Wine metabolome and sensory analyses demonstrate the oenological potential of novel grapevine genotypes for sustainable viticulture in warm climates.

BACKGROUND: Genetic breeding is essential to develop grapevine genotypes adapted to warm climates and resistant to pathogens. Traditionally cultivated Vitis vinifera is susceptible to biotic and abiotic stresses. Winemakers and consumers, however, perceive wines from non-vinifera or hybrid cultivars as inferior. In this study, sensory analyses and comprehensive metabolite profiling by targeted and untargeted approaches were used to investigate the oenological potential of wines from grapes of genotypes developed throughout four breeding cycles to improve climate adaptation, sugar contents and berry color. RESULTS: Novel genotypes had higher yields and the wines exhibited increased contents of polyphenols, including anthocyanins. Volatile monoterpenes in the wines decreased throughout breeding cycles in the absence of selective pressure. Polyphenol contents were higher in intermediate wines, with hydroxytyrosol contents reaching up to three times reported values. Mouthfeel attributes astringency, leafy taste, flavor and body, and persistency showed significant correlation with untargeted features. Supervised model-based analyses of the metabolome effectively discriminate wines from distinct genetic origins. CONCLUSION: Taken together, the results demonstrate the potential of novel grapevine genotypes to a more sustainable viticulture and quality wine production in warm climates. Comprehensive metabolite profiling of the wines reveals that genotype clustering is dependent on the chemical class and that traits not submitted to selective pressure are also altered by breeding. Supervised multivariate models were effective to predict the genetic origin of the wines based on the metabolic profile, indicating the potential of the technique to identify biomarkers for wines from sustainable genotypes. © 2024 Society of Chemical Industry.

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

A graph model for genomic prediction in the context of a linear mixed model framework.

Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation revealed that incorporating the graph constructed with line connections alone resulted in decreased prediction accuracy compared to conventional methods that consider only genotype effects. However, integrating both genotype effects and the graph structure led to slightly improved results over considering genotype effects alone. These findings were validated across 14 datasets commonly used in plant breeding research.

Combining ability for agronomic traits among commercial maize hybrids under low and high nitrogen inputs targeting the development of breeding populations.

Commercial hybrids are the main germplasm source for developing maize lines in breeding programs in Brazil; additionally, nitrogen (N) is one the major limiting maize production in Brazilian tropical areas. Here, we assessed the combining ability among ten commercial hybrids under contrasting N inputs and selected the best parental hybrids to develop breeding populations for optimal and N-stress environments. We evaluated the 45 F1 crosses for agronomic traits under contrasting N inputs and over two summer seasons. A mixed model approach was used to estimate the variance components of general combining ability (GCA) and specific combining ability (SCA) as well as to predict the GCA and SCA effects. N-stress caused a reduction in GY (33.25%) of F1 crosses averaged across seasons. We found presence of combining ability (GCA and SCA) x N input interaction for grain yield (GY), days to pollen and plant stature. The parental hybrids showed differences in GCA for cycle and plant stature but not for GY, irrespective of N inputs. Additionally, the variance components of SCA were not significant (P>0.10) for GY under LN, whereas SCA was the major component accounting for variation among F1 crosses under HN. Based on estimates of GCA effects for cycle and plant height, we selected the hybrids BAL188, BM3061, GNZ7210, BRS1060 and DKB390 as sources of favorable alleles for earlier maturing and shorter stature maize for both N conditions and suggested that hybrids GNZ7201 and DKB390, and AG1051 and NS70, which presented very small estimates of SCA for GY, must be recombined to develop two synthetic populations to begin a reciprocal recurrent selection program, mainly for non N-stress environments.

Leaf antioxidant activity in Colombian elite Hevea brasiliensis genotypes as a breeding strategy for water deficit tolerance under Amazonia conditions.

This study evaluated the foliar antioxidant activity in nine Hevea brasiliensis genotypes from the ECC-1 (Élite Caquetá Colombia) selection and IAN 873 cultivar (control) in trees in the growth stage in two large-scale clonal trials in response to different climatic (semi-humid warm and humid warm sites) and seasonal (dry and rainy periods) conditions in the Colombian Amazon. The results indicated that Reactive Oxygen Species (ROS) production increased under conditions of lower water availability (dry period), leading to lipid peroxidation, high defense of photosynthetic pigments, and development of better osmotic adjustment capacity in the ECC 64, IAN 873, ECC 90, and ECC 35 genotypes due to high concentrations of carotenoids (0.40 mg g-1), reducing sugars (65.83 µg mg-1), and malondialdehyde (MDA) (2.44 nmol ml-1). In contrast, during the rainy period, a post-stress action was observed due to high contents of proline and total sugars (39.43 µg g-1 and 173.03 µg g-1, respectively). At the site level, with high Photosynthetically Active Radiation (PAR) values (1143 moles photons m-2 s-1), temperature (32.11°C), and lower precipitation (135 mm), higher antioxidant activity (chlorophylls a, b and total, carotenoids, and proline) was recorded at the humid warm site, demonstrating that the ECC 90, ECC 64, and ECC 66 genotypes are tolerant to water deficit compared to IAN 873. The ECC 64 genotype, independent of seasonal changes and site conditions, presented the highest contents in Chl a, total Chl, reducing sugars, total sugars, and MDA, showing a tendency to adapt to fluctuating conditions. This study showed that water fluctuations do not cause the same metabolic responses, these vary within the same species, depending on their developmental stage and the climatic and seasonal variations characteristic of the Colombian Amazon.

Genetic resources of African mahogany in Brazil: genomic diversity and structure of forest plantations.

BACKGROUND: African mahogany species (Khaya sp.) have been introduced to Brazil gaining increasing economic interest over the last years, as they produce high quality wood for industrial applications. To this date, however, the knowledge available on the genetic basis of African mahogany plantations in Brazil is limited, which has driven this study to examine the extent of genetic diversity and structure of three cultivated species (Khaya grandifoliola, Khaya senegalensis and Khaya ivorensis) and their prospects for forest breeding. RESULTS: In total, 115 individuals were genotyped (48 of K. grandifoliola, 34 of K. senegalensis and 33 of K. ivorensis) for 3,330 filtered neutral loci obtained from genotyping-by-sequencing for the three species. The number of SNPs varied from 2,951 in K. ivorensis to 4,754 in K. senegalensis. Multiloci clustering, principal component analysis, Bayesian structure and network analyses showed a clear genetic separation among the three species. Structure analysis also showed internal structure within each species, highlighting genetic subgroups that could be sampled for selecting distinct genotypes for further breeding, although the genetic distances are moderate to low. CONCLUSION: In our study, SNP markers efficiently assessed the genomic diversity of African mahogany forest plantations in Brazil. Our genetic data clearly separated the three Khaya species. Moreover, pairwise estimates of genetic distances among individuals within each species showed considerable genetic divergence among individuals. By genotyping 115 pre-selected individuals with desirable growth traits, allowed us not only to recommend superior genotypes but also to identify genetically distinct individuals for use in breeding crosses.

Genetic divergence and truncation and simultaneous selection in inbred families (S1) of elephant grass for bioenergetic purposes via mixed models.

The State University of North Fluminense Darcy Ribeiro (UENF) has been developing for fifteen years a breeding program that aims at the development of new cultivars of elephant grass due to its high potential and the low availability of cultivars developed by genetic breeding programs that meet the needs of producers in the State of Rio de Janeiro. In this sense, inbred families were also obtained as a way of fixing potential alleles for traits related to production, as the inbreeding process apparently does not strongly affect elephant grass in aspects related to inbreeding depression. This study aimed to estimate genetic diversity, variance components and prediction of genotypic values in 11 (S1) elephant grass families, and perform the truncation and simultaneous selection of traits using the selection index, by mixed models. The experimental design consisted of randomized blocks with 11 (S1) families, three replications, and six plants per plot. For variables dry matter production, percentage of dry matter, plant height, stem diameter, number of tillers and leaf blade width, was performed the estimation of genetic parameters and selection of the best genotypes based selection index using mixed model. The descriptors were subjected to correlation analysis, distance matrices were generated by the Mahalanobis method, and individuals were grouped by the UPGMA method. In the selection via mixed models (REML/BLUP), families 6, 11, 8, 1, 3, 7, and 9 contributed most of the genotypes selected for the evaluated traits, indicating their high potential to generate superior genotype. The selection indices via mixed models indicated that the multiplicative index presented a greater selection gain.The phenotypic correlations showed the possibility of performing an indirect selection from six evaluated traits.The genotypes were separated into 18 groups by the Mahalanobis distance, allowing the observation of a wide genetic diversity. The most divergent and productive genotypes were self-fertilized to obtain the second generation (S2), continuing the development program.

A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.

Genomic selection (GS) is changing plant breeding by significantly reducing the resources needed for phenotyping. However, its accuracy can be compromised by mismatches between training and testing sets, which impact efficiency when the predictive model does not adequately reflect the genetic and environmental conditions of the target population. To address this challenge, this study introduces a straightforward method using binary-Lasso regression to estimate ß coefficients. In this approach, the response variable assigns 1 to testing set inputs and 0 to training set inputs. Subsequently, Lasso, Ridge, and Elastic Net regression models use the inverse of these ß coefficients (in absolute values) as weights during training (WLasso, WRidge, and WElastic Net). This weighting method gives less importance to features that discriminate more between training and testing sets. The effectiveness of this method is evaluated across six datasets, demonstrating consistent improvements in terms of the normalized root mean square error. Importantly, the model's implementation is facilitated using the glmnet library, which supports straightforward integration for weighting ß coefficients.

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

De novo domestication in the Solanaceae: advances and challenges.

The advent of highly efficient genome editing (GE) tools, coupled with high-throughput genome sequencing, has paved the way for the accelerated domestication of crop wild relatives. New crops could thus be rapidly created that are well adapted to cope with drought, flooding, soil salinity, or insect damage. De novo domestication avoids the complexity of transferring polygenic stress resistance from wild species to crops. Instead, new crops can be created by manipulating major genes in stress-resistant wild species. However, the genetic basis of certain relevant domestication-related traits often involve epistasis and pleiotropy. Furthermore, pan-genome analyses show that structural variation driving gene expression changes has been selected during domestication. A growing body of work suggests that the Solanaceae family, which includes crop species such as tomatoes, potatoes, eggplants, peppers, and tobacco, is a suitable model group to dissect these phenomena and operate changes in wild relatives to improve agronomic traits rapidly with GE. We briefly discuss the prospects of this exciting novel field in the interface between fundamental and applied plant biology and its potential impact in the coming years.

An Introduction to Plant Cell, Tissue, and Organ Culture: Current Status and Perspectives.

Plant cell, tissue, and organ cultures (PCTOC) have been used as experimental systems in basic research, allowing gene function demonstration through gene overexpression or repression and investigating the processes involved in embryogenesis and organogenesis or those related to the potential production of secondary metabolites, among others. On the other hand, PCTOC has also been applied at the commercial level for the vegetative multiplication (micropropagation) of diverse plant species, mainly ornamentals but also horticultural crops such as potato or fruit and tree species, and to produce high-quality disease-free plants. Moreover, PCTOC protocols are important auxiliary systems in crop breeding crops to generate pure lines (homozygous) to produce hybrids for the obtention of polyploid plants with higher yields or better performance. PCTOC has been utilized to preserve and conserve the germplasm of different crops or threatened species. Plant genetic improvement through genetic engineering and genome editing has been only possible thanks to the establishment of efficient in vitro plant regeneration protocols. Different companies currently focus on commercializing plant secondary metabolites with interesting biological activities using in vitro PCTOC. The impact of omics on PCTOC is discussed.

Molecular evaluation and phenotypic screening of brown and orange rust in Saccharum germplasm.

Brazil is the largest global producer of sugarcane and plays a significant role-supplier of sugar and bioethanol. However, diseases such as brown and orange rust cause substantial yield reductions and economic losses, due decrease photosynthesis and biomass in susceptible cultivars. Molecular markers associated with resistance genes, such as Bru1 (brown rust) and G1 (orange rust), could aid in predicting resistant genotypes. In this study, we sought to associate the phenotypic response of 300 sugarcane accessions with the genotypic response of Bru1 and G1 markers. The field trials were conducted in a randomized block design, and five six-month-old plants per plot were evaluated under natural disease conditions. Genotypic information about the presence or absence of Bru1 (haplotype 1) and G1 gene was obtained after extraction of genomic DNA and conventional PCR. Of the total accessions evaluated, 60.3% (181) showed resistance to brown rust in the field, and of these, 70.7% (128) had the Bru1 gene present. Considering the field-resistant accessions obtained from Brazilian breeding programs (116), the Bru1 was present in 77,6% of these accessions. While alternative resistance sources may exist, Bru1 likely confers enduring genetic resistance in current Brazilian cultivars. Regarding the phenotypic reaction to orange rust, the majority of accessions, 96.3% (288), were field resistant, and of these, 52.7% (152) carried the G1 marker. Although less efficient for predicting resistance when compared to Bru1, the G1 marker could be part of a quantitative approach when new orange rust resistance genes are described. Therefore, these findings showed the importance of Bru1 molecular markers for the early selection of resistant genotypes to brown rust by genetic breeding programs.

Creation of rice doubled haploids with low amylose content using in vitro anther culture.

In vitro androgenesis is a unique model for producing homozygous doubled haploid plants. The use of haploid biotechnology accelerates to obtain of doubled haploid plants, which is very important in rice breeding. The purpose of this work is to improve the production of doubled haploids in rice anther culture in vitro and selection of doubled haploid plants with valuable traits. The study the influence of nutrient media on the production of calli and plant regeneration processes in anther culture of 35 rice genotypes was revealed a significant influence of nutrient media on callus production. It was shown that the addition to culture medium phytohormones ratio with high level of cytokinin (5.0 mg/L BAP) and a low level of auxin (0.5 mg/L NAA), supplemented with amino acid composition promotes high production of green regenerated plants (68.75%) compared to albino plants (31.25%). As a result, doubled haploid lines of the glutinous variety Violetta were selected, which characterized by a low amylose content variation (from 1.86 to 2.80%). These doubled haploids are superior to the original variety in some yield traits and represent valuable breeding material.

Multi-trait index: selection and recommendation of superior black bean genotypes as new improved varieties.

Common bean provides diet rich in vitamins, fiber, minerals, and protein, which could contribute into food security of needy populations in many countries. Developing genotypes that associate favorable agronomic and grain quality traits in the common bean crop could increase the chances of adopting new cultivars black bean. In this context, the present study aimed at selection of superior black bean lines using multi-variate indexes, Smith-Hazel-index, and genotype by yield*trait biplot analysis. These trials were conducted in Campos dos Goytacazes - RJ, in 2020 and 2021. The experimental design used was randomized blocks, with 28 treatments and three replications. The experimental unit consisted of four rows 4.0 m long, spaced at 0.50 m apart, with a sowing density of 15 seeds per meter. The two central rows were used for the evaluations. The selection of superior genotypes was conducted using the multiple trait stability index (MTSI), multi-trait genotype-ideotype distance index (MGIDI), multi-trait index based on factor analysis and genotype-ideotype distance (FAI-BLUP), Smith-Hazel index, and Genotype by Yield*Trait Biplot (GYT). The multivariate indexes efficiently selected the best black bean genotypes, presenting desirable selection gains for most traits. The use of multivariate indexes and GYT enable the selection of early genotypes with higher grain yields. These lines G9, G13, G17, G23, and G27 were selected based on their performance for multiple traits closest to the ideotype and could be recommended as new varieties.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

Engineering a One Health Super Wheat.

Wheat is the predominant crop worldwide, contributing approximately 20% of protein and calories to the human diet. However, the yield potential of wheat faces limitations due to pests, diseases, and abiotic stresses. Although conventional breeding has improved desirable traits, the use of modern transgenesis technologies has been limited in wheat in comparison to other crops such as maize and soybean. Recent advances in wheat gene cloning and transformation technology now enable the development of a super wheat consistent with the One Health goals of sustainability, food security, and environmental stewardship. This variety combines traits to enhance pest and disease resistance, elevate grain nutritional value, and improve resilience to climate change. In this review, we explore ways to leverage current technologies to combine and transform useful traits into wheat. We also address the requirements of breeders and legal considerations such as patents and regulatory issues.

Diagnosis and Characterization of Plant Viruses Using HTS to Support Virus Management and Tomato Breeding.

Viral diseases pose a significant threat to tomato crops (Solanum lycopersicum L.), one of the world's most economically important vegetable crops. The limited genetic diversity of cultivated tomatoes contributes to their high susceptibility to viral infections. To address this challenge, tomato breeding programs must harness the genetic resources found in native populations and wild relatives. Breeding efforts may aim to develop broad-spectrum resistance against the virome. To identify the viruses naturally infecting 19 advanced lines, derived from native tomatoes, high-throughput sequencing (HTS) of small RNAs and confirmation with PCR and RT-PCR were used. Single and mixed infections with tomato mosaic virus (ToMV), tomato golden mosaic virus (ToGMoV), and pepper huasteco yellow vein virus (PHYVV) were detected. The complete consensus genomes of three variants of Mexican ToMV isolates were reconstructed, potentially forming a new ToMV clade with a distinct 3' UTR. The absence of reported mutations associated with resistance-breaking to ToMV suggests that the Tm-1, Tm-2, and Tm-22 genes could theoretically be used to confer resistance. However, the high mutation rates and a 63 nucleotide insertion in the 3' UTR, as well as amino acid mutations in the ORFs encoding 126 KDa, 183 KDa, and MP of Mexican ToMV isolates, suggest that it is necessary to evaluate the capacity of these variants to overcome Tm-1, Tm-2, and Tm-22 resistance genes. This evaluation, along with the characterization of advanced lines using molecular markers linked to these resistant genes, will be addressed in future studies as part of the breeding strategy. This study emphasizes the importance of using HTS for accurate identification and characterization of plant viruses that naturally infect tomato germplasm based on the consensus genome sequences. This study provides crucial insights to select appropriate disease management strategies and resistance genes and guide breeding efforts toward the development of virus-resistant tomato varieties.

Abiotic Stress Tolerance Boosted by Genetic Diversity in Plants.

Plant breeding [...].

Selection of rice breeding lines for resistance to biotic and abiotic stresses.

Rice (Oryza sativa L.) grown in many countries around the world with different climatic conditions and a huge number of environmental stresses, both biotic (fungi, bacteria, viruses, insects) and abiotic (cold, drought, salinity) limit rice productivity. In this regard, breeders and scientists are trying to create rice lines that are resistant to multiple stresses. The aim of this work was to screen and select cold and blast resistant rice breeding lines (RBLs) using molecular markers. Molecular screening of RBLs and parental varieties to cold tolerance was carried out using markers RM24545, RM1377, RM231 and RM569 associated with QTLs (qPSST-3, qPSST-7, qPSST-9). It was discovered that the presence of three QTLs characterizes the cold resistance of studied genotypes, and the absence of one of them leads to cold sensitivity. As a result, 21 cold-resistant out of the 28 studied RBLs were identified. These cold resistant 21 RBLs were further tested to blast resistance using markers Pi-ta, Pita3, Z56592, 195R-1, NMSMPi9-1, TRS26, Pikh MAS, MSM6, 9871.T7E2b, RM224 and RM1233. It was revealed that 16 RBLs from 21 studied lines contain 5-6 blast resistance genes. In accordance with the blast resistance strategy, the presence of 5 or more genes ensures the formation of stable resistance to Magnaporthe oryzae. Thus, 16 lines resistant to multiple stresses, such as cold and blast disease were developed. It should be noted that 6 of these selected lines are high-yielding, which is very important in rice breeding program. These RBLs can be used in breeding process as starting lines, germplasm exchange as a source of resistant genes for the development of new rice varieties resistant to multiple stress factors.