Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

PNNGS, a multi-convolutional parallel neural network for genomic selection

Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS.

Maximizing efficiency in sunflower breeding through historical data optimization

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Genotypic influence in the juvenile to adult transition in olive seedlings

Olive breeding is a long process and any improvement in shortening the juvenile phase is highly desirable. In the present study, the effect of olive tree parents in different agronomic characteristics have been evaluated during four years in 520 olive genotypes generated from three different crosses in three different experimental fields, all located in Andalusia region, Spain. The crosses evaluated are 'Arbosana' x 'Sikitita' and its reciprocal, whose parents are characterized by being early bearers; and 'Frantoio' free pollinated, whose mother variety is characterized by having a long unproductive period. We studied plant height, distance and time to the first flowering, plant vigor and percentage of olive oil in the fruits. The findings reveal that progeny from 'Arbosana' and 'Sikitita' crosses, irrespective of the direction of the cross, exhibited a lower distance to flower, early bearing, reduced vigor and a lower percentage of olive oil in fruit compared to 'Frantoio' seedlings obtained from free pollination. Furthermore, no discernible differences were observed in the evaluated characteristics when comparing reciprocal crosses across the three fields in the four-years assessment period. Therefore, these results highlight the significance of planting height in reducing the evaluation period required in an olive breeding program and support the hypothesis that there is no maternal effect in the inheritance of the evaluated agronomic characteristics in olive trees.

Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family

This review highlights -omics research in Solanaceae family, with a particular focus on resilient traits. Extensive research has enriched our understanding of Solanaceae genomics and genetics, with historical varietal development mainly focusing on disease resistance and cultivar improvement but shifting the emphasis towards unveiling resilience mechanisms in genebank-preserved germplasm is nowadays crucial. Collecting such information, might help researchers and breeders developing new experimental design, providing an overview of the state of the art of the most advanced approaches for the identification of the genetic elements laying behind resilience. Building this starting point, we aim at providing a useful tool for tackling the global agricultural resilience goals in these crops.

Generation of parthenocarpic tomato plants in multiple elite cultivars using the CRISPR/Cas9 system

Tomato (Solanum lycopersicum L.) is one of the most important crops in the world for its fruit production. Advances in cutting-edge techniques have enabled the development of numerous critical traits related to the quality and quantity of tomatoes. Genetic engineering techniques, such as gene transformation and gene editing, have emerged as powerful tools for generating new plant varieties with superior traits. In this study, we induced parthenocarpic traits in a population of elite tomato (ET) lines. At first, the adaptability of ET lines to genetic transformation was evaluated to identify the best-performing lines by transforming the SlANT1 gene overexpression cassette and then later used to produce the SlIAA9 knockout lines using the CRISPR/Cas9 system. ET5 and ET8 emerged as excellent materials for these techniques and showed higher efficiency. Typical phenotypes of knockout sliaa9 were clearly visible in G0 and G1 plants, in which simple leaves and parthenocarpic fruits were observed. The high efficiency of the CRISPR/Cas9 system in developing new tomato varieties with desired traits in a short period was demonstrated by generating T-DNA-free homozygous sliaa9 knockout plants in the G1 generation. Additionally, a simple artificial fertilization method was successfully applied to recover seed production from parthenocarpic plants, securing the use of these varieties as breeding materials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01452-1.

Genomics empowering conservation action and improvement of celery in the face of climate change

MAIN CONCLUSION

Integration of genomic approaches like whole genome sequencing, functional genomics, evolutionary genomics, and CRISPR/Cas9-based genome editing has accelerated the improvement of crop plants including leafy vegetables like celery in the face of climate change. The anthropogenic climate change is a real peril to the existence of life forms on our planet, including human and plant life. Climate change is predicted to be a significant threat to biodiversity and food security in the coming decades and is rapidly transforming global farming systems. To avoid the ghastly future in the face of climate change, the elucidation of shifts in the geographical range of plant species, species adaptation, and evolution is necessary for plant scientists to develop climate-resilient strategies. In the post-genomics era, the increasing availability of genomic resources and integration of multifaceted genomics elements is empowering biodiversity conservation action, restoration efforts, and identification of genomic regions adaptive to climate change. Genomics has accelerated the true characterization of crop wild relatives, genomic variations, and the development of climate-resilient varieties to ensure food security for 10 billion people by 2050. In this review, we have summarized the applications of multifaceted genomic tools, like conservation genomics, whole genome sequencing, functional genomics, genome editing, pangenomics, in the conservation and adaptation of plant species with a focus on celery, an aromatic and medicinal Apiaceae vegetable. We focus on how conservation scientists can utilize genomics and genomic data in conservation and improvement.

The promise and challenges of characterizing genome-wide structural variants: A case study in a critically endangered parrot

There is growing interest in the role of structural variants (SVs) as drivers of local adaptation and speciation. From a biodiversity genomics perspective, the characterization of genome-wide SVs provides an exciting opportunity to complement single nucleotide polymorphisms (SNPs). However, little is known about the impacts of SV discovery and genotyping strategies on the characterization of genome-wide SV diversity within and among populations. Here, we explore a near whole-species resequence data set, and long-read sequence data for a subset of highly represented individuals in the critically endangered kākāpō (Strigops habroptilus). We demonstrate that even when using a highly contiguous reference genome, different discovery and genotyping strategies can significantly impact the type, size and location of SVs characterized genome-wide. Further, we found that the mean number of SVs in each of two kākāpō lineages differed both within and across generations. These combined results suggest that genome-wide characterization of SVs remains challenging at the population-scale. We are optimistic that increased accessibility to long-read sequencing and advancements in bioinformatic approaches including multireference approaches like genome graphs will alleviate at least some of the challenges associated with resolving SV characteristics below the species level. In the meantime, we address caveats, highlight considerations, and provide recommendations for the characterization of genome-wide SVs in biodiversity genomic research.

Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm

Oil palm is the most important oil crop worldwide. Colombia is the fourth largest producer, primarily relying on production from interspecific hybrids, derived from crosses between Elaeis oleifera and Elaeis guineensis (OxG). However, conventional breeding can take up to 20 years to generate a new variety. Therefore, reducing the breeding cycle while improving the genetic gain for complex traits is desirable. Genomic selection (GS) is an approach with the potential to achieve this goal. In this study, we evaluated 431 F1 interspecific hybrids (OxG) and 444 backcrosses (BC1) for morphological and yield-related traits. Genomic predictions were performed with the G-BLUP model using three different population datasets for training the model: the same population (TRN1), the other population (TRN2), and both populations (TRN1+2). Higher multi-family prediction accuracies were obtained for foliar area (0.3 in OxG) and trunk height (0.47 in BC1) when the model was trained with TRN1. Single-family prediction accuracies were lower in the OxG compared to BC1 families for traits such as trunk diameter, trunk height, bunch number, and yield using TRN1. Conversely, lower prediction accuracies were obtained for most traits when the model was trained using TRN2 (< 0.1). Multi-trait models showed a substantial increase of the predictions for traits such as yield (0.22 for OxG and 0.44 for BC1), because of the genetic correlations between traits. The results herein highlighted the potential of GS for parental selection in OxG and BC1 populations, but further studies are required to improve the models to select individuals by their genetic value.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01341-5.

Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits

Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response

Many studies showed that few degrees above tomato optimum growth temperature threshold can lead to serious loss in production. Therefore, the development of innovative strategies to obtain tomato cultivars with improved yield under high temperature conditions is a main goal both for basic genetic studies and breeding activities. In this paper, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress conditions. Moreover, a genotyping by sequencing (GBS) approach has been employed for building up genomic selection (GS) models both for yield and soluble solid content (SCC). Several parameters, including training population size, composition and marker quality were tested to predict genotype performance under heat stress conditions. A good prediction accuracy for the two analyzed traits (0.729 for yield production and 0.715 for SCC) was obtained. The predicted models improved the genetic gain of selection in the next breeding cycles, suggesting that GS approach is a promising strategy to accelerate breeding for heat tolerance in tomato. Finally, the annotation of SNPs located in gene body regions combined with QTL analysis allowed the identification of five candidates putatively involved in high temperatures response, and the building up of a GS model based on calibrated panel of SNP markers.

Tomato transcriptomic response to Tuta absoluta infestation

BACKGROUND

The South America pinworm, Tuta absoluta, is a destructive pest of tomato that causes important losses worldwide. Breeding of resistant/tolerant tomato cultivars could be an effective strategy for T. absoluta management but, despite the economic importance of tomato, very limited information is available about its response to this treat. To elucidate the defense mechanisms to herbivore feeding a comparative analysis was performed between a tolerant and susceptible cultivated tomato at both morphological and transcriptome level to highlight constitutive leaf barriers, molecular and biochemical mechanisms to counter the effect of T. absoluta attack.

RESULTS

The tolerant genotype showed an enhanced constitutive barrier possibly as result of the higher density of trichomes and increased inducible reactions upon mild infestation thanks to the activation/repression of key transcription factors regulating genes involved in cuticle formation and cell wall strength as well as of antinutritive enzymes, and genes involved in the production of chemical toxins and bioactive secondary metabolites.

CONCLUSIONS

Overall, our findings suggest that tomato resilience to the South America pinworm is achieved by a combined strategy between constitutive and induced defense system. A well-orchestrated modulation of plant transcription regulation could ensure a trade-off between defense needs and fitness costs. Our finding can be further exploited for developing T. absoluta tolerant cultivars, acting as important component of integrated pest management strategy for more sustainable production.

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

Comparative biochemical and transcriptome analyses in tomato and eggplant reveal their differential responses to Tuta absoluta infestation

Tomato is more prone to Tuta absoluta invasion and damages as compared to other host plants but the mechanism behind this preference has not been elucidated. Here, two contrasting host preference plants, tomato and eggplant, were used to investigate biochemical and transcriptomic modifications induced by T. absoluta infestation. Biochemical analysis at 0-72 h post T. absoluta infestation revealed significantly reduced concentrations of amino acid, fructose, sucrose, jasmonic acid, salicylic acid, and total phenols in tomato compared to eggplant, mainly at 48 h post T. absoluta infestation. Transcriptome analysis showed higher transcript changes in infested eggplant than tomato. Signaling genes had significant contributions to mediate plant immunity against T. absoluta, specifically genes associated with salicylic acid in eggplant. Genes from PR1b1, NPR1, NPR3, MAPKs, and ANP1 families play important roles to mitigate T. absoluta infestation. Our results will facilitate the development of control strategies against T. absoluta for sustainable tomato production.