Halogeton glomeratus (H. glomeratus) is an extreme halophyte that is widely distributed in arid regions, including foothills, the Gobi desert of northwest China, and the marginal loess of Central Asia. However, research on the salt-tolerant mechanisms and genes of this species are limited because of a lack of genomic sequences. In the present study, the transcriptome of H. glomeratus was analyzed using next-generation sequencing technology to identify genes involved in salt tolerance and better understand mechanisms of salt response in the halophyte H. glomeratus. Illumina RNA-sequencing was performed in five sequencing libraries that were prepared from samples treated with 200 mM NaCl for 6, 12, 24, and 72 h and a control sample to investigate changes in the H. glomeratus transcriptome in response to salt stress. The de novo assembly of five transcriptomes identified 50,267 transcripts. Among these transcripts, 31,496 (62.66%) were annotated, including 44 Gene Ontology (GO) terms and 128 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Compared with transcriptomes from the control and NaCl-treated samples, there were 2,223, 5,643, 7,510 and 10,908 genes that were differentially expressed after exposure to NaCl for 6, 12, 24, and 72 h, respectively. One hundred and eighteen salt-induced genes were common to at least two stages of salt stress, and 291 up-regulated genes were common to various stages of salt stress. Numerous genes that are related to ion transport, reactive oxygen species scavenging, energy metabolism, hormone-response pathways, and responses to biotic and abiotic stress appear to play a significant role in adaptation to salinity conditions in this species. The detection of expression patterns of 18 salt-induced genes by quantitative real-time polymerase chain reaction were basically consistent with their changes in transcript abundance determined by RNA sequencing. Our findings provide a genomic sequence resource for functional genetic assignments of an extreme halophyte, H. glomeratus. We believe that the transcriptome datasets will help elucidate the genetic basis of this species’ response to a salt environment and develop stress-tolerant crops based on favorable wild genetic resources.

Pyrenophora graminea is the causative agent of barley leaf stripe disease. In this study, the strong pathogenic isolate QWC was used to generate DNA for Illumina sequencing. After assembly, its genome size was 42.5 Mb, consisting of 264 scaffolds, and a total of 10,376 genes was predicted. This is the first genome resource available for P. graminea. The genome sequences of P. graminea will accelerate the understanding interaction of P. graminea and barley.

The elongation and development of wheat (Triticum aestivum L.) stem play an important role in plant architecture. The shortened stem would result in a sheathed spike and a low yield in crops. Unraveling the molecular mechanisms underlying a sheathed spike would be beneficial for plant architecture and yield improvement. We identified a novel gene, TaWUS-like (WUSCHEL-related homeobox-like), which regulated sheathed spike and plant architecture in wheat. The plant height of overexpression transgenic lines was significantly decreased and the spike was not completely elongated and enclosed in flag leaf sheaths. Moreover, the increase in tiller angle resulted in loose plant architecture and lower yield. The statistical and cytological analysis demonstrated that the length of the uppermost and secondary internode was significantly shortened, especially the uppermost internode which was only half the length of the wild-type. The size of parenchyma cells was obviously reduced and cell length on the longitudinal section was elongated insufficiently compared with wild-type. The analysis of hormone content showed that there was a lack of gibberellin A 3 (GA₃) in internodes but a higher brassinosteroid (BR) content. TaWUS-like may inhibit the synthesis of GA₃ and/or BR, thus affecting the function of signal transduction of these hormones, which further caused stem shortening and plant dwarfing in wheat.

Cuticular waxes comprise the hydrophobic layer that protects crops against nonstomatal water loss and biotic and abiotic stresses. Expanding on our current knowledge of the genes that are involved in cuticular wax biosynthesis and regulation plays an important role in dissecting the processes of cuticular wax metabolism. In this study, we identified the Cer-GN1 barley (Hordeum vulgare L.) mutant that is generated by ethyl methanesulfonate mutagenesis with a glossy spike phenotype that is controlled by a single recessive nuclear gene. A physiological analysis showed that the total cuticular wax loads of Cer-GN1 were one-third that of the progenitor wild-type (WT), and its water loss rate was significantly accelerated (p < 0.05). In addition, Cer-GN1 was defective in the glume’s cuticle according to the toluidine blue dye test, and it was deficient in the tubule-shaped crystals which were observed on the glume surfaces by scanning electron microscopy. Using metabolomics and transcriptomics, we investigated the impacts of cuticular wax composition and waxy regulatory genes on the loss of the glaucous wax in the spikes of Cer-GN1. Among the differential metabolites, we found that 16-hydroxyhexadecanoic acid, which is one of the predominant C16 and C18 fatty acid-derived cutin monomers, was significantly downregulated in Cer-GN1 when it was compared to that of WT. We identified two novel genes that are located on chromosome 4H and are downregulated in Cer-GN1 (HvMSTRG.29184 and HvMSTRG.29185) that encode long-chain fatty acid omega-monooxygenase CYP704B1, which regulates the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid. A quantitative real-time PCR revealed that the expression levels of HvMSTRG.29184 and HvMSTRG.29185 were downregulated at 1, 4, 8, 12, and 16 days after the heading stage in Cer-GN1 when it was compared to those of WT. These results suggested that HvMSTRG.29184 and HvMSTRG.29185 have CYP704B1 activity, which could regulate the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid in barley. Their downregulation in Cer-GN1 reduced the synthesis of the cuticular wax components and ultimately caused the loss of the glaucous wax in the spikes. It is necessary to verify whether HvMSTRG.29184 and HvMSTRG.29185 truly encode a CYP704B1 that regulates the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid in barley.

Spot blotch, caused by fungal pathogen Bipolaris sorokiniana Shoemaker, is one of the most frequent diseases affecting barley-growing regions worldwide. In this study, we reported the genome sequence of the highly virulent B. sorokiniana strain GN1 using the Illumina HiSeq 4000 platform. In total, 57 million 150-nucleotide paired-end clean reads were obtained and assembled into 96 scaffolds with an estimated genome size of 34.33 Mb. Furthermore, we identified genes that may be associated with strain-specific virulence and performed phylogenetic analysis of GN1 with five other Bipolaris spp. These results for GN1 will provide important information in understanding its molecular underpinning of pathogenicity and help identify novel sources of genetic resistance for improving disease resistance in barley.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.