The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Functional divergences of natural variations of TaNAM-A1 in controlling leaf senescence during wheat grain filling

Leaf senescence is an essential physiological process related to grain yield potential and nutritional quality. Green leaf duration (GLD) after anthesis directly reflects the leaf senescence process and exhibits large genotypic differences in common wheat; however, the underlying gene regulatory mechanism is still lacking. Here, we identified TaNAM-A1 as the causal gene of the major loci qGLD-6A for GLD during grain filling by map-based cloning. Transgenic assays and TILLING mutant analyses demonstrated that TaNAM-A1 played a critical role in regulating leaf senescence, and also affected spike length and grain size. Furthermore, the functional divergences among the three haplotypes of TaNAM-A1 were systematically evaluated. Wheat varieties with TaNAM-A1d (containing two mutations in the coding DNA sequence of TaNAM-A1) exhibited a longer GLD and superior yield-related traits compared to those with the wild type TaNAM-A1a. All three haplotypes were functional in activating the expression of genes involved in macromolecule degradation and mineral nutrient remobilization, with TaNAM-A1a showing the strongest activity and TaNAM-A1d the weakest. TaNAM-A1 also modulated the expression of the senescence-related transcription factors TaNAC-S-7A and TaNAC016-3A. TaNAC016-3A enhanced the transcriptional activation ability of TaNAM-A1a by protein-protein interaction, thereby promoting the senescence process. Our study offers new insights into the fine-tuning of the leaf functional period and grain yield formation for wheat breeding under various geographical climatic conditions.

An Accurate Representation of the Number of bZIP Transcription Factors in the Triticum aestivum (Wheat) Genome and the Regulation of Functional Genes during Salt Stress

Climate change is dramatically increasing the overall area of saline soils around the world, which is increasing by approximately two million hectares each year. Soil salinity decreases crop yields and, thereby, makes farming less profitable, potentially causing increased poverty and hunger in many areas. A solution to this problem is increasing the salt tolerance of crop plants. Transcription factors (TFs) within crop plants represent a key to understanding salt tolerance, as these proteins play important roles in the regulation of functional genes linked to salt stress. The basic leucine zipper (bZIP) TF has a well-documented role in the regulation of salt tolerance. To better understand how bZIP TFs are linked to salt tolerance, we performed a genome-wide analysis in wheat using the Chinese spring wheat genome, which has been assembled by the International Wheat Genome Sequencing Consortium. We identified 89 additional bZIP gene sequences, which brings the total of bZIP gene sequences in wheat to 237. The majority of these 237 sequences included a single bZIP protein domain; however, different combinations of five other domains also exist. The bZIP proteins are divided into ten subfamily groups. Using an in silico analysis, we identified five bZIP genes (ABF2, ABF4, ABI5, EMBP1, and VIP1) that were involved in regulating salt stress. By scrutinizing the binding properties to the 2000 bp upstream region, we identified putative functional genes under the regulation of these TFs. Expression analyses of plant tissue that had been treated with or without 100 mM NaCl revealed variable patterns between the TFs and functional genes. For example, an increased expression of ABF4 was correlated with an increased expression of the corresponding functional genes in both root and shoot tissues, whereas VIP1 downregulation in root tissues strongly decreased the expression of two functional genes. Identifying strategies to sustain the expression of the functional genes described in this study could enhance wheat's salt tolerance.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

Machine learning bridges omics sciences and plant breeding

Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Wheat NAM genes regulate the majority of early monocarpic senescence transcriptional changes including nitrogen remobilization genes

Senescence enables the remobilization of nitrogen and micronutrients from vegetative tissues of wheat (Triticum aestivum L.) into the grain. Understanding the molecular players in this process will enable the breeding of wheat lines with tailored grain nutrient content. The NAC transcription factor NAM-B1 is associated with earlier senescence and higher levels of grain protein, iron, and zinc contents due to increased nutrient remobilization. To investigate how related NAM genes control nitrogen remobilization at the molecular level, we carried out a comparative transcriptomic study using flag leaves at 7 time points (3, 7, 10, 13, 15, 19, and 26 days after anthesis) in wild type and NAM RNA interference lines with reduced NAM gene expression. Approximately 2.5 times more genes were differentially expressed in wild type than NAM RNA interference plants during this early senescence time course (6,508 vs 2,605 genes). In both genotypes, differentially expressed genes were enriched for gene ontology terms related to photosynthesis, hormones, amino acid transport, and nitrogen metabolism. However, nitrogen metabolism genes including glutamine synthetase (GS1 and GS2), glutamate decarboxylase (GAD), glutamate dehydrogenase (GDH), and asparagine synthetase (ASN1) showed stronger or earlier differential expression in wild-type than in NAM RNA interference plants, consistent with higher nitrogen remobilization. The use of time course data identified the dynamics of NAM-regulated and NAM-independent gene expression changes during senescence and provides an entry point to functionally characterize the pathways regulating senescence and nutrient remobilization in wheat.

Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology

Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.

Genome-Wide Identification and Characterization of Oil-Body-Membrane Proteins in Polyploid Crop Brassica napus

Oil-body-membrane proteins (OBMPs) are essential structural molecules of oil bodies and also versatile metabolic enzymes involved in multiple cellular processes such as lipid metabolism, hormone signaling and stress responses. However, the global landscape for OBMP genes in oil crops is still lacking. Here, we performed genome-wide identification and characterization of OBMP genes in polyploid crop Brassica napus. B. napus contains up to 88 BnaOBMP genes including 53 oleosins, 20 caleosins and 15 steroleosins. Both whole-genome and tandem duplications have contributed to the expansion of the BnaOBMP gene family. These BnaOBMP genes have extensive sequence polymorphisms, and some harbor strong selection signatures. Various cis-acting regulatory elements involved in plant growth, phytohormones and abiotic and biotic stress responses are detected in their promoters. BnaOBMPs exhibit differential expression at various developmental stages from diverse tissues. Importantly, some BnaOBMP genes display spatiotemporal patterns of seed-specific expression, which could be orchestrated by transcriptional factors such as EEL, GATA3, HAT2, SMZ, DOF5.6 and APL. Altogether, our data lay the foundations for studying the regulatory mechanism of the seed oil storage process and provide candidate genes and alleles for the genetic improvement and breeding of rapeseed with high seed oil content.

RNAi silencing of wheat gliadins alters the network of transcription factors that regulate the synthesis of seed storage proteins toward maintaining grain protein levels

Gluten proteins are responsible for the unique viscoelastic properties of wheat dough, but they also trigger the immune response in celiac disease patients. RNA interference (RNAi) wheat lines with strongly silenced gliadins were obtained to reduce the immunogenic response of wheat. The E82 line presents the highest reduction of gluten, but other grain proteins increased, maintaining a total nitrogen content comparable to that of the wild type. To better understand the regulatory mechanisms in response to gliadin silencing, we carried out a transcriptomic analysis of grain and leaf tissues of the E82 line during grain filling. A network of candidate transcription factors (TFs) that regulates the synthesis of the seed storage proteins (SSPs), α-amylase/trypsin inhibitors, lipid transfer proteins, serpins, and starch in the grain was obtained. Moreover, there were a high number of differentially expressed genes in the leaf of E82, where processes such as nutrient availability and transport were enriched. The source-sink communication between leaf and grain showed that many down-regulated genes were related to protease activity, amino acid and sugar metabolism, and their transport. In the leaf, specific proline transporters and lysine-histidine transporters were down- and up-regulated, respectively. Overall, the silencing of gliadins in the RNAi line is compensated mainly with lysine-rich globulins, which are not related to the proposed candidate network of TFs, suggesting that these proteins are regulated independently of the other SSPs. Results reported here can explain the protein compensation mechanisms and contribute to decipher the complex TF network operating during grain filling.

Transcription factor retention through multiple polyploidization steps in wheat

Whole-genome duplication is widespread in plant evolutionary history and is followed by nonrandom gene loss to return to a diploid state. Across multiple angiosperm species, the retained genes tend to be dosage-sensitive regulatory genes such as transcription factors, yet data for younger polyploid species is sparse. Here, we analyzed the retention, expression, and genetic variation in transcription factors in the recent allohexaploid bread wheat (Triticum aestivum L.). By comparing diploid, tetraploid, and hexaploid wheat, we found that, following each of two hybridization and whole-genome duplication events, the proportion of transcription factors in the genome increased. Transcription factors were preferentially retained over other genes as homoeologous groups in tetraploid and hexaploid wheat. Across cultivars, transcription factor homoeologs contained fewer deleterious missense mutations than nontranscription factors, suggesting that transcription factors are maintained as three functional homoeologs in hexaploid wheat populations. Transcription factor homoeologs were more strongly coexpressed than nontranscription factors, indicating conservation of function between homoeologs. We found that the B3, MADS-M-type, and NAC transcription factor families were less likely to have three homoeologs present than other families, which was associated with low expression levels and high levels of tandem duplication. Together, our results show that transcription factors are preferentially retained in polyploid wheat genomes although there is variation between families. Knocking out one transcription factor homoeolog to alter gene dosage, using TILLING or CRISPR, could generate new phenotypes for wheat breeding.

Transcriptomic and Physiological Response of Durum Wheat Grain to Short-Term Heat Stress during Early Grain Filling

In a changing climate, extreme weather events such as heatwaves will be more frequent and could affect grain weight and the quality of crops such as wheat, one of the most significant crops in terms of global food security. In this work, we characterized the response of Triticum turgidum L. spp. durum wheat to short-term heat stress (HS) treatment at transcriptomic and physiological levels during early grain filling in glasshouse experiments. We found a significant reduction in grain weight (23.9%) and grain dimensions from HS treatment. Grain quality was also affected, showing a decrease in starch content (20.8%), in addition to increments in grain protein levels (14.6%), with respect to the control condition. Moreover, RNA-seq analysis of durum wheat grains allowed us to identify 1590 differentially expressed genes related to photosynthesis, response to heat, and carbohydrate metabolic process. A gene regulatory network analysis of HS-responsive genes uncovered novel transcription factors (TFs) controlling the expression of genes involved in abiotic stress response and grain quality, such as a member of the DOF family predicted to regulate glycogen and starch biosynthetic processes in response to HS in grains. In summary, our results provide new insights into the extensive transcriptome reprogramming that occurs during short-term HS in durum wheat grains.

A comprehensive transcription factor and DNA-binding motif resource for the construction of gene regulatory networks in Botrytis cinerea and Trichoderma atroviride

Botrytis cinerea and Trichoderma atroviride are two relevant fungi in agricultural systems. To gain insights into these organisms' transcriptional gene regulatory networks (GRNs), we generated a manually curated transcription factor (TF) dataset for each of them, followed by a GRN inference utilizing available sequence motifs describing DNA-binding specificity and global gene expression data. As a proof of concept of the usefulness of this resource to pinpoint key transcriptional regulators, we employed publicly available transcriptomics data and a newly generated dual RNA-seq dataset to build context-specific Botrytis and Trichoderma GRNs under two different biological paradigms: exposure to continuous light and Botrytis-Trichoderma confrontation assays. Network analysis of fungal responses to constant light revealed striking differences in the transcriptional landscape of both fungi. On the other hand, we found that the confrontation of both microorganisms elicited a distinct set of differentially expressed genes with changes in T. atroviride exceeding those in B. cinerea. Using our regulatory network data, we were able to determine, in both fungi, central TFs involved in this interaction response, including TFs controlling a large set of extracellular peptidases in the biocontrol agent T. atroviride. In summary, our work provides a comprehensive catalog of transcription factors and regulatory interactions for both organisms. This catalog can now serve as a basis for generating novel hypotheses on transcriptional regulatory circuits in different experimental contexts.

Delaying or delivering: identification of novel NAM-1 alleles that delay senescence to extend wheat grain fill duration

Senescence is a complex trait under genetic and environmental control, in which resources are remobilized from vegetative tissue into grain. Delayed senescence, or 'staygreen' traits, can confer stress tolerance, with extended photosynthetic activity hypothetically sustaining grain filling. The genetics of senescence regulation are largely unknown, with senescence variation often correlated with phenological traits. Here, we confirm staygreen phenotypes of two Triticum aestivum cv. Paragon ethyl methane sulfonate mutants previously identified during a forward genetic screen and selected for their agronomic performance, similar phenology, and differential senescence phenotypes. Grain filling experiments confirmed a positive relationship between onset of senescence and grain fill duration, reporting an associated ~14% increase in final dry grain weight for one mutant (P<0.05). Recombinant inbred line (RIL) populations segregating for the timing of senescence were developed for trait mapping purposes and phenotyped over multiple years under field conditions. Quantification and comparison of senescence metrics aided RIL selection, facilitating exome capture-enabled bulk segregant analysis (BSA). Using BSA we mapped our two staygreen traits to two independent, dominant, loci of 4.8 and 16.7 Mb in size encompassing 56 and 142 genes, respectively. Combining association analysis with variant effect prediction, we identified single nucleotide polymorphisms encoding self-validating mutations located in NAM-1 homoeologues, which we propose as gene candidates.

From complex data to biological insight: 'DEKER' feature selection and network inference

Network inference is a valuable approach for gaining mechanistic insight from high-dimensional biological data. Existing methods for network inference focus on ranking all possible relations (edges) among all measured quantities such as genes, proteins, metabolites (features) observed, which yields a dense network that is challenging to interpret. Identifying a sparse, interpretable network using these methods thus requires an error-prone thresholding step which compromises their performance. In this article we propose a new method, DEKER-NET, that addresses this limitation by directly identifying a sparse, interpretable network without thresholding, improving real-world performance. DEKER-NET uses a novel machine learning method for feature selection in an iterative framework for network inference. DEKER-NET is extremely flexible, handling linear and nonlinear relations while making no assumptions about the underlying distribution of data, and is suitable for categorical or continuous variables. We test our method on the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge data, demonstrating that it can directly identify sparse, interpretable networks without thresholding while maintaining performance comparable to the hypothetical best-case thresholded network of other methods.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Back to the future: Implications of genetic complexity for the structure of hybrid breeding programs

Commercial hybrid breeding operations can be described as decentralized networks of smaller, more or less isolated breeding programs. There is further a tendency for the disproportionate use of successful inbred lines for generating the next generation of recombinants, which has led to a series of significant bottlenecks, particularly in the history of the North American and European maize germplasm. Both the decentralization and the disproportionate contribution of inbred lines reduce effective population size and constrain the accessible genetic space. Under these conditions, long-term response to selection is not expected to be optimal under the classical infinitesimal model of quantitative genetics. In this study, we therefore aim to propose a rationale for the success of large breeding operations in the context of genetic complexity arising from the structure and properties of interactive genetic networks. For this, we use simulations based on the NK model of genetic architecture. We indeed found that constraining genetic space through program decentralization and disproportionate contribution of parental inbred lines, is required to expose additive genetic variation and thus facilitate heritable genetic gains under high levels of genetic complexity. These results introduce new insights into why the historically grown structure of hybrid breeding programs was successful in improving the yield potential of hybrid crops over the last century. We also hope that a renewed appreciation for “why things worked” in the past can guide the adoption of novel technologies and the design of future breeding strategies for navigating biological complexity.

Annotation and Molecular Characterisation of the TaIRO3 and TaHRZ Iron Homeostasis Genes in Bread Wheat (Triticum aestivum L.)

Effective maintenance of plant iron (Fe) homoeostasis relies on a network of transcription factors (TFs) that respond to environmental conditions and regulate Fe uptake, translocation, and storage. The iron-related transcription factor 3 (IRO3), as well as haemerythrin motif-containing really interesting new gene (RING) protein and zinc finger protein (HRZ), are major regulators of Fe homeostasis in diploid species like Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa L.), but remain uncharacterised in hexaploid bread wheat (Triticum aestivum L.). In this study, we have identified, annotated, and characterised three TaIRO3 homoeologs and six TaHRZ1 and TaHRZ2 homoeologs in the bread wheat genome. Protein analysis revealed that TaIRO3 and TaHRZ proteins contain functionally conserved domains for DNA-binding, dimerisation, Fe binding, or polyubiquitination, and phylogenetic analysis revealed clustering of TaIRO3 and TaHRZ proteins with other monocot IRO3 and HRZ proteins, respectively. Quantitative reverse-transcription PCR analysis revealed that all TaIRO3 and TaHRZ homoeologs have unique tissue expression profiles and are upregulated in shoot tissues in response to Fe deficiency. After 24 h of Fe deficiency, the expression of TaHRZ homoeologs was upregulated, while the expression of TaIRO3 homoeologs was unchanged, suggesting that TaHRZ functions upstream of TaIRO3 in the wheat Fe homeostasis TF network.

A Collinearity-Incorporating Homology Inference Strategy for Connecting Emerging Assemblies in the Triticeae Tribe as a Pilot Practice in the Plant Pangenomic Era

Plant genome sequencing has dramatically increased, and some species even have multiple high-quality reference versions. Demands for clade-specific homology inference and analysis have increased in the pangenomic era. Here we present a novel method, GeneTribe (https://chenym1.github.io/genetribe/), for homology inference among genetically similar genomes that incorporates gene collinearity and shows better performance than traditional sequence-similarity-based methods in terms of accuracy and scalability. The Triticeae tribe is a typical allopolyploid-rich clade with complex species relationships that includes many important crops, such as wheat, barley, and rye. We built Triticeae-GeneTribe (http://wheat.cau.edu.cn/TGT/), a homology database, by integrating 12 Triticeae genomes and 3 outgroup model genomes and implemented versatile analysis and visualization functions. With macrocollinearity analysis, we were able to construct a refined model illustrating the structural rearrangements of the 4A-5A-7B chromosomes in wheat as two major translocation events. With collinearity analysis at both the macro- and microscale, we illustrated the complex evolutionary history of homologs of the wheat vernalization gene Vrn2, which evolved as a combined result of genome translocation, duplication, and polyploidization and gene loss events. Our work provides a useful practice for connecting emerging genome assemblies, with awareness of the extensive polyploidy in plants, and will help researchers efficiently exploit genome sequence resources.

Strategies to improve wheat for human health

Despite their economic importance and growing demand, concerns are emerging around wheat-based foods and human health. Most wheat-based foods are made from refined white flour rather than wholemeal flour, and the overconsumption of these products may contribute to the increasing global prevalence of chronic diseases, particularly type 2 diabetes and obesity. Here, we review how the amount, composition and interactions of starch and cell wall polysaccharides, the major carbohydrate components in refined wheat products, impact human health. We discuss strategies and challenges to manipulate these components for improved diet and health using newly developed wheat genomics tools and resources. Commercial foods developed from these novel approaches must be produced without adverse effects on cost, consumer acceptability and processing properties.