Bivariate GWA mapping reveals associations between aliphatic glucosinolates and plant responses to thrips and heat stress

Although plants harbor a huge phytochemical diversity, only a fraction of plant metabolites is functionally characterized. In this work, we aimed to identify the genetic basis of metabolite functions during harsh environmental conditions in Arabidopsis thaliana. With machine learning algorithms we predicted stress-specific metabolomes for 23 (a)biotic stress phenotypes of 300 natural Arabidopsis accessions. The prediction models identified several aliphatic glucosinolates (GLSs) and their breakdown products to be implicated in responses to heat stress in siliques and herbivory by Western flower thrips, Frankliniella occidentalis. Bivariate GWA mapping of the metabolome predictions and their respective (a)biotic stress phenotype revealed genetic associations with MAM, AOP, and GS-OH, all three involved in aliphatic GSL biosynthesis. We, therefore, investigated thrips herbivory on AOP, MAM, and GS-OH loss-of-function and/or overexpression lines. Arabidopsis accessions with a combination of MAM2 and AOP3, leading to 3-hydroxypropyl dominance, suffered less from thrips feeding damage. The requirement of MAM2 for this effect could, however, not be confirmed with an introgression line of ecotypes Cvi and Ler, most likely due to other, unknown susceptibility factors in the Ler background. However, AOP2 and GS-OH, adding alkenyl or hydroxy-butenyl groups, respectively, did not have major effects on thrips feeding. Overall, this study illustrates the complex implications of aliphatic GSL diversity in plant responses to heat stress and a cell-content-feeding herbivore.

A guided network estimation approach using multi-omic information

INTODUCTION

In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks.

OBJECTIVE

In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves.

METHODS

The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data.

CONCLUSIONS

We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

Tomato fruit quality traits and metabolite content are affected by reciprocal crosses and heterosis

Heterosis occurs when the F1s outperform their parental lines for a trait. Reciprocal hybrids are obtained by changing the cross direction of parental genotypes. Both biological phenomena could affect the external and internal attributes of fleshy fruits. This work aimed to detect reciprocal effects and heterosis in tomato (Solanum lycopersicum) fruit quality traits and metabolite content. Twelve agronomic traits and 28 metabolites identified and estimated by 1H-NMR were evaluated in five cultivars grown in two environments. Given that the genotype component was more important than the phenotype, the traits were evaluated following a full diallel mating design among those cultivars, in a greenhouse. Hybrids showed a higher phenotypic diversity than parental lines. Interestingly, the metabolites, mainly amino acids, displayed more reciprocal effects and heterosis. Agronomic traits were more influenced by general combining ability (GCA) and metabolites by specific combining ability (SCA). Furthermore, the genetic distance between parental lines was not causally related to the occurrence of reciprocal effects or heterosis. Hybrids with heterosis and a high content of metabolites linked to tomato flavour and nutritious components were obtained. Our results highlight the impact of selecting a cultivar as male or female in a cross to enhance the variability of fruit attributes through hybrids as well as the possibility to exploit heterosis for fruit composition.

Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer's disease

There are currently no disease-modifying treatments for Alzheimer's disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition-a preclinical predictor of AD-translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR-BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs-particularly XL.HDL.FC-as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Computational aspects underlying genome to phenome analysis in plants

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

New insights into the impacts of elevated CO2, nitrogen, and temperature levels on the regulation of C and N metabolism in durum wheat using network analysis

The use of correlation networks and hierarchical cluster analysis provides a framework to organize and study the coordination of parameters such as genes, metabolites, proteins and physiological parameters. We have analyzed 142 traits from primary C and N metabolism, including biochemical and gene expression analyses, in a range of 32 different growth conditions (various [CO₂] levels, temperatures, N supplies, growth stages and experimental methods). To test the integration of primary metabolism, particularly under climate change, we investigated which C and N metabolic traits and transcript levels are correlated in durum wheat flag leaves using a correlation network and a hierarchical cluster analysis. There was a high amount of positive correlation between traits involved in a wide range of biological processes, suggesting a close and intricate coordination between C-N metabolisms at the biochemical and transcriptional levels. Transcript levels for genes related to N uptake and assimilation were especially coexpressed with genes belonging to the respiratory pathway, highlighting the coordination between the synthesis of organic N compounds and provision of energy and C skeletons. Also involved in this coordination were Rubisco and nitrate reductase activities, which play a key role in the regulation of plant metabolism. Carbohydrate accumulation was linked with a down-regulation of photosynthetic and N metabolism genes and nitrate reductase activity. Based on the degree of connectivity between nodes, network exploration facilitated the identification of some traits that may be biologically relevant during plant abiotic stress tolerance, as most of them are involved in limiting steps of plant metabolism.

Effects of Parental Temperature and Nitrate on Seed Performance are Reflected by Partly Overlapping Genetic and Metabolic Pathways

Seed performance is affected by the seed maturation environment, and previously we have shown that temperature, nitrate and light intensity were the most influential environmental factors affecting seed performance. Seeds developed in these environments were selected to assess the underlying metabolic pathways, using a combination of transcriptomics and metabolomics. These analyses revealed that the effects of the parental temperature and nitrate environments were reflected by partly overlapping genetic and metabolic networks, as indicated by similar changes in the expression levels of metabolites and transcripts. Nitrogen metabolism-related metabolites (asparagine, γ-aminobutyric acid and allantoin) were significantly decreased in both low temperature (15 °C) and low nitrate (N0) maturation environments. Correspondingly, nitrogen metabolism genes (ALLANTOINASE, NITRATE REDUCTASE 1, NITRITE REDUCTASE 1 and NITRILASE 4) were differentially regulated in the low temperature and nitrate maturation environments, as compared with control conditions. High light intensity during seed maturation increased galactinol content, and displayed a high correlation with seed longevity. Low light had a genotype-specific effect on cell surface-encoding genes in the DELAY OF GERMINATION 6-near isogenic line (NILDOG6). Overall, the integration of phenotypes, metabolites and transcripts led to new insights into the regulation of seed performance.