Global agricultural intensification during climate change: a role for genomics

GEFormer: A genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms

The integration of genotypic and environmental data can enhance genomic prediction accuracy for crop field traits. Existing genomic prediction methods fail to consider environmental factors and the real growth environments of crops, resulting in low genomic prediction accuracy. In this work, we developed GEFormer, a genotype-environment interaction genomic prediction method that integrates gating multilayer perceptron (gMLP) and linear attention mechanisms. First, GEFormer uses gMLP to extract local and global features among SNPs. Then, Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day, taking into consideration the real growth pattern of crops. A linear attention mechanism is used to capture the temporal features of environmental changes. Finally, GEFormer uses a gating mechanism to effectively fuse the genomic and environmental features. We examined the accuracy of GEFormer for predicting important agronomic traits of maize, rice, and wheat under three experimental scenarios: untested genotypes in tested environments, tested genotypes in untested environments, and untested genotypes in untested environments. The results showed that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods, especially with great advantages under the scenario of untested genotypes in untested environments. In addition, we used GEFormer for three real-world breeding applications: phenotype prediction in unknown environments, hybrid phenotype prediction using an inbred population, and cross-population phenotype prediction. The results showed that GEFormer had better prediction performance in actual breeding scenarios and could be used to assist in crop breeding.

Optimal implementation of genomic selection in clone breeding programs exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain

Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato (Solanum tuberosum L.) is lacking so far. The objectives of our study were to (i) assess the effects of different CS methods and the incorporation of GS and genetic variability monitoring on both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selected proportions had increased short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) achieved the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) maintained the same level of genetic gain but resulted in higher diversity and a lower number of fixed QTLs. Moreover, EUCD had a relatively high degree of efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeder's objectives. Our results provide breeders with concrete methods to improve their potato breeding programs.

Dissection of QTLs underlying the genetic basis of drought resistance in wheat: a meta-analysis

Wheat (Triticum aestivum L.) is one of the most important cereal crops, with its grain serving as a predominant staple food source on a global scale. However, there are many biotic and abiotic stresses challenging the stability of wheat production. Among the abiotic stresses, drought is recognized as a significant stress and poses a substantial threat to food production and quality throughout the world. Raising drought tolerance of wheat varieties through genetic regulation is therefore considered as one of the most effective ways to combat the challenges caused by drought stress. Meta-QTL analysis has demonstrated its effectiveness in identifying consensus QTL regions in wheat drought resistance in numerous instances. In this study, we present a comprehensive meta-analysis aimed at unraveling the drought tolerance genetic basis associated with agronomic traits in bread wheat. Extracting data from 34 previously published studies, we aggregated a corpus of 1291 Quantitative Trait Loci (QTL) pertinent to wheat drought tolerance. Then, the translation of the consensus genetic map yielded a comprehensive compendium of 49 distinct MQTLs, each associated with diverse agronomic traits. Prominently featured among the MQTLs were MQTLs 1.1, 1.7, 1.8 (1D), 4.1 (4A), 4.6 (4D), 5.2 (5B), 6.6 (6B), and 7.2 (7B), distinguished as pivotal MQTLs offering significant potential for application in marker-assisted breeding endeavors. Altogether, a total of 66 putative candidate genes (CGs)-related drought tolerance were identified. This work illustrates a translational research approach in transferring information from published mapping studies to genomic regions hosting major QTLs governing key agronomical traits in wheat.

Unveiling the hidden world: How arbuscular mycorrhizal fungi and its regulated core fungi modify the composition and metabolism of soybean rhizosphere microbiome

BACKGROUND

The symbiosis between arbuscular mycorrhizal fungi (AMF) and plants often stimulates plant growth, increases agricultural yield, reduces costs, thereby providing significant economic benefits. AMF can also benefit plants through affecting the rhizosphere microbial community, but the underlying mechanisms remain unclear. Using Rhizophagus intraradices as a model AMF species, we assessed how AMF influences the bacterial composition and functional diversity through 16 S rRNA gene sequencing and non-targeted metabolomics analysis in the rhizosphere of aluminum-sensitive soybean that were inoculated with pathogenic fungus Nigrospora oryzae and phosphorus-solubilizing fungus Talaromyces verruculosus in an acidic soil.

RESULTS

The inoculation of R. intraradices, N. oryzae and T. verruculosus didn't have a significant influence on the levels of soil C, N, and P, or various plant characteristics such as seed weight, crude fat and protein content. However, their inoculation affected the structure, function and nutrient dynamics of the resident bacterial community. The co-inoculation of T. verruculosus and R. intraradices increased the relative abundance of Pseudomonas psychrotolerans, which was capable of N-fixing and was related to cry-for-help theory (plants signal for beneficial microbes when under stress), within the rhizosphere. R. intraradices increased the expression of metabolic pathways associated with the synthesis of unsaturated fatty acids, which was known to enhance plant resistance under adverse environmental conditions. The inoculation of N. oryzae stimulated the stress response inside the soil environment by enriching the polyene macrolide antifungal antibiotic-producing bacterial genus Streptomyces in the root endosphere and upregulating two antibacterial activity metabolic pathways associated with steroid biosynthesis pathways in the rhizosphere. Although inoculation of pathogenic fungus N. oryzae enriched Bradyrhizobium and increased soil urease activity, it had no significant effects on biomass and N content of soybean. Lastly, the host niches exhibited differences in the composition of the bacterial community, with most N-fixing bacteria accumulating in the endosphere and Rhizobium vallis only detected in the endosphere.

CONCLUSIONS

Our findings demonstrate that intricate interactions between AMF, associated core fungi, and the soybean root-associated ecological niches co-mediate the regulation of soybean growth, the dynamics of rhizosphere soil nutrients, and the composition, function, and metabolisms of the root-associated microbiome in an acidic soil.

Agricultural intensification in Lake Naivasha Catchment in Kenya and associated nutrients and pesticides pollution

Investments in agricultural intensification in sub-Saharan Africa aim to fulfill food and economic demands. However, the increased use of fertilizers and pesticides poses ecological risks to water bodies in agricultural catchments. This study focused on assessing the impact of agricultural intensification on nutrient and pesticide pollution in the L. Naivasha catchment in Kenya. The research revealed significant changes in the catchment's agricultural landscape between 1989 and 2019, driven by intensified agricultural expansion. As a result, nutrient and pesticide emissions have worsened the lake's trophic status, shifting it towards hypereutrophic conditions. The study found a weak relationship between total nitrogen (TN) and sum dichlorodiphenyltrichloroethane (∑DDT), indicating that an increase in TN slightly predicted a reduction in ∑DDT. Analysis also showed potential phosphorus (P) limitation in the lake. Additionally, the observed ratio between dichlorodiphenyldichloroethane and dichlorodiphenyldichloroethylene (DDD:DDE) and (DDE + DDD):DDT ratios suggest recent use of banned DDT in the catchment. The study concludes that the transformation of L. Naivasha landscape shows unsustainable agricultural expansion with reduced forest cover, increased croplands, and increased pesticide contamination. This reflects a common issue in sub-Saharan Africa, that sustainable catchment management must address, specifically for combined pollutants, to support water quality and achieve the SDGs in agriculture.

Unveiling Drought-Resilient Latin American Popcorn Lines through Agronomic and Physiological Evaluation

Water stress can lead to physiological and morphological damage, affecting the growth and development of popcorn. The objective of this study was to identify the yield potential of 43 popcorn lines derived from a Latin American germplasm collection, based on agronomic and physiological traits, under full irrigation (WW) and water deficit conditions (WS), aiming to select superior germplasm. The evaluated agronomic traits included the ear length and diameter, number of grains per row (NGR) and rows per ear (NRE), grain yield (GY), popping expansion (EC), volume of expanded popcorn per hectare (VP), grain length (GL), width, and thickness. The physiological traits included the chlorophyll, anthocyanin, and flavonoid content in the leaves. The genetic variability and distinct behavior among the lines for all the agronomic traits under WW and WS conditions were observed. When comparing the water conditions, line L292 had the highest mean for the GY, and line L688 had the highest mean for the EC, highlighting them as the most drought-tolerant lines. A water deficit reduced the leaf greenness but increased the anthocyanin content as an adaptive response. The GY trait showed positive correlations with the VP, NGR, and GL under both water conditions, making the latter useful for indirect selection and thus of great interest for plant breeding targeting the simultaneous improvement of these traits.

Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea

Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Abiotic Stress Tolerance Boosted by Genetic Diversity in Plants

Plant breeding [...].

Stress salinity in plants: New strategies to cope with in the foreseeable scenario

The excess of salts in soils causes stress in most plants, except for some halophytes that can tolerate higher levels of salinity. The excess of Na+ generates an ionic imbalance, reducing the K+ content and altering cellular metabolism, thus impacting in plant growth and development. Additionally, salinity in soil induces water stress due to osmotic effects and increments the production of reactive oxygen species (ROS) that affect the cellular structure, damaging membranes and proteins, and altering the electrochemical potential of H+, which directly affects nutrient absorption by membrane transporters. However, plants possess mechanisms to overcome the toxicity of the sodium ions, such as internalization into the vacuole or exclusion from the cell, synthesis of enzymes or protective compounds against ROS, and the synthesis of metabolites that help to regulate the osmotic potential of plants. Physiologic and molecular mechanisms of salinity tolerance in plants will be addressed in this review. Furthermore, a revision of strategies taken by researchers to confer salt stress tolerance on agriculturally important species are discussed. These strategies include conventional breeding and genetic engineering as transgenesis and genome editing by CRISPR/Cas9.

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required.

Molecular identification and validation of four stable QTL for adult-plant resistance to powdery mildew in Chinese wheat cultivar Bainong 64

KEY MESSAGE

Four stable QTL for adult-plant resistance (APR) to powdery mildew were identified on chromosome arms 1DL, 2BS, 2DL, and 6BL in the widely grown Chinese wheat cultivar Bainong 64. These QTL had no effect on response to stripe rust or leaf rust. Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a devastating fungal disease. Seedlings of Chinese wheat Bainong 64 are susceptible to Bgt, but adult plants have maintained resistance since it was released in 1996. A population of 171 recombinant inbred lines (RILs) developed from cross Jingshuang 16/Bainong 64 (JS16/BN64) was used to dissect genetic components of powdery mildew resistance. A genetic map comprising 5383 polymorphic markers was constructed using the 15 K SNP chip and kompetitive allele-specific PCR (KASP) markers. Composite interval mapping identified four stable QTL with favorable alleles all from BN64 on chromosome arms 1DL, 2BS, 2DL, and 6BL in at least four environments. They accounted for 8.3%, 13.8%, 14.4%, and 9.0% of the total phenotypic variation explained (PVE) in maximum, respectively. QPmjbr.caas-1DL, situated about 22 Mb from centromere, is probably a new QTL. QPmjbr.caas-2DL located near the end of arm 2DL and explained the largest PVE. Using genetic maps populated with KASP markers, QPmjbr.caas-2BS and QPmjbr.caas-6BL were fine mapped to a 1.8 cM genetic intervals spanning 13.6 Mb (76.0-89.6 Mb) and 1.7 cM and 4.9 Mb (659.9-664.8 Mb), respectively. The four QTL independent of stripe rust and leaf rust resistance were validated for powdery mildew resistance in another RIL population related to BN64 and a cultivar panel using representative KASP markers. Since BN64 has been a leading cultivar and an important breeding parent in China, the QTL and markers reported in this study will be useful for marker-assisted selection of APR.

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome-host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum

In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Plant genome resequencing and population genomics: Current status and future prospects

Advances in DNA sequencing technology have sparked a genomics revolution, driving breakthroughs in plant genetics and crop breeding. Recently, the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement. This transformation has been facilitated by the increasing adoption of whole-genome resequencing. In this review, we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding. A total of 187 land plants from 163 countries have been resequenced, comprising 54 413 accessions. As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted. Economically important crops, particularly cereals, vegetables, and legumes, have dominated the resequencing efforts, leaving a gap in 49 orders, including Lycopodiales, Liliales, Acorales, Austrobaileyales, and Commelinales. The resequenced germplasm is distributed across diverse geographic locations, providing a global perspective on plant genomics. We highlight genes that have been selected during domestication, or associated with agronomic traits, and form a repository of candidate genes for future research and application. Despite the opportunities for cross-species comparative genomics, many population genomic datasets are not accessible, impeding secondary analyses. We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy. The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs, coupled with advances in analysis and computational technologies. This expansion, in terms of both scale and quality, holds promise for deeper insights into plant trait genetics and breeding design.

The potential of metabolomics in assessing global compositional changes resulting from the application of CRISPR/Cas9 technologies

Exhaustive analysis of genetically modified crops over multiple decades has increased societal confidence in the technology. New Plant Breeding Techniques are now emerging with improved precision and the ability to generate products containing no foreign DNA and mimic/replicate conventionally bred varieties. In the present study, metabolomic analysis was used to compare (i) tobacco genotypes with and without the CRISPR associated protein 9 (Cas9), (ii) tobacco lines with the edited and non-edited DE-ETIOLATED-1 gene without phenotype and (iii) leaf and fruit tissue from stable non-edited tomato progeny with and without the Cas9. In all cases, multivariate analysis based on the difference test using LC-HRMS/MS and GC-MS data indicated no significant difference in their metabolomes. The variations in metabolome composition that were evident could be associated with the processes of tissue culture regeneration and/or transformation (e.g. interaction with Agrobacterium). Metabolites responsible for the variance included quantitative changes of abundant, well characterised metabolites such as phenolics (e.g. chlorogenic acid) and several common sugars such as fructose. This study provides fundamental data on the characterisation of gene edited crops, that are important for the evaluation of the technology and its assessment. The approach also suggests that metabolomics could contribute to routine product-based analysis of crops/foods generated from New Plant Breeding approaches.

Basil (Ocimum basilicum) Landraces Can Be Used in a Water-Limited Environment

Basil (Ocimum basilicum L.) is a member of the Labiatae family and is one of the most widely consumed aromatic and medicinal plants in many countries due to its numerous properties and uses. The objective of the study was to determine whether landraces are better adapted to water-limited environments compared to commercial cultivars. Irrigation levels and genotypes affected plant height and leaf area index, with 25% and 33% higher values observed under complete irrigation, respectively. Additionally, limited water availability resulted in a 20% reduction in dry matter yield and a 21% reduction in essential oil yield over the three years in all of the genotypes tested, specifically in the lower irrigation treatment (d₄₀), compared to the control treatment (d₁₀₀). The landraces that performed the best under limited water supply were Athos white spike (AWS) and Gigas white spike (GWS), indicating their suitability for environments with limited water resources. The results demonstrate that there are landraces that can be utilized in dryland climates with appropriate water management, enabling water conservation and utilization of fields in water-scarce areas for irrigation purposes.

Genetically modified (GM) late blight-resistant potato and consumer attitudes before and after a field visit

Late blight, caused by Phytophthora infestans, is the most devastating disease in potato production. Here, we show full late blight resistance in a location with a genetically diverse pathogen population with the use of GM potato stacked with three resistance (R) genes over three seasons. In addition, using this field trials, we demonstrate that in-the-field intervention among consumers led to change for more favorable attitude generally toward GM crops.

Relevance of the Exocyst in Arabidopsis exo70e2 Mutant for Cellular Homeostasis under Stress

Plants must adapt to cope with adverse environmental conditions that affect their growth and development. To overcome these constraints, they can alter their developmental patterns by modulating cellular processes and activating stress-responsive signals. Alongside the activation of the antioxidant (AOX) system, a high number of genes are expressed, and proteins must be distributed to the correct locations within the cell. The endomembrane system and associated vesicles thus play an important role. Several pathways have been associated with adverse environmental conditions, which is the case for the exocyst-positive organelle-EXPO. The present work, using Arabidopsis mutants with T-DNA insertions in the gene EXO70, essential for EXPO vesicles formation, was designed to characterise the anatomical (morphology and root length), biochemical (quantification of stress markers and antioxidant system components), and molecular responses (gene expression) to abiotic stresses (saline, drought, oxidative, and metal-induced toxicity). The results obtained showed that mutant plants behave differently from the wild type (WT) plants. Therefore, in the exo70 mutant, morphological changes were more noticeable in plants under stress, and the non-enzymatic component of the antioxidant system was activated, with no alterations to the enzymatic component. Furthermore, other defence strategies, such as autophagy, did not show important changes. These results confirmed the EXPO as an important structure for tolerance/adaptation to stress.

Direct and indirect impacts of synthetic biology on biodiversity conservation

The world's biodiversity is in crisis. Synthetic biology has the potential to transform biodiversity conservation, both directly and indirectly, in ways that are negative and positive. However, applying these biotechnology tools to environmental questions is fraught with uncertainty and could harm cultures, rights, livelihoods, and nature. Decisions about whether or not to use synthetic biology for conservation should be understood alongside the reality of ongoing biodiversity loss. In 2022, the 196 Parties to the United Nations Convention on Biological Diversity are negotiating the post-2020 Global Biodiversity Framework that will guide action by governments and other stakeholders for the next decade to conserve the worlds' biodiversity. To date, synthetic biologists, conservationists, and policy makers have operated in isolation. At this critical time, this review brings these diverse perspectives together and emerges out of the need for a balanced and inclusive examination of the potential application of these technologies to biodiversity conservation.

A comprehensive survey on computational learning methods for analysis of gene expression data

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Predicted impacts of climate change on wild and commercial berry habitats will have food security, conservation and agricultural implications

Climate change is now a reality and is altering ecosystems, with Canada experiencing 2-4 times the global average rate of warming. This will have a critical impact on berry cultivation and horticulture. Enhancing our understanding of how wild and cultivated berries will perform under changing climates will be essential to mitigating impacts on ecosystems, culture and food security. Our objective was to predict the impact of climate change on habitat suitability of four berry producing Vaccinium species: two species with primarily northern distributions (V. uliginosum, V. vitis-idaea), one species with a primarily southern distribution (V. oxycoccos), and the commercially cultivated V. macrocarpon. We used the maximum entropy (Maxent) model and the CMIP6 shared socioeconomic pathways (SSPs) 126 and 585 projected to 2041-2060 and 2061-2080. Wild species showed a uniform northward progression and expansion of suitable habitat. Our modeling predicts that suitable growing regions for commercial cranberries are also likely to shift with some farms becoming unsuitable for the current varieties and other regions becoming more suitable for cranberry farms. Both V. macrocarpon and V. oxycoccos showed a high dependence on precipitation-associated variables. Vaccinium vitis-idaea and V. uliginosum had a greater number of variables with smaller contributions which may improve their resilience to individual climactic events. Future competition between commercial cranberry farms and wild berries in protected areas could lead to conflicts between agriculture and conservation priorities. New varieties of commercial berries are required to maintain current commercial berry farms.

A gain-of-function allele of a DREB transcription factor gene ameliorates drought tolerance in wheat

Drought is a major environmental factor limiting wheat production worldwide. However, the genetic components underlying wheat drought tolerance are largely unknown. Here, we identify a DREB transcription factor gene (TaDTG6-B) by genome-wide association study that is tightly associated with drought tolerance in wheat. Candidate gene association analysis revealed that a 26-bp deletion in the TaDTG6-B coding region induces a gain-of-function for TaDTG6-BDel574, which exhibits stronger transcriptional activation, protein interactions, and binding activity to dehydration-responsive elements (DRE)/CRT cis-elements than the TaDTG6-BIn574 encoded by the allele lacking the deletion, thus conferring greater drought tolerance in wheat seedlings harboring this variant. Knockdown of TaDTG6-BDel574 transcripts attenuated drought tolerance in transgenic wheat, whereas its overexpression resulted in enhanced drought tolerance without accompanying phenotypic abnormalities. Furthermore, the introgression of the TaDTG6-BDel574 elite allele into drought-sensitive cultivars improved their drought tolerance, thus providing a valuable genetic resource for wheat breeding. We also identified 268 putative target genes that are directly bound and transcriptionally regulated by TaDTG6-BDel574. Further analysis showed that TaDTG6-BDel574 positively regulates TaPIF1 transcription to enhance wheat drought tolerance. These results describe the genetic basis and accompanying mechanism driving phenotypic variation in wheat drought tolerance, and provide a novel genetic resource for crop breeding programs.

Pathways to sustainable land use and food systems in Canada

Meeting global sustainability targets under the United Nations Sustainable Development Goals and the Paris Agreement requires paying attention to major land-use sectors such as forestry and agriculture. These sectors play a large role in national emissions, biodiversity conservation, and human well-being. There are numerous possible pathways to sustainability in these sectors and potential synergies and trade-offs along those pathways. This paper reports on the use of a model for Canada's land use to 2050 to assess three different pathways (one based on current trends and two with differing levels of ambition for meeting sustainability targets). This was done as part of a large international consortium, Food, Agriculture, Biodiversity, Land and Energy (FABLE), which allows for incorporating international trade in meeting both national and global sustainability targets. The results show not only the importance of increasingly stringent policies in meeting the targets, but also the role that population and consumption (e.g., diets) play in meeting the targets. Both the medium and high ambition sustainability pathways can drastically reduce greenhouse gas emissions while protecting forestland.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01213-z.

Smart breeding approaches in post-genomics era for developing climate-resilient food crops

Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.

Pangenomics and Crop Genome Adaptation in a Changing Climate

During crop domestication and breeding, wild plant species have been shaped into modern high-yield crops and adapted to the main agro-ecological regions. However, climate change will impact crop productivity in these regions, and agriculture needs to adapt to support future food production. On a global scale, crop wild relatives grow in more diverse environments than crop species, and so may host genes that could support the adaptation of crops to new and variable environments. Through identification of individuals with increased climate resilience we may gain a greater understanding of the genomic basis for this resilience and transfer this to crops. Pangenome analysis can help to identify the genes underlying stress responses in individuals harbouring untapped genomic diversity in crop wild relatives. The information gained from the analysis of these pangenomes can then be applied towards breeding climate resilience into existing crops or to re-domesticating crops, combining environmental adaptation traits with crop productivity.

The impact of aquaculture system on the microbiome and gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis)

The commercial aquatic animal microbiome may markedly affect the successful host's farming in various aquaculture systems. However, very little was known about it. Here, two different aquaculture systems, the rice-fish culture (RFC) and intensive pond culture (IPC) systems, were compared to deconstruct the skin, oral, and gut microbiome, as well as the gut metabolome of juvenile Chinese softshell turtle (Pelodiscus sinensis). Higher alpha-diversity and functional redundancy of P. sinensis microbial community were found in the RFC than those of the IPC. The aquaculture systems have the strongest influence on the gut microbiome, followed by the skin microbiome, and finally the oral microbiome. Source-tracking analysis showed that the RFC's microbial community originated from more unknown sources than that of the IPC across all body regions. Strikingly, the RFC's oral and skin microbiome exhibited a significantly higher proportion of generalists and broader habitat niche breadth than those of the IPC, but not the gut. Null model analysis revealed that the RFC's oral and skin microbial community assembly was governed by a significantly greater proportion of deterministic processes than that of the IPC, but not the gut. We further identified the key gene and microbial contribution to five significantly changed gut metabolites, 2-oxoglutarate, N-acetyl-d-mannosamine, cis-4-hydroxy-d-proline, nicotinamide, and l-alanine, which were significantly correlated with important categories of microbe-mediated processes, including the amino acid metabolism, GABAergic synapse, ABC transporters, biosynthesis of unsaturated fatty acids, as well as citrate cycle. Moreover, different aquaculture systems have a significant impact on the hepatic lipid metabolism and body shape of P. sinensis. Our results provide new insight into the influence of aquaculture systems on the microbial community structure feature and assembly mechanism in an aquatic animal, also highlighting the key microbiome and gene contributions to the metabolite variation in the gut microbiome-metabolome association.

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Improvement of prediction ability by integrating multi-omic datasets in barley

Background

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Results

The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.

Conclusions

The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08337-7).

Expanding Gene-Editing Potential in Crop Improvement with Pangenomes

Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations.

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (Melaleuca alternifolia). They produce a variety of nutrients for the daily human diet in addition to the nutrition provided by staple crops, and some of them additionally possess ornamental and medicinal features. As such, horticultural crops make unique and important contributions to both food security and a colorful lifestyle. Under the current climate change scenario, the growing population and limited arable land means that agriculture, and especially horticulture, has been facing unprecedented challenges to meet the diverse demands of human daily life. Breeding horticultural crops with high quality and adaptability and establishing an effective system that combines cultivation, post-harvest handling, and sales becomes increasingly imperative for horticultural production. This review discusses characteristic and recent research highlights in horticultural crops, focusing on the breeding of quality traits and the mechanisms that underpin them. It additionally addresses challenges and potential solutions in horticultural production and post-harvest practices. Finally, we provide a prospective as to how emerging technologies can be implemented alongside interdisciplinary basic research to enhance our understanding and exploitation of horticultural crops.

New approaches to improve crop tolerance to biotic and abiotic stresses

During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.

Beyond the Cereal Box: Breeding Buckwheat as a Strategic Crop for Human Nutrition

While intensification of farming systems is essential for achieving the Millennium Development Goal of "Zero hunger", issues such as availability of nutritious foods would demand increased attention if any long-term form of food security is to be achieved. Since wheat, rice and maize have reached near to 80 percent of their yield potential and reliance on these crops alone would not be sufficient to close the gap between demand and supply, there is a need to bring other climate-resilient and nutritionally dense crops into agricultural portfolio. Buckwheat (Fagopyrum spp.) has attracted considerable interest amongst global scientific community due to its nutritional and pharmaceutical properties. The gluten free nature of buckwheat, nutritionally balanced amino acid composition of its grain protein, and high levels of anti-oxidants, such as rutin, makes buckwheat an important crop with immense nutraceutical benefits. However, a key challenge in buckwheat cultivation is the variation in yield between years, which impacts the entire value chain. Current information on buckwheat indicates existence of significant phenotypic variation for agronomic and nutritional traits. However, genetic bottlenecks in conventional breeding restrict effective utilization of the existing diversity in mainstreaming buckwheat cultivation. Availability of high density buckwheat genome map for both the cultivated species viz. F. esculentum and F. tataricum would add to our understanding of genetic basis of their agronomic traits. The review examines the potential of buckwheat as a strategic crop for human nutrition and prospects of effective exploitation genomic information of common and Tartary buckwheat for genome assisted breeding.

Genetic and Proteomic Basis of Sclerotinia Stem Rot Resistance in Indian Mustard [Brassica juncea (L.) Czern & Coss.]

Sclerotinia stem rot is one of the utmost important disease of mustard, causing considerable losses in seed yield and oil quality. The study of the genetic and proteomic basis of resistance to this disease is imperative for its effective utilization in developing resistant cultivars. Therefore, the genetic pattern of Sclerotinia stem rot resistance in Indian mustard was studied using six generations (P₁, P₂, F₁, F₂, BC₁P₁, and BC₁P₂) developed from the crossing of one resistant (RH 1222-28) and two susceptible (EC 766300 and EC 766123) genotypes. Genetic analysis revealed that resistance was governed by duplicate epistasis. Comparative proteome analysis of resistant and susceptible genotypes indicated that peptidyl-prolyl cis-trans isomerase (A0A078IDN6 PPIase) showed high expression in resistant genotype at the early infection stage while its expression was delayed in susceptible genotypes. This study provides important insight to mustard breeders for designing effective breeding programs to develop resistant cultivars against this devastating disease.

Rewilding crops for climate resilience: economic analysis and de novo domestication strategies

To match predicted population growth, annual food production should be doubled by 2050. This is not achievable by current agronomical and breeding practices, due to the impact of climate changes and associated abiotic stresses on agricultural production systems. Here, we analyze the impact of global climate trends on crop productivity and show that the overall loss in crop production from climate-driven abiotic stresses may exceed US$170 billion year-1 and represents a major threat to global food security. We also show that abiotic stress tolerance had been present in wild progenitors of modern crops but was lost during their domestication. We argue for a major shift in our paradigm of crop breeding, focusing on climate resilience, and call for a broader use of wild relatives as a major tool in this process. We argue that, while molecular tools are currently in place to harness the potential of climate-resilient genes present in wild relatives, the complex polygenic nature of tolerance traits remains a major bottleneck in this process. Future research efforts should be focused not only on finding appropriate wild relatives but also on development of efficient cell-based high-throughput phenotyping platforms allowing assessment of the in planta operation of key genes.

Nanoceria seed priming enhanced salt tolerance in rapeseed through modulating ROS homeostasis and α-amylase activities

BACKGROUND

Salinity is a big threat to agriculture by limiting crop production. Nanopriming (seed priming with nanomaterials) is an emerged approach to improve plant stress tolerance; however, our knowledge about the underlying mechanisms is limited.

RESULTS

Herein, we used cerium oxide nanoparticles (nanoceria) to prime rapeseeds and investigated the possible mechanisms behind nanoceria improved rapeseed salt tolerance. We synthesized and characterized polyacrylic acid coated nanoceria (PNC, 8.5 ± 0.2 nm, -43.3 ± 6.3 mV) and monitored its distribution in different tissues of the seed during the imbibition period (1, 3, 8 h priming). Our results showed that compared with the no nanoparticle control, PNC nanopriming improved germination rate (12%) and biomass (41%) in rapeseeds (Brassica napus) under salt stress (200 mM NaCl). During the priming hours, PNC were located mostly in the seed coat, nevertheless the intensity of PNC in cotyledon and radicle was increased alongside with the increase of priming hours. During the priming hours, the amount of the absorbed water (52%, 14%, 12% increase at 1, 3, 8 h priming, respectively) and the activities of α-amylase were significantly higher (175%, 309%, 295% increase at 1, 3, 8 h priming, respectively) in PNC treatment than the control. PNC primed rapeseeds showed significantly lower content of MDA, H2O2, and •O2- in both shoot and root than the control under salt stress. Also, under salt stress, PNC nanopriming enabled significantly higher K+ retention (29%) and significantly lower Na+ accumulation (18.5%) and Na+/K+ ratio (37%) than the control.

CONCLUSIONS

Our results suggested that besides the more absorbed water and higher α-amylase activities, PNC nanopriming improves salt tolerance in rapeseeds through alleviating oxidative damage and maintaining Na+/K+ ratio. It adds more knowledge regarding the mechanisms underlying nanopriming improved plant salt tolerance.

De-novo Domestication for Improving Salt Tolerance in Crops

Global agriculture production is under serious threat from rapidly increasing population and adverse climate changes. Food security is currently a huge challenge to feed 10 billion people by 2050. Crop domestication through conventional approaches is not good enough to meet the food demands and unable to fast-track the crop yields. Also, intensive breeding and rigorous selection of superior traits causes genetic erosion and eliminates stress-responsive genes, which makes crops more prone to abiotic stresses. Salt stress is one of the most prevailing abiotic stresses that poses severe damages to crop yield around the globe. Recent innovations in state-of-the-art genomics and transcriptomics technologies have paved the way to develop salinity tolerant crops. De novo domestication is one of the promising strategies to produce superior new crop genotypes through exploiting the genetic diversity of crop wild relatives (CWRs). Next-generation sequencing (NGS) technologies open new avenues to identifying the unique salt-tolerant genes from the CWRs. It has also led to the assembly of highly annotated crop pan-genomes to snapshot the full landscape of genetic diversity and recapture the huge gene repertoire of a species. The identification of novel genes alongside the emergence of cutting-edge genome editing tools for targeted manipulation renders de novo domestication a way forward for developing salt-tolerance crops. However, some risk associated with gene-edited crops causes hurdles for its adoption worldwide. Halophytes-led breeding for salinity tolerance provides an alternative strategy to identify extremely salt tolerant varieties that can be used to develop new crops to mitigate salinity stress.

Reading between the Lines: RNA-seq Data Mining Reveals the Alternative Message of the Rice Leaf Transcriptome in Response to Heat Stress

Rice (Oryza sativa L.) is a major food crop but heat stress affects its yield and grain quality. To identify mechanistic solutions to improve rice yield under rising temperatures, molecular responses of thermotolerance must be understood. Transcriptional and post-transcriptional controls are involved in a wide range of plant environmental responses. Alternative splicing (AS), in particular, is a widespread mechanism impacting the stress defence in plants but it has been completely overlooked in rice genome-wide heat stress studies. In this context, we carried out a robust data mining of publicly available RNA-seq datasets to investigate the extension of heat-induced AS in rice leaves. For this, datasets of interest were subjected to filtering and quality control, followed by accurate transcript-specific quantifications. Powerful differential gene expression (DE) and differential AS (DAS) identified 17,143 and 2162 heat response genes, respectively, many of which are novel. Detailed analysis of DAS genes coding for key regulators of gene expression suggests that AS helps shape transcriptome and proteome diversity in response to heat. The knowledge resulting from this study confirmed a widespread transcriptional and post-transcriptional response to heat stress in plants, and it provided novel candidates for rapidly advancing rice breeding in response to climate change.

Next-Generation Breeding Strategies for Climate-Ready Crops

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Genome-wide association mapping of lentil (Lens culinaris Medikus) prebiotic carbohydrates toward improved human health and crop stress tolerance

Lentil, a cool-season food legume, is rich in protein and micronutrients with a range of prebiotic carbohydrates, such as raffinose-family oligosaccharides (RFOs), fructooligosaccharides (FOSs), sugar alcohols (SAs), and resistant starch (RS), which contribute to lentil's health benefits. Beneficial microorganisms ferment prebiotic carbohydrates in the colon, which impart health benefits to the consumer. In addition, these carbohydrates are vital to lentil plant health associated with carbon transport, storage, and abiotic stress tolerance. Thus, lentil prebiotic carbohydrates are a potential nutritional breeding target for increasing crop resilience to climate change with increased global nutritional security. This study phenotyped a total of 143 accessions for prebiotic carbohydrates. A genome-wide association study (GWAS) was then performed to identify associated variants and neighboring candidate genes. All carbohydrates analyzed had broad-sense heritability estimates (H2) ranging from 0.22 to 0.44, comparable to those reported in the literature. Concentration ranges corresponded to percent recommended daily allowances of 2-9% SAs, 7-31% RFOs, 51-111% RS, and 57-116% total prebiotic carbohydrates. Significant SNPs and associated genes were identified for numerous traits, including a galactosyltransferase (Lcu.2RBY.1g019390) known to aid in RFO synthesis. Further studies in multiple field locations are necessary. Yet, these findings suggest the potential for molecular-assisted breeding for prebiotic carbohydrates in lentil to support human health and crop resilience to increase global food security.

Refined mapping of stripe rust resistance gene YrP10090 within a desirable haplotype for wheat improvement on chromosome 6A

A large genomic region spanning over 300 Mb on chromosome 6A under intense artificial selection harbors multiple loci associated with favorable traits including stripe rust resistance in wheat. The development of resistance cultivars can be an optimal strategy for controlling wheat stripe rust disease. Although loci for stripe rust resistance have been identified on chromosome 6A in previous studies, it is unclear whether these loci span a common genetic interval, and few studies have attempted to analyze the haplotype changes that have accompanied wheat improvement over the period of modern breeding. In this study, we used F_2:3 families and F_6:7 recombinant inbred lines (RILs) derived from a cross between a resistant CIMMYT wheat accession P10090 and the susceptible landrace Mingxian 169 to improve the resolution of the QTL on chromosome 6A. The co-located QTL, designated as YrP10090, was flanked by SNP markers AX-94460938 and AX-110585473 with a genetic interval of 3.5 cM, however, corresponding to a large physical distance of over 300 Mb in RefSeq v.1.0 (positions 107.1-446.5 Mb). More than 1,300 SNP markers in this genetic region were extracted for haplotype analysis in a panel of 1,461 worldwide common wheat accessions, and three major haplotypes (Hap1, Hap2, and Hap3) were identified. The favorable haplotype Hap1 associated with stripe rust resistance exhibited a large degree of linkage disequilibrium. Selective sweep analyses were performed between different haplotype groups, revealing specific genomic regions with strong artificial selection signals. These regions harbored multiple desirable traits associated with resilience to environmental stress, different yield components, and quality characteristics. P10090 and its derivatives that carry the desirable haplotype can provide a concrete foundation for bread wheat improvement including the genomic selection.

Plant genomics in Africa: present and prospects

Plants are the world's most consumed goods. They are of high economic value and bring many health benefits. In most countries in Africa, the supply and quality of food will rise to meet the growing population's increasing demand. Genomics and other biotechnology tools offer the opportunity to improve subsistence crops and medicinal herbs in the continent. Significant advances have been made in plant genomics, which have enhanced our knowledge of the molecular processes underlying both plant quality and yield. The sequencing of complex genomes of African plant species, facilitated by the continuously evolving next-generation sequencing technologies and advanced bioinformatics approaches, has provided new opportunities for crop improvement. This review summarizes the achievements of genome sequencing projects of endemic African plants in the last two decades. We also present perspectives and challenges for future plant genomic studies that will accelerate important plant breeding programs for African communities. These challenges include a lack of basic facilities, a lack of sequencing and bioinformatics facilities, and a lack of skills to design genomics studies. However, it is imperative to state that African countries have become key players in the plant genome revolution and genome derived-biotechnology. Therefore, African governments should invest in public plant genomics research and applications, establish bioinformatics platforms and training programs, and stimulate university and industry partnerships to fully deploy plant genomics, particularly in the fields of agriculture and medicine.

Deploying Big Data to Crack the Genotype to Phenotype Code

Mechanistically connecting genotypes to phenotypes is a longstanding and central mission of biology. Deciphering these connections will unite questions and datasets across all scales from molecules to ecosystems. Although high-throughput sequencing has provided a rich platform on which to launch this effort, tools for deciphering mechanisms further along the genome to phenome pipeline remain limited. Machine learning approaches and other emerging computational tools hold the promise of augmenting human efforts to overcome these obstacles. This vision paper is the result of a Reintegrating Biology Workshop, bringing together the perspectives of integrative and comparative biologists to survey challenges and opportunities in cracking the genotype to phenotype code and thereby generating predictive frameworks across biological scales. Key recommendations include promoting the development of minimum "best practices" for the experimental design and collection of data; fostering sustained and long-term data repositories; promoting programs that recruit, train, and retain a diversity of talent; and providing funding to effectively support these highly cross-disciplinary efforts. We follow this discussion by highlighting a few specific transformative research opportunities that will be advanced by these efforts.

Harnessing Crop Wild Diversity for Climate Change Adaptation

Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

Bambara Groundnut (Vigna subterranea L. Verdc): A Crop for the New Millennium, Its Genetic Diversity, and Improvements to Mitigate Future Food and Nutritional Challenges

The world’s food and agricultural schemes have gradually fallen into an alarming state due to challenges such as high population birth rates, diverse agro-climatic zones, a lack of measures to counter global warming, severe practices of sole-culture cultivation, and asset reduction. A very high dependency on limited staple food crops is associated with repetitious diets, deprivation of food, and shortages of trace minerals, which often causes dietary sicknesses. To ensure nutritious diets worldwide, a real-world and justifiable scheme is provided to garner extra attention towards variation in both agriculture/farming approaches and food habits. The EAT-Lancet statement emphasized an increase in agri-based diets as a way of attaining global generational health. Enlarging neglected crops with plenty of genomic stocks and potentially profitable attributes is a solution that could address food and nutritional security concerns. Bambara groundnut is one such imperative and neglected legume crop that contributes positively to improving global food and nutrient safety. As a “complete food”, this crop has recently been treated as a new millennium crop, and furthermore, it is more adjusted to poor soil and climatic conditions than other dominant crops. Bambara groundnut is a repository of vital nutrients that provides carbohydrates, crucial amino acids, proteins, and energy as well as minerals and vitamins to developed and low-income countries where animal proteins are not readily available. This review explores the potential of Bambara groundnut in ensuring food and nutrient security; its variables, production, processing, nutrient values, role in reducing the nutritional gap, and diverse uses; and attempts in improving its traits. To strengthen food production, an agricultural revolution is required for underutilized crop species to feed the ever-expanding population in the world. Henceforth, advanced plant-breeding procedures, such as next-generation breeding techniques, various molecular tools, TILLING, Eco-TILLING, proteomics, genomics, and transcriptomics (which has been used for major crops), also need to be practiced to intensify production. To boost productivity and to feed the most starved and malnourished populations of the world, it is assumed that the application of modern techniques will play a vital role in the advancement of the underutilized Bambara groundnut.

New genomic approaches for enhancing maize genetic improvement

Maize (Zea mays) is one of the most widely grown crops in the world, with an annual global production of over 1147 million tons. Genomics approaches are thought to be the best solution for accelerating yield improvement to meet the challenges of a growing population and global climate change. Here, we review current approaches to the exploration of novel genetic variation in genomes, DNA modifications, and transcription levels of cultivated maize, landraces, and wild relatives. We discuss applications of genetic engineering to maize yield improvement and highlight future directions for maize genomics studies.

Advanced domestication: harnessing the precision of gene editing in crop breeding

Human population growth has increased the demand for food crops, animal feed, biofuel and biomaterials, all the while climate change is impacting environmental growth conditions. There is an urgent need to develop crop varieties which tolerate adverse growth conditions while requiring fewer inputs. Plant breeding is critical to global food security and, while it has benefited from modern technologies, it remains constrained by a lack of valuable genetic diversity, linkage drag, and an effective way to combine multiple favourable alleles for complex traits. CRISPR/Cas technology has transformed genome editing across biological systems and promises to transform agriculture with its high precision, ease of design, multiplexing ability and low cost. We discuss the integration of CRISPR/Cas-based gene editing into crop breeding to advance domestication and refine inbred crop varieties for various applications and growth environments. We highlight the use of CRISPR/Cas-based gene editing to fix desirable allelic variants, generate novel alleles, break deleterious genetic linkages, support pre-breeding and for introgression of favourable loci into elite lines.