Background Whether keratoconus (KC) is an inflammatory disease is currently debated. Hence, we aimed to investigate the immune-related features of KC based on single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data. Methods scRNA-seq data were obtained from the Genome Sequence Archive (GSA), bulk RNA-seq data were obtained from the Gene Expression Omnibus (GEO), and immune-associated genes(IAGs) were obtained from the ImmPort database. Cell clusters of KC were annotated, and different cell clusters were then selected. The IAG score of each cell was calculated using the AUCell package. Three bulk RNA-seq datasets were merged and used to identify the differentially expressed genes (DEGs), biological functions, and immune characteristics. Weighted gene coexpression network analysis (WGCNA) was used to select the IAG score-related hub genes. Based on scRNA-seq and bulk RNA-seq analyses, three machine learning algorithms, including random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) regression analysis, were used to identify potential prognostic markers for KC. A predictive nomogram was developed based on prognostic markers. Results Six cell clusters were identified in KC, and decreased corneal stromal cell-5 (CSC-5) and increased CSC-6 were found in KC. CSC and immune cell clusters had the highest IAG scores. The bulk RNA-seq analysis identified 1362 DEGs (553 upregulated and 809 downregulated) in KC. We found different immune cell populations and differentially expressed cytokines in KC. More than three key IAG score-related modules and 367 genes were identified. By integrating the scRNA-seq and bulk RNA-seq analyses, 250 IAGs were selected and then incorporated into three machine learning models, and 10 IAGs (CEP112, FYN, IFITM1, IGFBP5, LPIN2, MAP1B, RNASE1, RUNX3, SMIM10, and SRGN) were identified as potential prognostic genes that were significantly associated with cytokine and matrix metalloproteinase(MMP)1-14 expression. Finally, a predictive nomogram was constructed and validated. Conclusion Taken together, our results identified CSCs and immune cell clusters that may play a key role during KC progression by regulating immunological features and maintaining cell stability.
Drought is the main abiotic stress factor limiting the growth and yield of wheat (Triticum aestivum L.). Therefore, improving wheat tolerance to drought stress is essential for maintaining yield. Previous studies have reported on the important role of TaNRX1 in conferring drought stress tolerance. Therefore, to elucidate the regulation mechanism by which TaNRX1 confers drought resistance in wheat, we generated TaNRX1 overexpression (OE) and RNA interference (RNAi) wheat lines. The results showed that the tolerance of the OE lines to drought stress were significantly enhanced. The survival rate, leaf chlorophyll, proline, soluble sugar content, and activities of the antioxidant enzymes (catalase, superoxide dismutase, and peroxidase) of the OE lines were higher than those of the wild type (WT); however, the relative electrical conductivity and malondialdehyde, hydrogen peroxide, and superoxide anion levels of the OE lines were lower than those of the WT; the RNAi lines showed the opposite results. RNA-seq results showed that the common differentially expressed genes of TaNRX1 OE and RNAi lines, before and after drought stress, were mainly distributed in the plant-pathogen interaction, plant hormone signal transduction, phenylpropane biosynthesis, starch and sucrose metabolism, and carbon metabolism pathways and were related to the transcription factors, including WRKY, MYB, and bHLH families. This study suggests that TaNRX1 positively regulates drought stress tolerance in wheat.
Thioredoxins (TRXs) are small-molecule proteins with redox activity that play very important roles in the growth, development, and stress resistance of plants. Foxtail millet (Setaria italica) gradually became a model crop for stress resistance research because of its advantages such as its resistance to sterility and its small genome. To date, the thioredoxin (TRX) family has been identified in Arabidopsis thaliana, rice and wheat. However, studies of the TRX family in foxtail millet have not been reported, and the biological function of this family remains unclear. In this study, 35 SiTRX genes were identified in the whole genome of foxtail millet through bioinformatic analysis. According to phylogenetic analysis, 35 SiTRXs can be divided into 13 types. The chromosome distribution, gene structure, cis-elements and conserved protein motifs of 35 SiTRXs were characterized. Three nucleoredoxin (NRX) members were further identified by a structural analysis of TRX family members. The expression patterns of foxtail millet's SiNRX members under abiotic stresses showed that they have different stress-response patterns. In addition, subcellular localization revealed that SiNRXs were localized to the nucleus, cytoplasm and membrane. Further studies demonstrated that the overexpression of SiNRX1 enhanced Arabidopsis' tolerance to drought and salt stresses, resulting in a higher survival rate and better growth performance. Moreover, the expression levels of several known stress-related genes were generally higher in overexpressed lines than in the wild-type. Thus, this study provides a general picture of the TRX family in foxtail millet and lay a foundation for further research on the mechanism of the action of TRX proteins on abiotic stresses.
Low-molecular-weight glutenin subunits (LMW-GS) account for 40% of the total wheat grain gluten protein fraction, which plays a significant role in the formation of noodle processing quality. The goal of this study was to clarify the effects of the major LMW-GS encoded by Glu-A3 on gluten and Chinese fresh noodle (CFN) quality. Four near-isogenic lines (NILs) were used as materials in this study, respectively carrying alleles Glu-A3a, Glu-A3b, Glu-A3c, and Glu-A3e, against the background of wheat variety Xiaoyan 22. The grain protein and its component contents and the gluten content, gluten index, farinograph properties, cooking quality, and textural quality of CFN were investigated. The results show that the ratios of glutenin to gliadin (Glu/Gli) in the NILs ranked them as Glu-A3b > Glu-A3c/Glu-A3a > Glu-A3e, and the unextractable polymeric protein content (UPP%), gluten index (GI), and farinograph quality in the NILs ranked them as Glu-A3b > Glu-A3c > Glu-A3a/Glu-A3e. Compared to Glu-A3b and Glu-A3a, the NILs carrying alleles Glu-A3c and Glu-A3e had better cooking and texture properties in CFN. All these findings suggest that the introduction of alleles Glu-A3c or Glu-A3e is an efficient method for quality improvement in CFN, which provides an excellent subunit selection for improving CFN quality.
Abstract The aim of this study was to clarify the effects of the high‐molecular‐weight glutenin subunits (HMW‐GSs) 1Dx3+1Dy12 (3+12) and 1Dx4+1Dy12 (4+12) at the Glu‐D1 locus on gluten and Chinese steamed bread (CSB) quality. The grain protein content and composition, gluten content and gluten index, farinograph properties, and CSB quality were investigated using four wheat near‐isogenic lines (NILs) carrying HMW‐GSs 1Dx2+1Dy12 (2+12), 3+12, 4+12 and 1Dx5+1Dy10 (5+10), respectively. The unextractable polymeric protein (UPP) and glutenin macropolymer (GMP) content, gluten index, dough development time, stability time, and farinograph quality number of four NILs all ranked as 5+10 > 3+12 > 2+12/4+12, such as the gluten index ranked as 5+10(44.88%) > 3+12(40.07%) > 2+12(37.46%)/4+12(35.85%); however, their contributions to the quality of CSB were ranked as 3+12 > 5+10 > 2+12/4+12, such as the specific volume ranked as 3+12(2.64 mL/g) > 5+10(2.49 mL/g) > 2+12(2.36 mL/g)/4+12(2.35 mL/g), which indicated that a suitable gluten strength (3+12) was crucial to making high‐quality CSB. In addition, subunits 4+12 had a similar quality performance to low‐quality subunits 2+12. All these findings suggested that, except for the acknowledged high‐quality subunits 5+10, the introduction of 3+12 at the Glu‐D1 locus is an efficient way for quality improvement of gluten as well as CSB.
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