Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers

Applications of CRISPR Technologies in Forestry and Molecular Wood Biotechnology

Forests worldwide are under increasing pressure from climate change and emerging diseases, threatening their vital ecological and economic roles. Traditional breeding approaches, while valuable, are inherently slow and limited by the long generation times and existing genetic variation of trees. CRISPR technologies offer a transformative solution, enabling precise and efficient genome editing to accelerate the development of climate-resilient and productive forests. This review provides a comprehensive overview of CRISPR applications in forestry, exploring its potential for enhancing disease resistance, improving abiotic stress tolerance, modifying wood properties, and accelerating growth. We discuss the mechanisms and applications of various CRISPR systems, including base editing, prime editing, and multiplexing strategies. Additionally, we highlight recent advances in overcoming key challenges such as reagent delivery and plant regeneration, which are crucial for successful implementation of CRISPR in trees. We also delve into the potential and ethical considerations of using CRISPR gene drive for population-level genetic alterations, as well as the importance of genetic containment strategies for mitigating risks. This review emphasizes the need for continued research, technological advancements, extensive long-term field trials, public engagement, and responsible innovation to fully harness the power of CRISPR for shaping a sustainable future for forests.

Enhancing prediction accuracy of foliar essential oil content, growth, and stem quality in Eucalyptus globulus using multi-trait deep learning models

Eucalyptus globulus Labill., is a recognized multipurpose tree, which stands out not only for the valuable qualities of its wood but also for the medicinal applications of the essential oil extracted from its leaves. In this study, we implemented an integrated strategy comprising genomic and phenomic approaches to predict foliar essential oil content, stem quality, and growth-related traits within a 9-year-old breeding population of E. globulus. The strategy involved evaluating Uni/Multi-trait deep learning (DL) models by incorporating genomic data related to single nucleotide polymorphisms (SNPs) and haplotypes, as well as the phenomic data from leaf near-infrared (NIR) spectroscopy. Our results showed that essential oil content (oil yield) ranged from 0.01 to 1.69% v/fw and had no significant correlation with any growth-related traits. This suggests that selection solely based on growth-related traits did n The emphases (colored text) from revisions were removed throughout the article. Confirm that this change is fine. ot influence the essential oil content. Genomic heritability estimates ranged from 0.25 (diameter at breast height (DBH) and oil yield) to 0.71 (DBH and stem straightness (ST)), while pedigree-based heritability exhibited a broader range, from 0.05 to 0.88. Notably, oil yield was found to be moderate to highly heritable, with genomic values ranging from 0.25 to 0.60, alongside a pedigree-based estimate of 0.48. The DL prediction models consistently achieved higher prediction accuracy (PA) values with a Multi-trait approach for most traits analyzed, including oil yield (0.699), tree height (0.772), DBH (0.745), slenderness coefficient (0.616), stem volume (0.757), and ST (0.764). The Uni-trait approach achieved superior PA values solely for branching quality (0.861). NIR spectral absorbance was the best omics data for CNN or MLP models with a Multi-trait approach. These results highlight considerable genetic variation within the Eucalyptus progeny trial, particularly regarding oil production. Our results contribute significantly to understanding omics-assisted deep learning models as a breeding strategy to improve growth-related traits and optimize essential oil production in this species.

Pinpointing genomic loci for drought-induced proline and hydrogen peroxide accumulation in bread wheat under field conditions

BACKGROUND

Proline (Pro) and hydrogen peroxide (H₂O₂) play a critical role in plants during drought adaptation. Genetic mapping for drought-induced Pro and H₂O₂ production under field conditions is very limited in crop plants since their phenotyping with large populations is labor-intensive. A genome-wide association study (GWAS) of a diversity panel comprised of 184 bread wheat cultivars grown in natural field (control) and rain-out shelter (drought) environments was performed to identify candidate loci and genes regulating Pro and H₂O₂ accumulation induced by drought.

RESULTS

The GWAS identified top significant marker-trait associations (MTAs) on 1A and 2A chromosomes, respectively for Pro and H₂O₂ in response to drought. Similarly, MTAs for stress tolerance index (STI) of Pro and H₂O₂ were identified on 5B and 1B chromosomes, respectively. Total 143 significant MTAs were identified including 36 and 71 were linked to drought and 2 and 34 were linked to STI for Pro and H₂O_2, respectively. Next, linkage disequilibrium analysis revealed minor alleles of significant single-markers and haplotypes were associated with higher Pro and H₂O₂ accumulation under drought. Several putative candidate genes for Pro and H₂O₂ content encode proteins with kinase, transporter or protein-binding activities.

CONCLUSIONS

The identified genetic factors associated with Pro and H₂O₂ biosynthesis underlying drought adaptation lay a fundamental basis for functional studies and future marker-assisted breeding programs.

A Neural Network-Based Spectral Approach for the Assignment of Individual Trees to Genetically Differentiated Subpopulations

Studying population structure has made an essential contribution to understanding evolutionary processes and demographic history in forest ecology research. This inference process basically involves the identification of common genetic variants among individuals, then grouping the similar individuals into subpopulations. In this study, a spectral-based classification of genetically differentiated groups was carried out using a provenance–progeny trial of Eucalyptus cladocalyx. First, the genetic structure was inferred through a Bayesian analysis using single-nucleotide polymorphisms (SNPs). Then, different machine learning models were trained with foliar spectral information to assign individual trees to subpopulations. The results revealed that spectral-based classification using the multilayer perceptron method was very successful at classifying individuals into their respective subpopulations (with an average of 87% of correct individual assignments), whereas 85% and 81% of individuals were assigned to their respective classes correctly by convolutional neural network and partial least squares discriminant analysis, respectively. Notably, 93% of individual trees were assigned correctly to the class with the smallest size using the spectral data-based multi-layer perceptron classification method. In conclusion, spectral data, along with neural network models, are able to discriminate and assign individuals to a given subpopulation, which could facilitate the implementation and application of population structure studies on a large scale.

Cyanogenesis in cassava and its molecular manipulation for crop improvement

While cassava is one of the most important staple crops worldwide, it has received the least investment per capita consumption of any of the major global crops. This is in part due to cassava being a crop of subsistence farmers that is grown in countries with limited resources for crop improvement. While its starchy roots are rich in calories, they are poor in protein and other essential nutrients. In addition, they contain potentially toxic levels of cyanogenic glycosides which must be reduced to safe levels before consumption. Furthermore, cyanogens compromise the shelf life of harvested roots due to cyanide-induced inhibition of mitochondrial respiration, and associated production of reactive oxygen species that accelerate root deterioration. Over the past two decades, the genetic, biochemical, and developmental factors that control cyanogen synthesis, transport, storage, and turnover have largely been elucidated. It is now apparent that cyanogens contribute substantially to whole-plant nitrogen metabolism and protein synthesis in roots. The essential role of cyanogens in root nitrogen metabolism, however, has confounded efforts to create acyanogenic varieties. This review proposes alternative molecular approaches that integrate accelerated cyanogen turnover with nitrogen reassimilation into root protein that may offer a solution to creating a safer, more nutritious cassava crop.

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

Antiviral Activities of Eucalyptus Essential Oils: Their Effectiveness as Therapeutic Targets against Human Viruses

Given the limited therapeutic management of infectious diseases caused by viruses, such as influenza and SARS-CoV-2, the medicinal use of essential oils obtained from Eucalyptus trees has emerged as an antiviral alternative, either as a complement to the treatment of symptoms caused by infection or to exert effects on possible pharmacological targets of viruses. This review gathers and discusses the main findings on the emerging role and effectiveness of Eucalyptus essential oil as an antiviral agent. Studies have shown that Eucalyptus essential oil and its major monoterpenes have enormous potential for preventing and treating infectious diseases caused by viruses. The main molecular mechanisms involved in the antiviral activity are direct inactivation, that is, by the direct binding of monoterpenes with free viruses, particularly with viral proteins involved in the entry and penetration of the host cell, thus avoiding viral infection. Furthermore, this review addresses the coadministration of essential oil and available vaccines to increase protection against different viruses, in addition to the use of essential oil as a complementary treatment of symptoms caused by viruses, where Eucalyptus essential oil exerts anti-inflammatory, mucolytic, and spasmolytic effects in the attenuation of inflammatory responses caused by viruses, in particular respiratory diseases.

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.

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.