Conserving a threatened North American walnut: a chromosome-scale reference genome for butternut (Juglans cinerea).

With the advent of affordable and more accurate third-generation sequencing technologies, and the associated bioinformatic tools, it is now possible to sequence, assemble, and annotate more species of conservation concern than ever before. Juglans cinerea, commonly known as butternut or white walnut, is a member of the walnut family, native to the Eastern United States and Southeastern Canada. The species is currently listed as Endangered on the IUCN Red List due to decline from an invasive fungus known as Ophiognomonia clavigignenti-juglandacearum (Oc-j) that causes butternut canker. Oc-j creates visible sores on the trunks of the tree which essentially starves and slowly kills the tree. Natural resistance to this pathogen is rare. Conserving butternut is of utmost priority due to its critical ecosystem role and cultural significance. As part of an integrated undergraduate and graduate student training program in biodiversity and conservation genomics, the first reference genome for Juglans cinerea is described here. This chromosome-scale 539 Mb assembly was generated from over 100 × coverage of Oxford Nanopore long reads and scaffolded with the Juglans mandshurica genome. Scaffolding with a closely related species oriented and ordered the sequences in a manner more representative of the structure of the genome without altering the sequence. Comparisons with sequenced Juglandaceae revealed high levels of synteny and further supported J. cinerea's recent phylogenetic placement. Comparative assessment of gene family evolution revealed a significant number of contracting families, including several associated with biotic stress response.

Admixture mapping and selection scans identify genomic regions associated with stomatal patterning and disease resistance in hybrid poplars.

Variation in fitness components can be linked in some cases to variation in key traits. Metric traits that lie at the intersection of development, defense, and ecological interactions may be expected to experience environmental selection, informing our understanding of evolutionary and ecological processes. Here, we use quantitative genetic and population genomic methods to investigate disease dynamics in hybrid and non-hybrid populations. We focus our investigation on morphological and ecophysiological traits which inform our understanding of physiology, growth, and defense against a pathogen. In particular, we investigate stomata, microscopic pores on the surface of a leaf that regulate gas exchange during photosynthesis and are sites of entry for various plant pathogens. Stomatal patterning traits were highly predictive of disease risk. Admixture mapping identified a polygenic basis of disease resistance. Candidate genes for stomatal and disease resistance map to the same genomic regions and experienced positive selection. Genes with functions to guard cell homeostasis, the plant immune system, components of constitutive defenses, and growth-related transcription factors were identified. Our results indicate positive selection acted on candidate genes for stomatal patterning and disease resistance, potentially acting in concert to structure their variation in naturally formed backcrossing hybrid populations.

Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes.

Premise: Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein-coding gene predictions. Methods: The impact of repeat masking, long-read and short-read inputs, and de novo and genome-guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity. Results: Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono-exonic/multi-exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA-read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence-based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome-guided transcriptome assemblies, or full-length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post-processing with functional and structural filters is highly recommended. Discussion: While the annotation of non-model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions.

Growth-defense trade-offs masked in unadmixed populations are revealed by hybridization.

Organisms are constantly challenged by pathogens and pests, which can drive the evolution of growth-defense strategies. Plant stomata are essential for gas exchange during photosynthesis and conceptually lie at the intersection of the physiological demands of growth and exposure to foliar fungal pathogens. Generations of natural selection for locally adapted growth-defense strategies can eliminate variation between traits, potentially masking trade-offs and selection conflicts that may have existed in the past. Hybrid populations offer a unique opportunity to reset the clock on selection and to study potentially maladaptive trait variation before selection removes it. We study the interactions of growth, stomatal, ecopysiological, and disease resistance traits in poplars (Populus) after infection by the leaf rust Melampsora medusae. Phenotypes were measured in a common garden and genotyped at 227K SNPs. We isolate the effects of hybridization on trait variance, discover correlations between stomatal, ecophysiology, and disease resistance, examine trade-offs and selection conflicts, and explore the evolution of growth-defense strategies potentially mediated by selection for stomatal traits on the upper leaf surface. These results suggest an important role for stomata in determining growth-defense strategies in organisms susceptible to foliar pathogens, and reinforces the contribution of hybridization studies toward our understanding of trait evolution.

StomataCounter: a neural network for automatic stomata identification and counting.

Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user-friendly, fully automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo scanning electron microscropy micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.