Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants

Environmental factors, including different stresses, can have an impact on the expression of genes and subsequently the phenotype and development of plants. Since a large number of genes are involved in response to the perturbation of the environment, identifying groups of co-expressed genes is meaningful. The gene co-expression network models can be used for the exploration, interpretation, and identification of genes responding to environmental changes. Once a gene co-expression network is constructed, one can determine gene modules and the association of gene modules to the phenotypic response. To link modules to phenotype, one approach is to find the correlated eigengenes of given modules or to integrate all eigengenes in regularized linear model. This manuscript describes the method from construction of co-expression network, module discovery, association between modules and phenotypic data, and finally to annotation/visualization.

Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data

The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice

Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.

Updated efficacy results from the JAVELIN Renal 101 trial: first-line avelumab plus axitinib versus sunitinib in patients with advanced renal cell carcinoma

BACKGROUND

The phase 3 JAVELIN Renal 101 trial (NCT02684006) demonstrated significantly improved progression-free survival (PFS) with first-line avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma (aRCC). We report updated efficacy data from the second interim analysis.

PATIENTS AND METHODS

Treatment-naive patients with aRCC were randomized (1 : 1) to receive avelumab (10 mg/kg) intravenously every 2 weeks plus axitinib (5 mg) orally twice daily or sunitinib (50 mg) orally once daily for 4 weeks (6-week cycle). The two independent primary end points were PFS and overall survival (OS) among patients with programmed death ligand 1-positive (PD-L1+) tumors. Key secondary end points were OS and PFS in the overall population.

RESULTS

Of 886 patients, 442 were randomized to the avelumab plus axitinib arm and 444 to the sunitinib arm; 270 and 290 had PD-L1+ tumors, respectively. After a minimum follow-up of 13 months (data cut-off 28 January 2019), PFS was significantly longer in the avelumab plus axitinib arm than in the sunitinib arm {PD-L1+ population: hazard ratio (HR) 0.62 [95% confidence interval (CI) 0.490-0.777]}; one-sided P < 0.0001; median 13.8 (95% CI 10.1-20.7) versus 7.0 months (95% CI 5.7-9.6); overall population: HR 0.69 (95% CI 0.574-0.825); one-sided P < 0.0001; median 13.3 (95% CI 11.1-15.3) versus 8.0 months (95% CI 6.7-9.8)]. OS data were immature [PD-L1+ population: HR 0.828 (95% CI 0.596-1.151); one-sided P = 0.1301; overall population: HR 0.796 (95% CI 0.616-1.027); one-sided P = 0.0392].

CONCLUSION

Among patients with previously untreated aRCC, treatment with avelumab plus axitinib continued to result in a statistically significant improvement in PFS versus sunitinib; OS data were still immature.

CLINICAL TRIAL NUMBER

NCT02684006.

Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa)

Background

Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1).

Results

The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation.

Conclusions

Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice.

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration.

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.870.38-0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

BACKGROUND

Plant breeders seek to develop cultivars with maximal agronomic value, which is often assessed using numerous, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships among traits in the context of putative causal structures (i.e., trait networks). While multi-trait genome-wide association studies (MTM-GWAS) can infer putative genetic signals at the multivariate scale, standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). Here, we illustrate the utility of SEM-GWAS using a digital metric for shoot biomass, root biomass, water use, and water use efficiency in rice.

RESULTS

A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects acting on a trait into direct and indirect effects. Using this novel approach, we show that for most QTL associated with water use, total SNP effects were driven by genetic effects acting directly on water use rather that genetic effects originating from upstream traits. Conversely, total SNP effects for water use efficiency were largely due to indirect effects originating from the upstream trait, projected shoot area.

CONCLUSIONS

We describe a robust framework that can be applied to multivariate phenotypes to understand the interrelationships between complex traits. This framework provides novel insights into how QTL act within a phenotypic network that would otherwise not be possible with conventional multi-trait GWAS approaches. Collectively, these results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits.

Outcomes of patients with metastatic clear-cell renal cell carcinoma treated with second-line VEGFR-TKI after first-line immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) are being increasingly utilised in the front-line (1L) setting of metastatic clear-cell renal cell carcinoma (mccRCC). Limited data exist on responses and survival on second-line (2L) vascular endothelial growth factor-receptor tyrosine kinase inhibitor (VEGFR-TKI) therapy after 1L ICI therapy.

PATIENTS AND METHODS

This is a retrospective study of mccRCC patients treated with 2L VEGFR-TKI after progressive disease (PD) with 1L ICI. Patients were treated at MD Anderson Cancer Center or Memorial Sloan Kettering Cancer Center between December 2015 and February 2018. Objective response was assessed by blinded radiologists' review using Response Evaluation Criteria in Solid Tumours v1.1. Descriptive statistics and Kaplan-Meier method were used.

RESULTS

Seventy patients were included in the analysis. Median age at mccRCC diagnosis was 59 years; 8 patients (11%) had international metastatic database consortium favourable-risk disease, 48 (69%) had intermediate-risk disease and 14 (20%) had poor-risk disease. As 1L therapy, 12 patients (17%) received anti-programmed death ligand-1 (PD-(L)1) monotherapy with nivolumab or atezolizumab, 33 (47%) received nivolumab plus ipilimumab and 25 (36%) received combination anti-PD-(L)1 plus bevacizumab. 2L TKI therapies included pazopanib, sunitinib, axitinib and cabozantinib. On 2L TKI therapy, one patient (1.5%) achieved a complete response, 27 patients (39.7%) a partial response and 36 patients (52.9%) stable disease. Median progression-free survival (mPFS) was 13.2 months (95% confidence interval: 10.1, NA). Forty-five percent of subjects required a dose reduction, and twenty-seven percent of patients discontinued treatment because of toxicity.

CONCLUSIONS

In this retrospective study of patients with mccRCC receiving 2L TKI monotherapy after 1L ICI, we observed 2L antitumour activity and tolerance comparable to historical data for 1L TKI.

A Comprehensive Image-based Phenomic Analysis Reveals the Complex Genetic Architecture of Shoot Growth Dynamics in Rice (Oryza sativa)

Early vigor is an important trait for many rice ( L.)-growing environments. However, genetic characterization and improvement for early vigor is hindered by the temporal nature of the trait and strong genotype × environment effects. We explored the genetic architecture of shoot growth dynamics during the early and active tillering stages by applying a functional modeling and genomewide association (GWAS) mapping approach on a diversity panel of ∼360 rice accessions. Multiple loci with small effects on shoot growth trajectory were identified, indicating a complex polygenic architecture. Natural variation for shoot growth dynamics was assessed in a subset of 31 accessions using RNA sequencing and hormone quantification. These analyses yielded a gibberellic acid (GA) catabolic gene, , which could influence GA levels to regulate vigor in the early tillering stage. Given the complex genetic architecture of shoot growth dynamics, the potential of genomic selection (GS) for improving early vigor was explored using all 36,901 single-nucleotide polymorphisms (SNPs) as well as several subsets of the most significant SNPs from GWAS. Shoot growth trajectories could be predicted with reasonable accuracy using the 50 most significant SNPs from GWAS (0.37-0.53); however, the accuracy of prediction was improved by including more markers, which indicates that GS may be an effective strategy for improving shoot growth dynamics during the vegetative growth stage. This study provides insights into the complex genetic architecture and molecular mechanisms underlying early shoot growth dynamics and provides a foundation for improving this complex trait in rice.

Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content

Salinity is a major factor limiting crop productivity. Rice (Oryza sativa), a staple crop for the majority of the world, is highly sensitive to salinity stress. To discover novel sources of genetic variation for salt tolerance-related traits in rice, we screened 390 diverse accessions under 14 days of moderate (9 dS·m-1) salinity. In this study, shoot growth responses to moderate levels of salinity were independent of tissue Na+ content. A significant difference in root Na+ content was observed between the major subpopulations of rice, with indica accessions displaying higher root Na+ and japonica accessions exhibiting lower root Na+ content. The genetic basis of the observed variation in phenotypes was elucidated through genome-wide association (GWA). The strongest associations were identified for root Na+:K+ ratio and root Na+ content in a region spanning ~575 Kb on chromosome 4, named Root Na+ Content 4 (RNC4). Two Na+ transporters, HKT1;1 and HKT1;4 were identified as candidates for RNC4. Reduced expression of both HKT1;1 and HKT1;4 through RNA interference indicated that HKT1;1 regulates shoot and root Na+ content, and is likely the causal gene underlying RNC4. Three non-synonymous mutations within HKT1;1 were present at higher frequency in the indica subpopulation. When expressed in Xenopus oocytes the indica-predominant isoform exhibited higher inward (negative) currents and a less negative voltage threshold of inward rectifying current activation compared to the japonica-predominant isoform. The introduction of a 4.5kb fragment containing the HKT1;1 promoter and CDS from an indica variety into a japonica background, resulted in a phenotype similar to the indica subpopulation, with higher root Na+ and Na+:K+. This study provides evidence that HKT1;1 regulates root Na+ content, and underlies the divergence in root Na+ content between the two major subspecies in rice.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets.

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.

Genetic and molecular characterization of submergence response identifies Subtol6 as a major submergence tolerance locus in maize

Maize is highly sensitive to short term flooding and submergence. Early season flooding reduces germination, survival and growth rate of maize seedlings. We aimed to discover genetic variation for submergence tolerance in maize and elucidate the genetic basis of submergence tolerance through transcriptional profiling and linkage analysis of contrasting genotypes. A diverse set of maize nested association mapping (NAM) founder lines were screened, and two highly tolerant (Mo18W and M162W) and sensitive (B97 and B73) genotypes were identified. Tolerant lines exhibited delayed senescence and lower oxidative stress levels compared to sensitive lines. Transcriptome analysis was performed on these inbreds to provide genome level insights into the molecular responses to submergence. Tolerant lines had higher transcript abundance of several fermentation-related genes and an unannotated Pyrophosphate-Dependent Fructose-6-Phosphate 1-Phosphotransferase gene during submergence. A coexpression network enriched for CBF (C-REPEAT/DRE BINDING FACTOR: C-REPEAT/DRE BINDING FACTOR) genes, was induced by submergence in all four inbreds, but was more activated in the tolerant Mo18W. A recombinant inbred line (RIL) population derived from Mo18W and B73 was screened for submergence tolerance. A major QTL named Subtol6 was mapped to chromosome 6 that explains 22% of the phenotypic variation within the RIL population. We identified two candidate genes (HEMOGLOBIN2 and RAV1) underlying Subtol6 based on contrasting expression patterns observed in B73 and Mo18W. Sources of tolerance identified in this study (Subtol6) can be useful to increase survival rate during flooding events that are predicted to increase in frequency with climate change.

Introgression of novel traits from a wild wheat relative improves drought adaptation in wheat

Root architecture traits are an important component for improving water stress adaptation. However, selection for aboveground traits under favorable environments in modern cultivars may have led to an inadvertent loss of genes and novel alleles beneficial for adapting to environments with limited water. In this study, we elucidate the physiological and molecular consequences of introgressing an alien chromosome segment (7DL) from a wild wheat relative species (Agropyron elongatum) into cultivated wheat (Triticum aestivum). The wheat translocation line had improved water stress adaptation and higher root and shoot biomass compared with the control genotypes, which showed significant drops in root and shoot biomass during stress. Enhanced access to water due to higher root biomass enabled the translocation line to maintain more favorable gas-exchange and carbon assimilation levels relative to the wild-type wheat genotypes during water stress. Transcriptome analysis identified candidate genes associated with root development. Two of these candidate genes mapped to the site of translocation on chromosome 7DL based on single-feature polymorphism analysis. A brassinosteroid signaling pathway was predicted to be involved in the novel root responses observed in the A. elongatum translocation line, based on the coexpression-based gene network generated by seeding the network with the candidate genes. We present an effective and highly integrated approach that combines root phenotyping, whole-plant physiology, and functional genomics to discover novel root traits and the underlying genes from a wild related species to improve drought adaptation in cultivated wheat.

A method for purification and characterisation of Mycobacterium avium subsp. paratuberculosis from the intestinal mucosa of sheep with Johne's disease

Mycobacterium avium subsp. paratuberculosis, the cause of Johne's disease in ruminants, cannot be cultured in large quantities from affected sheep in Australia. A method is described for the purification of the organism from the intestinal mucosa of sheep with multibacillary Johne's disease in order to undertake restriction fragment length polymorphism (RFLP) analysis for epidemiological purposes. Using sucrose and potassium chloride as separation media for differential and density gradient centrifugation, yields of approximately 90 mg dry weight M. avium subsp. paratuberculosis per 5 g intestinal mucosa were obtained. The preparations of purified M. avium subsp. paratuberculosis were visually free of non-acid fast bacteria and contained 10(2)-10(3) aerobic/ facultatively anaerobic organisms per gram wet weight. DNA extracted from purified M. avium subsp. paratuberculosis was examined by hybridisation with an IS900 probe after digestion with BstEII and RFLP patterns distinct from isolates from cattle were obtained. The RFLP pattern of purified M. avium subsp. paratuberculosis from five sheep matched that obtained previously from organisms cultured from sheep in studies in New Zealand, indicating that the purification and RFLP method is robust.

Analysis of the sheep trichohyalin gene: potential structural and calcium-binding roles of trichohyalin in the hair follicle

Trichohyalin is a structural protein that is produced and retained in the cells of the inner root sheath and medulla of the hair follicle. The gene for sheep trichohyalin has been purified and the complete amino acid sequence of trichohyalin determined in an attempt to increase the understanding of the structure and function of this protein in the filamentous network of the hardened inner root sheath cells. Sheep trichohyalin has a molecular weight of 201,172 and is characterized by the presence of a high proportion of glutamate, arginine, glutamine, and leucine residues, together totaling more than 75% of the amino acids. Over 65% of trichohyalin consists of two sets of tandem peptide repeats which are based on two different consensus sequences. Trichohyalin is predicted to form an elongated alpha-helical rod structure but does not contain the sequences required for the formation of intermediate filaments. The amino terminus of trichohyalin contains two EF hand calcium-binding domains indicating that trichohyalin plays more than a structural role within the hair follicle. In situ hybridization studies have shown that trichohyalin is expressed in the epithelia of the tongue, hoof, and rumen as well as in the inner root sheath and medulla of the hair follicle.

Differential lectin binding to presumptive cortical cells of the wool follicle bulb

An alpha-D-galactoside-specific lectin from Bandeiraea simplicifolia (BSLI) showed differential binding to cortical cells of the wool follicle bulb. The lectin bound to cells on one side only of the bulb and was completely blocked by alpha-D-galactose. The region of lectin binding extended from presumptive cortical cells at the base of the bulb to cortical cells at the top of the bulb, disappearing as cortical cells entered the fibre cortex. An orthocortex-specific monoclonal antibody was used to show that cortical cells recognised by the lectin lie directly below the fibre orthocortex and presumably give rise to the orthocortex. The results suggest that two distinct populations of presumptive cortical cells are present only two to three cell layers from the base of the bulb in a region where no morphological differences are detectable. The lectin-bound pre-cortical cells appear to give rise to orthocortical cells while cells not bound by lectin presumably give rise to paracortical cells. Electron microscopy showed that the lectin bound to sites on the plasma membrane, probably on the extracellular surface. This suggests that the lectin target may be a membrane glycoprotein or glycolipid. Two polypeptides recognised by BSLI were separated from wool follicle extracts by SDS-gel electrophoresis. These polypeptides migrated at approximately 69 kDa and 17 kDa. However, only the 69 kDa molecule showed the expected loss of binding by BSLI in the presence of alpha-D-galactose. Further work will be required to determine if this glycoprotein is the bulb cell molecule recognised by BSLI.(ABSTRACT TRUNCATED AT 250 WORDS)

A lens fibre differentiation factor from calf neural retina

During growth of the eye lens, epithelial cells differentiate into fibre cells under the influence of neural retina. The fibre differentiation factor (FDF) was partially characterized from calf retina-conditioned medium, using lens epithelial explants from young rats, to provide a bioassay for differentiation. FDF was associated with large-protein aggregates, the smallest of which eluted at approximately 500-600 kD on Sephacryl S-300 columns and migrated as a single protein band near 600 kD on gradient gels. This protein resolved into nine major peptides on SDS-polyacrylamide gels, ranging between 23 and 27 kD. Eight of these peptides were present oa four doublets, but did not appear to contain specific carbohydrate residues. The approximately 500-600 kD complex could be slightly disrupted by trypsin or heat treatment to release a less stable 90 kD component. Fractionation of FDF invariably led to loss of activity, possibly due to gradual dissociation into less active and/or less stable components. A working hypothesis suggested by these findings is that FDF is associated with a small group of peptides, each contributing an essential function to the process of fibre differentiation.

Influence of neural retina on lens fibre differentiation in rats

Lens epithelial explants grown in retina-conditioned medium (RCM) undergo structural and molecular changes characteristic of fibre differentiation in the intact lens. We suggest that in vivo neural retina releases a fibre differentiation factor (FDF) that interacts with equatorial lens epithelial cells and stimulates them to undergo fibre cell differentiation. According to this model, interaction with neural retina is essential for normal lens formation in embryos and for normal lens growth throughout life. Preliminary work on purification of the factor indicates that FDF activity is associated with a high molecular weight complex of 500 kd. The active component of this complex appears to be an 80 kd molecule.

Onset of fibre differentiation in cultured rat lens epithelium under the influence of neural retina-conditioned medium

In the intact rat lens, epithelial cells, which are cuboidal and contain alpha-crystallin, give rise to fibre cells, which are elongated and contain alpha-, beta- and gamma-crystallins. Epithelial explants cultured in medium conditioned by bovine neural retinas (BRCM) showed changes analogous to fibre cell differentiation in vivo. The cells became enlarged and elongated and accumulated beta- and gamma-crystallin as well as alpha-crystallin. Labelling studies with [35S]-methionine showed that sequential changes in the synthesis of all three classes of crystallin occurred during culture in BRCM. After two days, synthesis of alpha-crystallin subunits, particularly alpha A and alpha AINS, increased relative to overall protein synthesis. After four days in culture, synthesis of beta-crystallin subunits, identified as beta B1a, beta B4, beta B5 and possibly beta A2, was detected for the first time and between four and eight days gamma-crystallin synthesis became detectable. The time of onset of gamma-crystallin synthesis seemed to show greater experimental variability than did onset of beta-crystallin synthesis. In explants cultured for 10 days in BRCM approximately 25% of new protein synthesis could be attributed to alpha-, beta- and gamma-crystallins. These events were completely dependent on BRCM, suggesting that neural retina secretes a factor(s) which initiates fibre cell differentiation. This culture system appears to be a suitable one for investigating the control of fibre differentiation in vitro.

The import of carbamoyl-phosphate synthase into mitochondria from foetal rat liver

A putative precursor of carbamoyl-phosphate synthase was isolated from a microsomal wash fraction and purified by high-pressure liquid chromatography. Autolytic degradation and limited proteolysis were used to characterize the putative precursor of carbamoyl-phosphate synthase and to show its similarity to the processed enzyme. The carbamoyl-phosphate synthase precursor underwent a time-dependent and concentration-dependent conversion into a dimeric or polymeric form. When labelled with 125I and incubated with foetal rat liver mitochondria the precursor was bound to the mitochondria and about 30% of the label was imported into the matrix space. This labelling required the presence of ATP and was time-dependent. Mitoplasts also imported the carbamoyl-phosphate synthase precursor. After import of the precursor, increases in carbamoyl-phosphate synthase activity could be demonstrated in foetal rat liver mitochondria.

The effect of premature and delayed birth on the development of UDP-glucuronosyltransferase activities towards bilirubin, morphine and testosterone in the rat

In the rat, UDP-glucuronosyltransferase activities towards bilirubin, morphine and testosterone increase markedly after normal or premature birth. This rapid development is superimposed upon a much slower maturation of activity which occurs in utero during the last 2 days of normal gestation and gestation when birth is delayed. Development of all three activities is similar under these different conditions, suggesting a common developmenpal regulatory mechanism.

The formation and distribution of bilirubin monoglucuronide and diglucuronide in rat liver slices

1. Bilirubin conjugation in rat liver slices was reassessed by using analysis of ethyl anthranilate azopigments to estimate separately the formation of bilirubin mono- and di-glucuronides. 2. Conjugation in slices resembles the situation in vivo more closely than does microsomal conjugation, in that diglucuronide is formed in appreciable quantity. 3. Both bilirubin mono- and di-glucuronides were present in slices in approximately equal amounts, but the monoglucuronide was the major product found in the incubation medium. 4. These results are discussed in relation to recent theories on the relationship between bilirubin mono- and di-glucuronide formation in vivo.

Demonstration of two functionally heterogenous groups within the activities of UDP-glucuronosyltransferase towards a series of 4-alkyl-substituted phenols

1. A simple colorimetric assay for UDP-glucuronosyltransferase activities towards phenolic substrates, using Folin & Ciocalteu's phenol reagent, is described. The assay is used to measure rat liver transferase activities towards substrates from a series of 4-alkyl-substituted phenols. 2. Activities towards phenol, 4-methylphenol and 4-ethylphenol develop near-adult values before birth, are precociously stimulated by dexa methasone in utero and are stimulated 3--4-fold by 3-methylcholanthrene in adult liver. These are assigned to a "late-foetal" group of transferase activities. 3. Activities towards 4-n-propylphenol, 4-s-butylphenol and 4-t-butylphenol are negligible in late-foetal liver, developing to near-adult values in the first 4 postnatal days, and are not affected by dexamethasone or 3-methylcholanthrene. They are assigned to a "neonatal" group of transferase activities. 4. Although 4-ethylphenol and 4-n-propylphenol differ only by a single --CH2-- moiety, this is sufficient to change the acceptability of these substrates respectively from the late-foetal to the neonatal group of transferase activities. The change is distinct, with no overlapping of substrate acceptability between the two groups of transferase activities. 5. From consideration of the above and other substrates, the two groups of transferase activities do not distinguish substrates on the basis of their molecular weights or lipophilicity. The distinguishing feature appears to be the specific molecular configurations of the substrates.

Synthesis and antiviral activity of certain thiazole C-nucleosides

A general reaction of glycosyl cyanides with liquid hydrogen sulfide in the presence of 4-dimethylaminopyridine to provide the corresponding glycosylthiocarboxamides is described. These glycosylthiocarboxamides were utilized as the precursors for the synthesis of 2-D-ribofuranosylthiazole-4-carboxamide and 2-beta-D-ribofuranosylthiazole-5-carboxamide (23). The structural modification of 2-beta-D-ribofuranosylthiazole-4-carboxamide (12) into 2-(2,3,5-tri-O-acetyl-beta-D-ribofuranosyl)thiazole-4-carboxamide (15), 2-beta-D-ribofuranosylthiazole-4-thiocarboxamide (17), and 2-(5-deoxy-beta-D-ribofuranosyl)thiazole-4-carboxamide (19) is also described. These thiazole nucleosides were tested for in vitro activity against type 1 herpes virus, type 3 parainfluenza virus, and type 13 rhinovirus and an in vivo experiment was run against parainfluenza virus. They were also evaluated as potential inhibitors of purine nucleotide biosynthesis. It was shown that the compounds (12 and 15) which possessed the most significant antiviral activity were also active inhibitors (40-70%) of guanine nucleotide biosynthesis.