Evolutionary and ecological functional genomics, from lab to the wild

Development of an infiltration-based RNA preservation method for cryogen-free storage of leaves for gene expression analyses in field-grown plants

BACKGROUND

Gene expression is a fundamental process for plants to express their phenotype, and its analysis is the basis of molecular studies. However, the instability of RNA often poses an obstacle to analyzing plants grown in fields or remote locations where the availability of liquid nitrogen or dry ice is limited. To deepen our understanding of plant phenotypes and tolerance to field-specific stresses, it is crucial to develop methodologies to maintain plant RNA intact and safely transfer it for downstream analyses such as qPCR and RNA-seq.

RESULTS

In this study, the author developed a novel tissue preservation method that involved the infiltration of RNA preservation solution into the leaf apoplast using a syringe and subsequent storage at 4 °C. RNA-seq using samples stored for 5 d and principal component analyses showed that rice leaves treated with the infiltration method maintained the original transcriptome pattern better than those treated with the traditional method when the leaves were simply immersed in the solution. Additionally, it was also found that extracted RNA can be transported with minimum risk of degradation when it is bound to the membrane of RNA extraction kits. The developed infiltration method was applied to rice plants grown in a local farmer's field in northern Madagascar to analyze the expression of nutrient-responsive genes, suggesting nutrient imbalances in some of the fields examined.

CONCLUSIONS

This study showed that the developed infiltration method was effective in preserving the transcriptome status of rice and sorghum leaves when liquid nitrogen or a deep freezer is not available. The developed method was useful for diagnosing plants in the field based on the expression of nutrient-responsive marker genes. Moreover, the method used to protect RNA samples from degradation during transportation offers the possibility to use them for RNA-seq. This novel technique could pave the way for revealing the molecular basis of plant phenotypes by accelerating gene expression analyses using plant samples that are unique in the field.

Pathogenic single nucleotide polymorphisms in RhoA gene: Insights into structural and functional impacts on RhoA-PLD1 interaction through molecular dynamics simulation

Molecular switches serve as key regulators of biological systems by acting as one of the crucial driving forces in the initiation of signal transduction pathway cascades. The Ras homolog gene family member A (RhoA) is one of the molecular switches that binds with GTP in order to cycle between an active GTP-bound state and an inactive GDP-bound state. Any aberrance in control over this circuit, particularly due to any perturbation in switching, leads to the development of different pathogenicity. Consequently, the single nucleotide polymorphisms (SNPs) within the RhoA gene, especially deleterious genetic variations, are crucial to study to forecast structural alteration and their functional impacts in light of disease onset. In this comprehensive study, we employed a range of computational tools to screen the deleterious SNPs of RhoA from 207 nonsynonymous SNPs (nsSNPs). By utilizing 7 distinct tools for further analysis, 8 common deleterious SNPs were sorted, among them 5 nsSNPs (V9G, G17E, E40K, A61T, F171L) were found to be in the highly conserved regions, with E40K and A61T at G2 and G3 motif of the GTP-binding domain respectively, indicating potential perturbation in GTP/GDP binding ability of the protein. RhoA-GDP complex interacts with the enzyme phospholipase, specifically PLD1, to regulate different cellular activities. PLD1 is also a crucial regulator of thrombosis and cancer. In that line of focus, our initial structural analysis of Y66H, A61T, G17E, I86N, and I151T mutations of RhoA revealed remarkable decreased hydrophobicity from which we further filtered out G17E and I86N which may have potential impact on the RhoA-GDP-PLD1 complex. Intriguingly, the comparative 250 ns (ns) molecular dynamics (MD) simulation of these two mutated complexes revealed overall structural instability and altered interaction patterns. Therefore, further investigation into these deleterious mutations with in vitro and in vivo studies could lead to the identification of potential biomarkers in terms of different pathogenesis and could also be utilized in personalized therapeutic targets in the long run.

Reducing herbivory in mixed planting by genomic prediction of neighbor effects in the field

Genetically diverse populations can increase plant resistance to natural enemies. Yet, beneficial genotype pairs remain elusive due to the occurrence of positive or negative effects of mixed planting on plant resistance, respectively called associational resistance or susceptibility. Here, we identify key genotype pairs responsible for associational resistance to herbivory using the genome-wide polymorphism data of the plant species Arabidopsis thaliana. To quantify neighbor interactions among 199 genotypes grown in a randomized block design, we employ a genome-wide association method named "Neighbor GWAS" and genomic prediction inspired by the Ising model of magnetics. These analyses predict that 823 of the 19,701 candidate pairs can reduce herbivory in mixed planting. We planted three pairs with the predicted effects in mixtures and monocultures, and detected 18-30% reductions in herbivore damage in the mixed planting treatment. Our study shows the power of genomic prediction to assemble genotype mixtures with positive biodiversity effects.

Transcriptome-based prediction for polygenic traits in rice using different gene subsets

BACKGROUND

Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait.

RESULTS

Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R2 = 0.59 when using all genes; R2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight.

CONCLUSIONS

Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.

Deepening genomic sequences of 1081 Gossypium hirsutum accessions reveals novel SNPs and haplotypes relevant for practical breeding utility

Fiber quality is a major breeding goal in cotton, but phenotypically direct selection is often hindered. In this study, we identified fiber quality and yield related loci using GWAS based on 2.97 million SNPs obtained from 10.65× resequencing data of 1081 accessions. The results showed that 585 novel fiber loci, including two novel stable SNP peaks associated with fiber length on chromosomes At12 and Dt05 and one novel genome regions linked with fiber strength on chromosome Dt12 were identified. Furthermore, by means of gene expression analysis, GhM_A12G0090, GhM_D05G1692, GhM_D12G3135 were identified and GhM_D11G2208 function was identified in Arabidopsis. Additionally, 14 consistent and stable superior haplotypes were identified, and 25 accessions were detected as possessing these 14 superior haplotype in breeding. This study providing fundamental insight relevant to identification of genes associated with fiber quality and yield will enhance future efforts toward improvement of upland cotton.

Response and inversion of skewness parameters to meteorological factors based on RGB model of leaf color digital image

In the natural environment, complex and changeable meteorological factors can influence changes in the internal physiology and phenotype of crops. It is important to learn how to convert complex meteorological factor stimuli into plant perception phenotypes when analyzing the biological data obtained under the natural field condition. We restored the true gradation distribution of leaf color, which is also known as the skewed distribution of color scale, and obtained 20 multi-dimensional color gradation skewness-distribution (CGSD) parameters based on the leaf color skewness parameter system. Furthermore, we analyzed the correlation between the five corresponding meteorological factors and canopy CGSD parameters of peppers growing in a greenhouse and cabbages growing in an open air environment, built response model and inversion mode of leaf color to meteorological factors. Based on the analysis, we find a new method for correlating complex environmental problems with multi-dimensional parameters. This study provides a new idea for building a correlation model that uses leaf color as a bridge between meteorological factors and plants internal physiological state.

Genome-wide association study of aphid abundance highlights a locus affecting plant growth and flowering in Arabidopsis thaliana

Plant life-history traits, such as size and flowering, contribute to shaping variation in herbivore abundance. Although plant genes involved in physical and chemical traits have been well studied, less is known about the loci linking plant life-history traits and herbivore abundance. Here, we conducted a genome-wide association study (GWAS) of aphid abundance in a field population of Arabidopsis thaliana. This GWAS of aphid abundance detected a relatively rare but significant variant on the third chromosome of A. thaliana, which was also suggestively but non-significantly associated with the presence or absence of inflorescence. Out of candidate genes near this significant variant, a mutant of a ribosomal gene (AT3G13882) exhibited slower growth and later flowering than a wild type under laboratory conditions. A no-choice assay with the turnip aphid, Lipaphis erysimi, found that aphids were unable to successfully establish on the mutant. Our GWAS of aphid abundance unexpectedly found a locus affecting plant growth and flowering.

Growth-limiting drought stress induces time-of-day-dependent transcriptome and physiological responses in hybrid poplar

Drought stress negatively impacts the health of long-lived trees. Understanding the genetic mechanisms that underpin response to drought stress is requisite for selecting or enhancing climate change resilience. We aimed to determine how hybrid poplars respond to prolonged and uniform exposure to drought; how responses to moderate and more severe growth-limiting drought stresses differed; and how drought responses change throughout the day. We established hybrid poplar trees (Populus × 'Okanese') from unrooted stem cutting with abundant soil moisture for 6 weeks. We then withheld water to establish well-watered, moderate and severe growth-limiting drought conditions. These conditions were maintained for 3 weeks during which growth was monitored. We then measured photosynthetic rates and transcriptomes of leaves that had developed during the drought treatments at two times of day. The moderate and severe drought treatments elicited distinct changes in growth and development, photosynthetic rates and global transcriptome profiles. Notably, the time of day of sampling produced the strongest effect in the transcriptome data. The moderate drought treatment elicited global transcriptome changes that were intermediate to the severe and well-watered treatments in the early evening but did not elicit a strong drought response in the morning. Stable drought conditions that are sufficient to limit plant growth elicit distinct transcriptional profiles depending on the degree of water limitation and on the time of day at which they are measured. There appears to be a limited number of genes and functional gene categories that are responsive to all of the tested drought conditions in this study emphasizing the complex nature of drought regulation in long-lived trees.

Whole genome sequencing reveals footprints of adaptive genetic variation in populations of Eruca sativa

Populations of Eruca sativa (Brassicaceae) derived from arid and Mediterranean habitats exhibit ecotypic differentiation. Here, pooled DNA sequencing was used to assess adaptive genome differentiation in the two ecotypes. Differentiated SNP loci were scanned with the empirical FST outlier method and by correlating allele frequencies with environmental parameters. Genetic diversity values were relatively higher in the pooled arid genome, whereas the pooled Mediterranean genome exhibited stronger directional selection, indicating the impact of climatic conditions on genetic diversity. GO enrichment analysis categorized the annotated differentiated loci according to biological processes, revealing a large set of candidate genes related to abiotic and biotic stress responses. Allelic variation was detected in regulatory elements and coding regions (synonymous and non-synonymous mutations) of genes belonging to different transcription factors and phytohormone signaling, suggesting adaptation to both abiotic and biotic conditions. Furthermore, SNP mutations were also found in genic regions belonging to the synthesis of secondary metabolites, including aliphatic glucosinolates and their hydrolyzed bioactive compounds, among others. The results of this eco-genomic study demonstrate the role of divergent abiotic and biotic selection factors in evolutionary processes leading to adaptive ecotypic differentiation.

Fillable and unfillable gaps in plant transcriptome under field and controlled environments

The differences between plants grown in field and in controlled environments have long been recognized. However, few studies have addressed the underlying molecular mechanisms. To evaluate plant responses to fluctuating environments using laboratory equipment, we developed SmartGC, a high-performance growth chamber that reproduces the fluctuating irradiance, temperature and humidity of field environments. We analysed massive transcriptome data of rice plants grown under field and SmartGC conditions to clarify the differences in plant responses to field and controlled environments. Rice transcriptome dynamics in SmartGC mimicked those in the field, particularly during the morning and evening but those in conventional growth chamber conditions did not. Further analysis revealed that fluctuation of irradiance affects transcriptome dynamics in the morning and evening, while fluctuation of temperature affects transcriptome dynamics only in the morning. We found upregulation of genes related to biotic and abiotic stress, and their expression was affected by environmental factors that cannot be mimicked by SmartGC. Our results reveal fillable and unfillable gaps in the transcriptomes of rice grown in field and controlled environments and can accelerate the understanding of plant responses to field environments for both basic biology and agricultural applications.

A cross-scale approach to unravel the molecular basis of plant phenology in temperate and tropical climates

Plants have evolved to time their leafing, flowering and fruiting in appropriate seasons for growth, reproduction and resting. As a consequence of their adaptation to geographically different environments, there is a rich diversity in plant phenology from temperate and tropical climates. Recent progress in genetic and molecular studies will provide numerous opportunities to study the genetic basis of phenological traits and the history of adaptation of phenological traits to seasonal and aseasonal environments. Integrating molecular data with long-term phenology and climate data into predictive models will be a powerful tool to forecast future phenological changes in the face of global environmental change. Here, we review the cross-scale approach from genes to plant communities from three aspects: the latitudinal gradient of plant phenology at the community level, the environmental and genetic factors underlying the diversity of plant phenology, and an integrated approach to forecast future plant phenology based on genetically informed knowledge. Synthesizing the latest knowledge about plant phenology from molecular, ecological and mathematical perspectives will help us understand how natural selection can lead to the further evolution of the gene regulatory mechanisms in phenological traits in future forest ecosystems.

Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems

Rice (Oryza sativa) was domesticated around 10,000 years ago and has developed into a staple for half of humanity. The crop evolved and is currently grown in stably wet and intermittently dry agro-ecosystems, but patterns of adaptation to differences in water availability remain poorly understood. While previous field studies have evaluated plant developmental adaptations to water deficit, adaptive variation in functional and hydraulic components, particularly in relation to gene expression, has received less attention. Here, we take an evolutionary systems biology approach to characterize adaptive drought resistance traits across roots and shoots. We find that rice harbors heritable variation in molecular, physiological, and morphological traits that is linked to higher fitness under drought. We identify modules of co-expressed genes that are associated with adaptive drought avoidance and tolerance mechanisms. These expression modules showed evidence of polygenic adaptation in rice subgroups harboring accessions that evolved in drought-prone agro-ecosystems. Fitness-linked expression patterns allowed us to identify the drought-adaptive nature of optimizing photosynthesis and interactions with arbuscular mycorrhizal fungi. Taken together, our study provides an unprecedented, integrative view of rice adaptation to water-limited field conditions.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists' emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants' genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

Studying the genetic basis of masting

The mechanisms underlying mast seeding have traditionally been studied by collecting long-term observational data on seed crops and correlating seedfall with environmental variables. Significant progress in ecological genomics will improve our understanding of the evolution of masting by clarifying the genetic basis of masting traits and the role of natural selection in shaping those traits. Here, we summarize three important aspects in studying the evolution of masting at the genetic level: which traits govern masting, whether those traits are genetically regulated, and which taxa show wide variation in these traits. We then introduce recent studies on the molecular mechanisms of masting. Those studies measure seasonal changes in gene expression in natural conditions to quantify how multiple environmental factors combine to regulate floral initiation, which in many masting plant species is the single largest contributor to among-year variation in seed crops. We show that Fagaceae offers exceptional opportunities for evolutionary investigations because of its diversity at both the phenotypic and genetic levels and existing documented genome sequences. This article is part of the theme issue 'The ecology and evolution of synchronized seed production in plants'.

A nano-biomimetic transformation system enables in planta expression of a reporter gene in mature plants and seeds

Plant genetic engineering will be essential to decipher the genomic basis of complex traits, optimize crop genomics, and enable plant-based production of recombinant proteins. However, established plant transformation approaches for bioengineering are fraught with limitations. Although nanoparticle-mediated methods show great promise for advancing plant biotechnology, many engineered nanomaterials can have cytotoxic and ecological effects. Here, we demonstrate the efficient uptake of a nano-biomimetic carrier of plasmid DNA and transient expression of a reporter gene in leaves of Arabidopsis, common ice plant and tobacco, as well as in the developing seed tissues of Arabidopsis, field mustard, barley, and wheat. The nano-biomimetic transformation system described here has all the advantages of other nanoparticle-mediated approaches for passive delivery of genetic cargo into a variety of plant species and is also nontoxic to cells and to the environment for diverse biotechnological applications in plant biology and crop science.

Advancing crop genomics from lab to field

Crop genomics remains a key element in ensuring scientific progress to secure global food security. It has been two decades since the sequence of the first plant genome, that of Arabidopsis thaliana, was released, and soon after that the draft sequencing of the rice genome was completed. Since then, the genomes of more than 100 crops have been sequenced, plant genome research has expanded across multiple fronts and the next few years promise to bring further advances spurred by the advent of new technologies and approaches. We are likely to see continued innovations in crop genome sequencing, genetic mapping and the acquisition of multiple levels of biological data. There will be exciting opportunities to integrate genome-scale information across multiple scales of biological organization, leading to advances in our mechanistic understanding of crop biological processes, which will, in turn, provide greater impetus for translation of laboratory results to the field.

Tackling microbial threats in agriculture with integrative imaging and computational approaches

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

Using single-plant-omics in the field to link maize genes to functions and phenotypes

Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene-phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory-generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field-generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab-field gap.

TagSeq for gene expression in non-model plants: A pilot study at the Santa Rita Experimental Range NEON core site

PREMISE

TagSeq is a cost-effective approach for gene expression studies requiring a large number of samples. To date, TagSeq studies in plants have been limited to those with a high-quality reference genome. We tested the suitability of reference transcriptomes for TagSeq in non-model plants, as part of a study of natural gene expression variation at the Santa Rita Experimental Range National Ecological Observatory Network (NEON) core site.

METHODS

Tissue for TagSeq was sampled from multiple individuals of four species (Bouteloua aristidoides and Eragrostis lehmanniana [Poaceae], Tidestromia lanuginosa [Amaranthaceae], and Parkinsonia florida [Fabaceae]) at two locations on three dates (56 samples total). One sample per species was used to create a reference transcriptome via standard RNA-seq. TagSeq performance was assessed by recovery of reference loci, specificity of tag alignments, and variation among samples.

RESULTS

A high fraction of tags aligned to each reference and mapped uniquely. Expression patterns were quantifiable for tens of thousands of loci, which revealed consistent spatial differentiation in expression for all species.

DISCUSSION

TagSeq using de novo reference transcriptomes was an effective approach to quantifying gene expression in this study. Tags were highly locus specific and generated biologically informative profiles for four non-model plant species.

Plant adaptation to climate change - Where are we?

Climate change poses critical challenges for population persistence in natural communities, agriculture and environmental sustainability, and food security. In this review, we discuss recent progress in climatic adaptation in plants. We evaluate whether climate change exerts novel selection and disrupts local adaptation, whether gene flow can facilitate adaptive responses to climate change, and if adaptive phenotypic plasticity could sustain populations in the short term. Furthermore, we discuss how climate change influences species interactions. Through a more in-depth understanding of these eco-evolutionary dynamics, we will increase our capacity to predict the adaptive potential of plants under climate change. In addition, we review studies that dissect the genetic basis of plant adaptation to climate change. Finally, we highlight key research gaps, ranging from validating gene function, to elucidating molecular mechanisms, expanding research systems from model species to other natural species, testing the fitness consequences of alleles in natural environments, and designing multifactorial studies that more closely reflect the complex and interactive effects of multiple climate change factors. By leveraging interdisciplinary tools (e.g., cutting-edge omics toolkits, novel ecological strategies, newly-developed genome editing technology), researchers can more accurately predict the probability that species can persist through this rapid and intense period of environmental change, as well as cultivate crops to withstand climate change, and conserve biodiversity in natural systems.

Going wild for functional genomics: RNA interference as a tool to study gene-behavior associations in diverse species and ecological contexts

Identifying the genetic basis of behavior has remained a challenge for biologists. A major obstacle to this goal is the difficulty of examining gene function in an ecologically relevant context. New tools such as CRISPR/Cas9, which alter the germline of an organism, have taken center stage in functional genomics in non-model organisms. However, germline modifications of this nature cannot be ethically implemented in the wild as a part of field experiments. This impediment is more than technical. Gene function is intimately tied to the environment in which the gene is expressed, especially for behavior. Most lab-based studies fail to recapitulate an organism's ecological niche, thus most published functional genomics studies of gene-behavior relationships may provide an incomplete or even inaccurate assessment of gene function. In this review, we highlight RNA interference as an especially effective experimental method to deepen our understanding of the interplay between genes, behavior, and the environment. We highlight the utility of RNAi for researchers investigating behavioral genetics, noting unique attributes of RNAi including transience of effect and the feasibility of releasing treated animals into the wild, that make it especially useful for studying the function of behavior-related genes. Furthermore, we provide guidelines for planning and executing an RNAi experiment to study behavior, including challenges to consider. We urge behavioral ecologists and functional genomicists to adopt a more fully integrated approach which we call "ethological genomics". We advocate this approach, utilizing tools such as RNAi, to study gene-behavior relationships in their natural context, arguing that such studies can provide a deeper understanding of how genes can influence behavior, as well as ecological aspects beyond the organism that houses them.

The strength and pattern of natural selection on gene expression in rice

Levels of gene expression underpin organismal phenotypes^1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown^1,2. Here we assayed gene expression in rice (Oryza sativa)³, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts^4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic⁶ (0.04%), and transcripts that display lower levels of expression and stochastic noise^7-9 and higher levels of plasticity⁹ are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity⁹. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes^4,5, but that the efficacy of selection is genetically constrained under drought conditions¹⁰. Drought selected for earlier flowering^11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering¹³-marking this gene as a drought-escape gene^11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.

Transcription Profiles of Age-at-Maturity-Associated Genes Suggest Cell Fate Commitment Regulation as a Key Factor in the Atlantic Salmon Maturation Process

Despite recent taxonomic diversification in studies linking genotype with phenotype, follow-up studies aimed at understanding the molecular processes of such genotype-phenotype associations remain rare. The age at which an individual reaches sexual maturity is an important fitness trait in many wild species. However, the molecular mechanisms regulating maturation timing processes remain obscure. A recent genome-wide association study in Atlantic salmon (Salmo salar) identified large-effect age-at-maturity-associated chromosomal regions including genes vgll3, akap11 and six6, which have roles in adipogenesis, spermatogenesis and the hypothalamic-pituitary-gonadal (HPG) axis, respectively. Here, we determine expression patterns of these genes during salmon development and their potential molecular partners and pathways. Using Nanostring transcription profiling technology, we show development- and tissue-specific mRNA expression patterns for vgll3, akap11 and six6. Correlated expression levels of vgll3 and akap11, which have adjacent chromosomal location, suggests they may have shared regulation. Further, vgll3 correlating with arhgap6 and yap1, and akap11 with lats1 and yap1 suggests that Vgll3 and Akap11 take part in actin cytoskeleton regulation. Tissue-specific expression results indicate that vgll3 and akap11 paralogs have sex-dependent expression patterns in gonads. Moreover, six6 correlating with slc38a6 and rtn1, and Hippo signaling genes suggests that Six6 could have a broader role in the HPG neuroendrocrine and cell fate commitment regulation, respectively. We conclude that Vgll3, Akap11 and Six6 may influence Atlantic salmon maturation timing via affecting adipogenesis and gametogenesis by regulating cell fate commitment and the HPG axis. These results may help to unravel general molecular mechanisms behind maturation.

Distributed phenomics with the unPAK project reveals the effects of mutations

Determining how genes are associated with traits in plants and other organisms is a major challenge in modern biology. The unPAK project - undergraduates phenotyping Arabidopsis knockouts - has generated phenotype data for thousands of non-lethal insertion mutation lines within a single Arabidopsis thaliana genomic background. The focal phenotypes examined by unPAK are complex macroscopic fitness-related traits, which have ecological, evolutionary and agricultural importance. These phenotypes are placed in the context of the wild-type and also natural accessions (phytometers), and standardized for environmental differences between assays. Data from the unPAK project are used to describe broad patterns in the phenotypic consequences of insertion mutation, and to identify individual mutant lines with distinct phenotypes as candidates for further study. Inclusion of undergraduate researchers is at the core of unPAK activities, and an important broader impact of the project is providing students an opportunity to obtain research experience.

Transcriptional Variation in Glucosinolate Biosynthetic Genes and Inducible Responses to Aphid Herbivory on Field-Grown Arabidopsis thaliana

Recently, increasing attempts have been made to understand how plant genes function in natura. In this context, transcriptional profiles represent plant physiological status in response to environmental stimuli. Herein, we combined high-throughput RNA-Seq with insect survey data on 19 accessions of Arabidopsis thaliana grown at a field site in Switzerland. We found that genes with the gene ontology (GO) annotations of "glucosinolate biosynthetic process" and "response to insects" were most significantly enriched, and the expression of these genes was highly variable among plant accessions. Nearly half of the total expression variation in the glucosinolate biosynthetic genes (AOPs, ESM1, ESP, and TGG1) was explained by among-accession variation. Of these genes, the expression level of AOP3 differed among Col-0 accession individuals depending on the abundance of the mustard aphid (Lipaphis erysimi). We also found that the expression of the major cis-jasmone activated gene CYP81D11 was positively correlated with the number of flea beetles (Phyllotreta striolata and Phyllotreta atra). Combined with the field RNA-Seq data, bioassays confirmed that AOP3 was up-regulated in response to attack by mustard aphids. The combined results from RNA-Seq and our ecological survey illustrate the feasibility of using field transcriptomics to detect an inducible defense, providing a first step towards an in natura understanding of biotic interactions involving phenotypic plasticity.

Plant trichomes and a single gene GLABRA1 contribute to insect community composition on field-grown Arabidopsis thaliana

BACKGROUND

Genetic variation in plants alters insect abundance and community structure in the field; however, little is known about the importance of a single gene among diverse plant genotypes. In this context, Arabidopsis trichomes provide an excellent system to discern the roles of natural variation and a key gene, GLABRA1, in shaping insect communities. In this study, we transplanted two independent glabrous mutants (gl1-1 and gl1-2) and 17 natural accessions of Arabidopsis thaliana to two localities in Switzerland and Japan.

RESULTS

Fifteen insect species inhabited the plant accessions, with the insect community composition significantly attributed to variations among plant accessions. The total abundance of leaf-chewing herbivores was negatively correlated with trichome density at both field sites, while glucosinolates had variable effects on leaf chewers between the sites. Interestingly, there was a parallel tendency for the abundance of leaf chewers to be higher on gl1-1 and gl1-2 than on their different parental accessions, Ler-1 and Col-0, respectively. Furthermore, the loss of function in the GLABRA1 gene significantly decreased the resistance of plants to the two predominant chewers; flea beetles and turnip sawflies.

CONCLUSIONS

Overall, our results indicate that insect community composition significantly varies among A. thaliana accessions across two distant field sites, with GLABRA1 playing a key role in altering the abundance of leaf-chewing herbivores. Given that such a trichome variation is widely observed in Brassicaceae plants, the present study exemplifies the community-wide effect of a single plant gene on crucifer-feeding insects in the field.