Proteome and transcriptome analyses of wheat near isogenic lines identifies key proteins and genes of wheat bread quality

Protein biomarkers for root length and root dry mass on chromosomes 4A and 7A in wheat

Improving the wheat (Triticum aestivum L.) root system is important for enhancing grain yield and climate resilience. Total root length (RL) and root dry mass (RM) significantly contribute to water and nutrient acquisition directly impacting grain yield and stress tolerance. This study used label-free quantitative proteomics to identify proteins associated with RL and RM in wheat near-isogenic lines (NILs). NIL pair 6 had 113 and NIL pair 9 had 30 differentially abundant proteins (DAPs). Three of identified DAPs located within the targeted genomic regions (GRs) of NIL pairs 6 (qDT.4A.1) and 9 (QHtscc.ksu-7A), showed consistent gene expressions at the protein and mRNA transcription (qRT-PCR) levels for asparagine synthetase (TraesCS4A02G109900), signal recognition particle 19 kDa protein (TraesCS7A02G333600) and 3,4-dihydroxy-2-butanone 4-phosphate synthase (TraesCS7A02G415600). This study discovered, for the first time, the involvement of these proteins as candidate biomarkers for increased RL and RM in wheat. However, further functional validation is required to ascertain their practical applicability in wheat root breeding. SIGNIFICANCE OF THE STUDY: Climate change has impacted global demand for wheat (Triticum aestivum L.). Root traits such as total root length (RL) and root dry mass (RM) are crucial for water and nutrient uptake and tolerance to abiotic stresses such as drought, salinity, and nutrient imbalance in wheat. Improving RL and RM could significantly enhance wheat grain yield and climate resilience. However, breeding for these traits has been limited by lack of appropriate root phenotyping methods, advanced genotypes, and the complex nature of the wheat genome. In this study, we used a semi-hydroponic root phenotyping system to collect accurate root data, near-isogenic lines (NILs; isolines with similar genetic backgrounds but contrasting target genomic regions (GRs)) and label-free quantitative proteomics to explore the molecular mechanisms underlying high RL and RM in wheat. We identified differentially abundant proteins (DAPs) and their molecular pathways in NIL pairs 6 (GR: qDT.4A.1) and 9 (GR: QHtscc.ksu-7A), providing a foundation for further molecular investigations. Furthermore, we identified three DAPs within the target GRs of the NIL pairs with differential expression at the transcript level, as confirmed by qRT-PCR analysis which could serve as candidate protein biomarkers for RL and RM improvement.

Transcriptome and Proteome Analysis Revealed the Influence of High-Molecular-Weight Glutenin Subunits (HMW-GSs) Deficiency on Expression of Storage Substances and the Potential Regulatory Mechanism of HMW-GSs

The processing quality of wheat is affected by seed storage substances, such as protein and starch. High-molecular-weight glutenin subunits (HMW-GSs) are the major components of wheat seed storage proteins (SSPs); they are also key determinators of wheat end-use quality. However, the effects of HMW-GSs absence on the expression of other storage substances and the regulation mechanism of HMW-GSs are still limited. Previously, a wheat transgenic line LH-11 with complete deletions of HMW-GSs was obtained through introducing an exogenous gene Glu-1E^bx to the wild-type cultivar Bobwhite by transgenic approach. In this study, comparative seed transcriptomics and proteomics of transgenic and non-transgenic lines at different seed developmental stages were carried out to explore the changes in genes and proteins and the underlying regulatory mechanism. Results revealed that a number of genes, including genes related to SSPs, carbohydrates metabolism, amino acids metabolism, transcription, translation, and protein process were differentially enriched. Seed storage proteins displayed differential expression patterns between the transgenic and non-transgenic line, a major rise in the expression levels of gliadins were observed at 21 and 28 days post anthesis (DPA) in the transgenic line. Changes in expressions of low-molecular-weight glutenins (LMW-GSs), avenin-like proteins (ALPs), lipid transfer proteins (LTPs), and protease inhibitors (PIs) were also observed. In addition, genes related to carbohydrate metabolism were differentially expressed, which probably leads to a difference in starch component and deposition. A list of gene categories participating in the accumulation of SSPs was proposed according to the transcriptome and proteome data. Six genes from the MYB and eight genes from the NAC transcription families are likely important regulators of HMW-GSs accumulation. This study will provide data support for understanding the regulatory network of wheat storage substances. The screened candidate genes can lay a foundation for further research on the regulation mechanism of HMW-GSs.

Consensus genomic regions associated with grain protein content in hexaploid and tetraploid wheat

A meta-analysis of QTLs associated with grain protein content (GPC) was conducted in hexaploid and tetraploid wheat to identify robust and stable meta-QTLs (MQTLs). For this purpose, as many as 459 GPC-related QTLs retrieved from 48 linkage-based QTL mapping studies were projected onto the newly developed wheat consensus map. The analysis resulted in the prediction of 57 MQTLs and 7 QTL hotspots located on all wheat chromosomes (except chromosomes 1D and 4D) and the average confidence interval reduced 2.71-fold in the MQTLs and QTL hotspots compared to the initial QTLs. The physical regions occupied by the MQTLs ranged from 140 bp to 224.02 Mb with an average of 15.2 Mb, whereas the physical regions occupied by QTL hotspots ranged from 1.81 Mb to 36.03 Mb with a mean of 8.82 Mb. Nineteen MQTLs and two QTL hotspots were also found to be co-localized with 45 significant SNPs identified in 16 previously published genome-wide association studies in wheat. Candidate gene (CG) investigation within some selected MQTLs led to the identification of 705 gene models which also included 96 high-confidence CGs showing significant expressions in different grain-related tissues and having probable roles in GPC regulation. These significantly expressed CGs mainly involved the genes/gene families encoding for the following proteins: aminotransferases, early nodulin 93, glutamine synthetases, invertase/pectin methylesterase inhibitors, protein BIG GRAIN 1-like, cytochrome P450, glycosyl transferases, hexokinases, small GTPases, UDP-glucuronosyl/UDP-glucosyltransferases, and EamA, SANT/Myb, GNAT, thioredoxin, phytocyanin, and homeobox domains containing proteins. Further, eight genes including GPC-B1, Glu-B1-1b, Glu-1By9, TaBiP1, GSr, TaNAC019-A, TaNAC019-D, and bZIP-TF SPA already known to be associated with GPC were also detected within some of the MQTL regions confirming the efficacy of MQTLs predicted during the current study.