Identifying potential dietary treatments for inherited metabolic disorders using Drosophila nutrigenomics.

Inherited metabolic disorders are a group of genetic conditions that can cause severe neurological impairment and child mortality. Uniquely, these disorders respond to dietary treatment; however, this option remains largely unexplored because of low disorder prevalence and the lack of a suitable paradigm for testing diets. Here, we screened 35 Drosophila amino acid disorder models for disease-diet interactions and found 26 with diet-altered development and/or survival. Using a targeted multi-nutrient array, we examine the interaction in a model of isolated sulfite oxidase deficiency, an infant-lethal disorder. We show that dietary cysteine depletion normalizes their metabolic profile and rescues development, neurophysiology, behavior, and lifelong fly survival, thus providing a basis for further study into the pathogenic mechanisms involved in this disorder. Our work highlights the diet-sensitive nature of metabolic disorders and establishes Drosophila as a valuable tool for nutrigenomic studies for informing potential dietary therapies.

Heterosis in crop improvement.

Heterosis, also known as hybrid vigor, is the phenomenon wherein a progeny exhibits superior traits relative to one or both parents. In terms of crop breeding, this usually refers to the yield advantage of F1 hybrids over both inbred parents. The development of high-yielding hybrid cultivars across a wider range of crops is key to meeting future food demands. However, conventional hybrid breeding strategies are proving to be exceptionally challenging to apply commercially in many self-pollinating crops, particularly wheat and barley. Currently in these crops, the relative performance advantage of hybrids over inbred line cultivars does not outweigh the cost of hybrid seed production. Here, we review the genetic basis of heterosis, discuss the challenges in hybrid breeding, and propose a strategy to recruit multiple heterosis-associated genes to develop lines with improved agronomic characteristics. This strategy leverages modern genetic engineering tools to synthesize supergenes by fusing multiple heterotic alleles across multiple heterosis-associated loci. We outline a plan to assess the feasibility of this approach to improve line performance using barley (Hordeum vulgare) as the model self-pollinating crop species, and a few heterosis-associated genes. The proposed method can be applied to all crops for which heterotic gene combinations can be identified.

Compare_Genomes: A Comparative Genomics Workflow to Streamline the Analysis of Evolutionary Divergence Across Eukaryotic Genomes.

The dawn of cost-effective genome assembly is enabling deep comparative genomics to address fundamental evolutionary questions by comparing the genomes of multiple species. However, comparative genomics analyses frequently deploy multiple, often purpose-built frameworks, limiting their transferability and replicability. Here, we present compare_genomes, a transferable and extensible comparative genomics workflow package we developed that streamlines the identification of orthologous families within and across eukaryotic genomes and tests for the presence of several mechanisms of evolution (gene family expansion or contraction and substitution rates within protein-coding sequences). The workflow is available for Linux, written as a Nextflow workflow that calls established genomics and phylogenetics tools to streamline the analysis and visualization of eukaryotic genome divergence. This workflow is freely available at https://github.com/jeffersonfparil/compare_genomes, distributed under the GNU General Public License version 3 (GPLv3). © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Comparative genomics with Nextflow and Conda.

Rounding up the annual ryegrass genome: High-quality reference genome of Lolium rigidum.

The genome of the major agricultural weed species, annual ryegrass (Lolium rigidum) was assembled, annotated and analysed. Annual ryegrass is a major weed in grain cropping, and has the remarkable capacity to evolve resistance to herbicides with various modes of action. The chromosome-level assembly was achieved using short- and long-read sequencing in combination with Hi-C mapping. The assembly size is 2.44 Gb with N50 = 361.79 Mb across 1,764 scaffolds where the seven longest sequences correspond to the seven chromosomes. Genome completeness assessed through BUSCO returned a 99.8% score for complete (unique and duplicated) and fragmented genes using the Viridiplantae set. We found evidence for the expansion of herbicide resistance-related gene families including detoxification genes. The reference genome of L. rigidum is a critical asset for leveraging genetic information for the management of this highly problematic weed species.

Adaptive significance of flowering time variation across natural seasonal environments in Arabidopsis thaliana.

The relevance of flowering time variation and plasticity to climate adaptation requires a comprehensive empirical assessment. We investigated natural selection and the genetic architecture of flowering time in Arabidopsis through field experiments in Europe across multiple sites and seasons. We estimated selection for flowering time, plasticity and canalization. Loci associated with flowering time, plasticity and canalization by genome-wide association studies were tested for a geographic signature of climate adaptation. Selection favored early flowering and increased canalization, except at the northernmost site, but was rarely detected for plasticity. Genome-wide association studies revealed significant associations with flowering traits and supported a substantial polygenic inheritance. Alleles associated with late flowering, including functional FRIGIDA variants, were more common in regions experiencing high annual temperature variation. Flowering time plasticity to fall vs spring and summer environments was associated with GIGANTEA SUPPRESSOR 5, which promotes early flowering under decreasing day length and temperature. The finding that late flowering genotypes and alleles are associated with climate is evidence for past adaptation. Real-time phenotypic selection analysis, however, reveals pervasive contemporary selection for rapid flowering in agricultural settings across most of the species range. The response to this selection may involve genetic shifts in environmental cuing compared to the ancestral state.

Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations.

Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

instaGraminoid, a Novel Colorimetric Method to Assess Herbicide Resistance, Identifies Patterns of Cross-Resistance in Annual Ryegrass.

Herbicide resistance in agricultural weeds is a global problem with an increasing understanding that it is caused by multiple genes leading to quantitative resistance. These quantitative patterns of resistance are not easy to decipher with mortality assays alone, and there is a need for straightforward and unbiased protocols to accurately assess quantitative herbicide resistance. instaGraminoid-a computer vision and statistical analysis package-was developed as an automated and scalable method for quantifying herbicide resistance. The package was tested in rigid ryegrass (Lolium rigidum), the most noxious and highly resistant weed in Australia and the Mediterranean region. This method provides quantitative measures of the degree of chlorosis and necrosis of individual plants which was shown to accurately reflect herbicide resistance. We were able to reliably characterise resistance to four herbicides with different sites of action (glyphosate, sulfometuron, terbuthylazine, and trifluralin) in two L. rigidum populations from Southeast Australia. Cross-validation of the method across populations and herbicide treatments showed high repeatability and transferability. Significant positive correlations in resistance of individual plants were observed across herbicides, which suggest either the accumulation of herbicide-specific resistance alleles in single genotypes (multiple stacked resistance) or the presence of general broad-effects resistance alleles (cross-resistance). We used these quantitative estimates of cross-resistance to simulate how resistance development under an herbicide rotation strategy is likely to be higher than expected.