A GWAS study highlights significant associations between a series of indels in a FLOWERING LOCUS T gene promoter and flowering time in white lupin (Lupinus albus L.)

BACKGROUND

White lupin (Lupinus albus L.) is a high-protein Old World grain legume with remarkable food and feed production interest. It is sown in autumn or early spring, depending on the local agroclimatic conditions. This study aimed to identify allelic variants associated with vernalization responsiveness, in order to improve our knowledge of legume flowering regulatory pathways and develop molecular selection tools for the desired phenology as required for current breeding and adaptation to the changing climate.

RESULTS

Some 120 white lupin accessions originating from a wide range of environments of Europe, Africa, and Asia were phenotyped under field conditions in three environments with different intensities of vernalization, namely, a Mediterranean and a subcontinental climate sites of Italy under autumn sowing, and a suboceanic climate site of France under spring sowing. Two hundred sixty-two individual genotypes extracted from them were phenotyped in a greenhouse under long-day photoperiod without vernalization. Phenology data, and marker data generated by Diversity Arrays Technology sequencing (DArT-seq) and by PCR-based screening targeting published quantitative trait loci (QTLs) from linkage map and newly identified insertion/deletion polymorphisms in the promoter region of the FLOWERING LOCUS T homolog, LalbFTc1 gene (Lalb_Chr14g0364281), were subjected to a genome-wide association study (GWAS). Population structure followed differences in phenology and isolation by distance pattern. The GWAS highlighted numerous loci significantly associated with flowering time, including four LalbFTc1 gene promoter deletions: 2388 bp and 2126 bp deletions at the 5' end, a 264 bp deletion in the middle and a 28 bp deletion at the 3' end of the promoter. Besides LalbFTc1 deletions, this set contained DArT-seq markers that matched previously published major QTLs in chromosomes Lalb_Chr02, Lalb_Chr13 and Lalb_Chr16, and newly discovered QTLs in other chromosomes.

CONCLUSIONS

This study highlighted novel QTLs for flowering time and validated those already published, thereby providing novel evidence on the convergence of FTc1 gene functional evolution into the vernalization pathway in Old World lupin species. Moreover, this research provided the set of loci specific for extreme phenotypes (the earliest or the latest) awaiting further implementation in marker-assisted selection for spring- or winter sowing.

Forecasting trait responses in novel environments to aid seed provenancing under climate change

Revegetation projects face the major challenge of sourcing optimal plant material. This is often done with limited information about plant performance and increasingly requires factoring resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide seed provenancing, but trait values expected under novel conditions are often unknown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits-days-to-bolting and fecundity-across the species' European range. Prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Genes Encoding Structurally Conserved Serpins in the Wheat Genome: Identification and Expression Profiles during Plant Development and Abiotic and Biotic Stress

Serpins constitute a family of proteins with a very wide distribution in nature. Serpins have a well-conserved tertiary structure enabling irreversible protease inhibition or other specific biochemical functions. We examined the 189 putative wheat serpin genes previously identified by Benbow et al. (2019) via analysis of gene annotations (RefSeq v1.0) and combined our previous examinations of wheat ESTs and the 454 genome assembly. We found that 81 of the 189 putative serpin genes, plus two manually annotated genes, encode full-length, structurally conserved serpins. Expression of these serpin genes during wheat development and disease/abiotic stress responses was analysed using a publicly available RNAseq database. Results showed that the wheat LR serpins, homologous to Arabidopsis AtSerpin1 and barley BSZx, are ubiquitously expressed across all tissues throughout the wheat lifecycle, whereas the expression of other wheat serpin genes is tissue-specific, including expression only in the grain, only in the root, and only in the anther and microspore. Nine serpin genes were upregulated in both biotic and abiotic responses. Two genes in particular were highly expressed during disease and abiotic challenges. Our findings provide valuable information for further functional study of the wheat serpins, which in turn may lead to their application as molecular markers in wheat breeding.

Effects of climatic and cultivar changes on winter wheat phenology in central Lithuania

It is essential to understand how climate change and varieties affect crop phenology and yields to adapt to future climate change. The aim of this study was to analyse the phenological development trends of three winter wheat cultivars (1990-2020) to identify the most critical meteorological-climatic factors influencing the development and yield of the cultivars and to investigate the heat requirements for each phenological phase to reveal the potential of the different cultivars to adapt to the warming climate. The observed dates of green-up, the beginning of stem elongation, and the grain development advanced significantly, but the timing of maturity changed insignificantly during the period of 1990-2020. The most marked change was related to the shortening of the period from sowing to green-up. The green-up dates were related to the mean temperature of the period after sowing. The occurrence of stem elongation and grain development dates were negatively correlated with the mean temperature in May. Significant correlations were found between temperature and duration from sowing to green-up and positive from stem elongation to grain development. The change of cultivar led to earlier green-up and grain development dates, but cultivar choise had no influence on sowing, stem elongation, and maturity dates from 1990 to 2020. The newer cultivar Skagen was more successful in exploiting increased thermal resources. The heat requirements remained almost unchanged during the vegetative development period, while the heat amount required during the reproductive period increased by about 15%. These findings demonstrate that the choice of crop cultivars with higher thermal requirements may be an appropriate adaptation mean to achieve higher yields in response to climate change, at least in the middle latitudes.

Winter warming post floral initiation delays flowering via bud dormancy activation and affects yield in a winter annual crop

In temperate climates many plant species use long-term detection of winter chilling as a seasonal cue. Previously the timing of flowering in winter annual plants has been shown to be controlled by the promotion of the floral transition by chilling, known as vernalization. In contrast, many temperate perennial species produce flower buds prior to winter and require winter chilling to break bud dormancy to enable bud break and flowering in the following spring. Here we show that flowering time in winter annuals can be controlled by bud dormancy and that in winter oilseed rape–reduced chilling during flower bud dormancy is associated with yield declines.

Winter annual life history is conferred by the requirement for vernalization to promote the floral transition and control the timing of flowering. Here we show using winter oilseed rape that flowering time is controlled by inflorescence bud dormancy in addition to vernalization. Winter warming treatments given to plants in the laboratory and field increase flower bud abscisic acid levels and delay flowering in spring. We show that the promotive effect of chilling reproductive tissues on flowering time is associated with the activity of two FLC genes specifically silenced in response to winter temperatures in developing inflorescences, coupled with activation of a BRANCHED1-dependent bud dormancy transcriptional module. We show that adequate winter chilling is required for normal inflorescence development and high yields in addition to the control of flowering time. Because warming during winter flower development is associated with yield losses at the landscape scale, our work suggests that bud dormancy activation may be important for effects of climate change on winter arable crop yields.

FLOWERING LOCUS T indel variants confer vernalization-independent and photoperiod-insensitive flowering of yellow lupin (Lupinus luteus L.)

Ongoing climate change has considerably reduced the seasonal window for crop vernalization, concurrently expanding cultivation area into northern latitudes with long-day photoperiod. To address these changes, cool season legume breeders need to understand molecular control of vernalization and photoperiod. A key floral transition gene integrating signals from these pathways is the Flowering locus T (FT). Here, a recently domesticated grain legume, yellow lupin (Lupinus luteus L.), was explored for potential involvement of FT homologues in abolition of vernalization and photoperiod requirements. Two FTa (LlutFTa1a and LlutFTa1b) and FTc (LlutFTc1 and LlutFTc2) homologues were identified and sequenced for two contrasting parents of a reference recombinant inbred line (RIL) population, an early-flowering cultivar Wodjil and a late-flowering wild-type P28213. Large deletions were detected in the 5' promoter regions of three FT homologues. Quantitative trait loci were identified for flowering time and vernalization response in the RIL population and in a diverse panel of wild and domesticated accessions. A 2227 bp deletion found in the LlutFTc1 promoter was linked with early phenology and vernalization independence, whereas LlutFTa1a and LlutFTc2 indels with photoperiod responsiveness. Comparative mapping highlighted convergence of FTc1 indel evolution in two Old World lupin species, addressing both artificial selection during domestication and natural adaptation to short season environmental conditions. We concluded that rapid flowering in yellow lupin is associated with the de-repression of the LlutFTc1 homologue from the juvenile phase, putatively due to the elimination of all binding sites in the promoter region for the AGAMOUS-like 15 transcription factor.

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France

In 2016, France, one of the leading wheat-producing and wheat-exporting regions in the world suffered its most extreme yield loss in over half a century. Yet, yield forecasting systems failed to anticipate this event. We show that this unprecedented event is a new type of compound extreme with a conjunction of abnormally warm temperatures in late autumn and abnormally wet conditions in the following spring. A binomial logistic regression accounting for fall and spring conditions is able to capture key yield loss events since 1959. Based on climate projections, we show that the conditions that led to the 2016 wheat yield loss are projected to become more frequent in the future. The increased likelihood of such compound extreme events poses a challenge: farming systems and yield forecasting systems, which often support them, must adapt.

In France, the 2016 winter wheat harvest was at its lowest since over 50 years. Here, Ben-Ari et al. show the role of seasonal temperature and precipitation extremes in this loss, and accounting for both of these variables explains large, historical yield loss events.