glycine max, Oryza sativa, Populus spp., Solanum lycopersicum, Vitis spp., and Zea mays) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., "hub") genes having significantly lower nonsynonymous substitution rates and dN/dS ratios compared to less highly connected (i.e., "peripheral") genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.

A target enrichment method for gathering phylogenetic information from hundreds of loci: An example from the Compositae.

PREMISE OF THE STUDY: The Compositae (Asteraceae) are a large and diverse family of plants, and the most comprehensive phylogeny to date is a meta-tree based on 10 chloroplast loci that has several major unresolved nodes. We describe the development of an approach that enables the rapid sequencing of large numbers of orthologous nuclear loci to facilitate efficient phylogenomic analyses. • METHODS AND RESULTS: We designed a set of sequence capture probes that target conserved orthologous sequences in the Compositae. We also developed a bioinformatic and phylogenetic workflow for processing and analyzing the resulting data. Application of our approach to 15 species from across the Compositae resulted in the production of phylogenetically informative sequence data from 763 loci and the successful reconstruction of known phylogenetic relationships across the family. • CONCLUSIONS: These methods should be of great use to members of the broader Compositae community, and the general approach should also be of use to researchers studying other families.