Euryale Small Auxin Up RNA62 promotes cell elongation and seed size by altering the distribution of indole-3-acetic acid under the light

Discovering novel genomic regions explaining adaptation of bread wheat to conservation agriculture through GWAS

To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes.

Natural variants of molybdate transporters contribute to yield traits of soybean by affecting auxin synthesis

Soybean (Glycine max) is a crop with high demand for molybdenum (Mo) and typically requires Mo fertilization to achieve maximum yield potential. However, the genetic basis underlying the natural variation of Mo concentration in soybean and its impact on soybean agronomic performance is still poorly understood. Here, we performed a genome-wide association study (GWAS) to identify GmMOT1.1 and GmMOT1.2 that drive the natural variation of soybean Mo concentration and confer agronomic traits by affecting auxin synthesis. The soybean population exhibits five haplotypes of the two genes, with the haplotype 5 demonstrating the highest expression of GmMOT1.1 and GmMOT1.2, as well as the highest transport activities of their proteins. Further studies showed that GmMOT1.1 and GmMOT1.2 improve soybean yield, especially when cultivated in acidic or slightly acidic soil. Surprisingly, these two genes contribute to soybean growth by enhancing the activity of indole-3-acetaldehyde (IAAld) aldehyde oxidase (AO), leading to increased indole-3-acetic acid (IAA) synthesis, rather than being involved in symbiotic nitrogen fixation or nitrogen assimilation. Furthermore, the geographical distribution of five haplotypes in China and their correlation with soil pH suggest the potential significance of GmMOT1.1 and GmMOT1.2 in soybean breeding strategies.

Comparative Anatomical and Transcriptomics Reveal the Larger Cell Size as a Major Contributor to Larger Fruit Size in Apricot

Fruit size is one of the essential quality traits and influences the economic value of apricots. To explore the underlying mechanisms of the formation of differences in fruit size in apricots, we performed a comparative analysis of anatomical and transcriptomics dynamics during fruit growth and development in two apricot cultivars with contrasting fruit sizes (large-fruit Prunus armeniaca 'Sungold' and small-fruit P. sibirica 'F43'). Our analysis identified that the difference in fruit size was mainly caused by the difference in cell size between the two apricot cultivars. Compared with 'F43', the transcriptional programs exhibited significant differences in 'Sungold', mainly in the cell expansion period. After analysis, key differentially expressed genes (DEGs) most likely to influence cell size were screened out, including genes involved in auxin signal transduction and cell wall loosening mechanisms. Furthermore, weighted gene co-expression network analysis (WGCNA) revealed that PRE6/bHLH was identified as a hub gene, which interacted with 1 TIR1, 3 AUX/IAAs, 4 SAURs, 3 EXPs, and 1 CEL. Hence, a total of 13 key candidate genes were identified as positive regulators of fruit size in apricots. The results provide new insights into the molecular basis of fruit size control and lay a foundation for future breeding and cultivation of larger fruits in apricot.