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Background: Long non-coding RNAs (lncRNAs) play important roles in response to abiotic stresses in plants, by acting as cis- or trans-acting regulators of protein-coding genes. As a widely cultivated crop worldwide, maize is sensitive to salt stress particularly at the seedling stage. However, it is unclear how the expressions of protein-coding genes are affected by non-coding RNAs in maize responding to salt tolerance. Results: The whole transcriptome sequencing was employed to investigate the differential lncRNAs and target transcripts responding to salt stress between two maize inbred lines with contrasting salt tolerance. We developed a flexible, user-friendly, and modular RNA analysis workflow, which facilitated the identification of lncRNAs and novel mRNAs from whole transcriptome data. Using the workflow, 12,817 lncRNAs and 8,320 novel mRNAs in maize seedling roots were identified and characterized. A total of 742 lncRNAs and 7,835 mRNAs were identified as salt stress-responsive transcripts. Moreover, we obtained 41 cis- and 81 trans-target mRNA for 88 of the lncRNAs. Among these target transcripts, 11 belonged to 7 transcription factor (TF) families including bHLH, C2H2, Hap3/NF-YB, HAS, MYB, WD40, and WRKY. The above 8,577 salt stress-responsive transcripts were further classified into 28 modules by weighted gene co-expression network analysis. In the salt-tolerant module, we constructed an interaction network containing 79 nodes and 3081 edges, which included 5 lncRNAs, 18 TFs and 56 functional transcripts (FTs). As a trans-acting regulator, the lncRNA MSTRG.8888.1 affected the expressions of some salt tolerance-relative FTs, including protein-serine/threonine phosphatase 2C and galactinol synthase 1, by regulating the expression of the bHLH TF. Conclusions: The contrasting genetic backgrounds of the two inbred lines generated considerable variations in the expression abundance of lncRNAs and protein-coding transcripts. In the co-expression networks responding to salt stress, some TFs were targeted by the lncRNAs, which further regulated the salt tolerance-related functional transcripts. We constructed a regulatory pathway of maize seedlings to salt stress, which was mediated by the hub lncRNA MSTRG.8888.1 and participated by the bHLH TF and its downstream target transcripts. Future work will be focused on the functional revelation of the regulatory pathway. [ABSTRACT FROM AUTHOR]

The free moisture in crop kernels after being naturally dried is referred to as kernel moisture content (KMC). Maize KMC reflects grain quality and influences transportation and storage of seeds. We used an IBM Syn10 DH maize population consisting of 249 lines and an association panel comprising 310 maize inbred lines to identify the genetic loci affecting maize KMC in three environments. Using the IBM population detected 13 QTL on seven chromosomes, which were clustered into nine common QTL. Genome‐wide association analysis (GWAS) identified 16 significant SNPs across the 3 environments, which were linked to 158 genes across the three environments. Combined QTL mapping and GWAS found two SNPs that were located in two of the mapped QTL, respectively. Twenty‐three genes were linked with the loci co‐localized in both populations. Of these 181 genes, five have previously been reported to be associated with KMC or to regulate seed development. These associations were verified by candidate gene association analysis. Two superior alleles and one favorable haplotype for Zm00001d007774 and Zm00001d047868 were found to influence KMC. These findings provide insights into molecular mechanisms underlying maize KMC and contribute to the use of marker‐assisted selection for breeding low‐KMC maize. [ABSTRACT FROM AUTHOR]

Key message: Using GWAS and QTL mapping identified 100 QTL and 138 SNPs, which control yield-related traits in maize. The candidate gene GRMZM2G098557 was further validated to regulate ear row number by using a segregation population. Understanding the genetic basis of yield-related traits contributes to the improvement of grain yield in maize. This study used an inter-mated B73 × Mo17 (IBM) Syn10 doubled-haploid (DH) population and an association panel to identify the genetic loci responsible for nine yield-related traits in maize. Using quantitative trait loci (QTL) mapping, 100 QTL influencing these traits were detected across different environments in the IBM Syn10 DH population, with 25 co-detected in multiple environments. Using a genome-wide association study (GWAS), 138 single-nucleotide polymorphisms (SNPs) were identified as correlated with these traits (P < 2.04E−06) in the association panel. Twenty-one pleiotropic QTL/SNPs were identified to control different traits in both populations. A combination of QTL mapping and GWAS uncovered eight significant SNPs (PZE-101097575, PZE-103169263, ZM011204-0763, PZE-104044017, PZE-104123110, SYN8062, PZE-108060911, and PZE-102043341) that were co-located within seven QTL confidence intervals. According to the eight co-localized SNPs by the two populations, 52 candidate genes were identified, among which the ear row number (ERN)-associated SNP SYN8062 was closely linked to SBP-transcription factor 7 (GRMZM2G098557). Several SBP-transcription factors were previously demonstrated to modulate maize ERN. We then validated the phenotypic effects of SYN8062 in the IBM Syn10 DH population, indicating that the ERN of the lines with the A-allele in SYN8062 was significantly (P < 0.05) larger than that of the lines with the G-allele in SYN8062 in each environment. These findings provide valuable information for understanding the genetic mechanisms of maize grain yield formation and for improving molecular marker-assisted selection for the high-yield breeding of maize. [ABSTRACT FROM AUTHOR]

Ear tip‐barrenness (ETB) phenotype threatens crop yield, because it reduces the kernel number per ear. The genetic basis of ETB in maize remains largely unknown. Herein, a genome‐wide association study (GWAS) and quantitative trait loci (QTL) mapping were jointly applied to identify the significant genetic loci interrelated with ETB. Six significant SNPs were detected at a stringent P‐value threshold (1.95 × 10−6). Additionally, four environment‐stable SNPs were co‐detected across a single environment and best linear unbiased prediction (BLUP) model at a less stringent P‐value threshold (1 × 10−4). The above 10 SNPs were closely linked to 6 candidate genes, which mainly involved seed development, photosynthesis and ethylene response. Moreover, the ratio of superior allele at each significant SNP ranged from 0 to 83.33% in 30 investigated maize elite lines. QTL mapping identified 14 QTL with phenotypic variation explained (PVE) ranging from 3.64 to 7.09%, of which one QTL (qETB2‐1) was repeatedly identified in two environments. Combined analysis of GWAS and QTL mapping showed that one SNP (PZE‐102175229, chromosome 2: 217 66 Mb) was located in the QTL (qETB2‐2, chromosome 2: 215 90–217 82 Mb). Eighteen gene models situated in the linkage disequilibrium (LD) region of the co‐localized SNP were further used to evaluate their correlation with ETB by candidate gene association analysis. Two superior haplotypes and two superior alleles were detected among 74 lines for Zm00001d007195, Zm00001d007197 and Zm00001d007201. These results provide more information for clarifying the molecular mechanism of ETB and for speeding up the genetic improvement of maize varieties. [ABSTRACT FROM AUTHOR]

Summary: Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10−6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10−3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1,CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize. [ABSTRACT FROM AUTHOR]

Maize tassel architecture is a complex quantitative trait that is significantly correlated with biomass yield and grain yield. The present study evaluated the major trait of maize tassel architecture, namely, tassel branch number (TBN), in an association population of 359 inbred lines and an IBM Syn 10 population of 273 doubled haploid lines across three environments. Approximately 43,958 high-quality single nucleotide polymorphisms were utilized to detect significant QTNs associated with TBN based on new multi-locus genome-wide association study methods. There were 30, 38, 73, 40, 47, and 53 QTNs associated with tassel architecture that were detected using the FastmrEMMA, FastmrMLM, EM-BLASSO, mrMLM, pkWMEB, and pLARmEB models, respectively. Among these QTNs, 51 were co-identified by at least two of these methods. In addition, 12 QTNs were consistently detected across multiple environments. Furthermore, 19 QTLs distributed on chromosomes 1, 2, 3, 4, 6, and 7 were detected in 3 environments and the BLUP model based on 6618 bin markers, which explained 3.64–10.96% of the observed phenotypic variations in TBN. Of these, three QTLs were co-detected in two environments. One QTN associated with TBN was localized to one QTL. Approximately 55 candidate genes were detected by common QTNs and LD criteria. One candidate gene, Zm00001d016615, was identified as a putative target of the RA1 gene. Meanwhile, RA1 was previously validated to plays an important role in tassel development. In addition, the newly identified candidate genes Zm00001d003939, Zm00001d030212, Zm00001d011189, and Zm00001d042794 have been reported to involve in a spikelet meristem identity module. The findings of the present study improve our understanding of the genetic basis of tassel architecture in maize. [ABSTRACT FROM AUTHOR]

Stalk lodging severely limits the grain yield of maize (Zea mays L.). Mechanical stalk strength can be reflected by the traits of stalk diameter (SD), stalk bending strength (SBS), and lodging rind penetrometer resistance (RPR). To determine the genetic basis of maize stalk lodging, quantitative trait loci (QTLs) were mapped for these three traits using the IBM Syn10 DH population in three environments. The results indicated that there were strong genetic correlations among the three traits, and the analyses of phenotypic variations for SD, SBS, and RPR across the three environments showed high broad-sense heritability (0.6843, 0.5175, and 0.7379, respectively). In total, 44 significant QTLs were identified control the above traits across the 3 environments. A total of 14, 14, and 16 QTLs were identified for SD, SBS, and RPR across single-environment mapping, respectively. Notably, ten QTLs were stably expressed across multiple-environments, including two QTLs for SD, three for SBS, and five for RPR. Three major QTLs each accounting for over 10% of the phenotypic variation were qSD6-2 (10.03%), qSD8-2 (13.73%), and qSBS1-2 (11.89%). Comprehensive analysis of all QTLs in this study revealed that 5 QTL clusters including 12 QTLs were located on chromosomes 1, 3, 7, and 8, respectively. Among these 44 QTLs, 9 harbored 13 stalk lodging-associated SNPs that were detected by our recently published work, with 1 SNP successfully validated in the IBM Syn10 DH population. These chromosomal regions will be useful for marker-assisted selection and fine mapping of stalk lodging-related traits in maize. [ABSTRACT FROM AUTHOR]

Seed germination directly affect maize yield and grain quality. Low-temperature reduces maize yield by affecting seed germination and seedling growth. However, the molecular mechanism of maize seed germination under low-temperature remains unclear. In this study, the transcriptome data of two maize inbred lines SCL127 (chilling-sensitive) and SCL326 (chilling-tolerant) were analyzed at five time points (0 H, 4 H, 12 H, 24 H, and 48 H) under low-temperature conditions. Through the comparison of SCL127-0 H-vs-SCL326-0 H (Group I), SCL127-4 H-vs-SCL326-4 H (Group Ⅱ), SCL127-12 H-vs-SCL326-12 H (Group Ⅲ), SCL127-24 H-vs-SCL326-24 H (Group Ⅳ), and SCL127-48 H-vs SCL326-48 H (Group Ⅴ), a total of 8,526 differentially expressed genes (DEGs) were obtained. Weighted correlation network analysis revealed that Zm00001d010445 was the hub gene involved in seed germination under low-temperature conditions. Zm00001d010445 -based association analysis showed that Hap Ⅱ (G) was the excellent haplotype for seed germination under low-temperature conditions. These findings provide a new perspective for the study of the genetic architecture of maize tolerance to low-temperature and contribute to the cultivation of maize varieties with low-temperature tolerance. • RNA-seq was performed on two maize lines under low-temperature. • Zm00001d010445 was identified to affect maize germination. [ABSTRACT FROM AUTHOR]

Background: Maize is one of the primary crops of genetic manipulation, which provides an excellent means of promoting stress resistance and increasing yield. However, the differences in induction and regeneration capacity of embryonic callus (EC) among various genotypes result in genotypic dependence in genetic transformation. Results: In this study, embryonic calli of two maize inbred lines with strong redifferentiation capacity and two lines with weak redifferentiation capability were separately subjected to transcriptome sequencing analysis during the early redifferentiation stages (stage I, 1–3 d; stage II, 4–6 d; stage III, 7–9 d) along with their corresponding controls. A total of ~ 654.72 million cDNA clean reads were yielded, and 62.64%~ 69.21% clean reads were mapped to the reference genome for each library. In comparison with the control, the numbers of differentially expressed genes (DEGs) for the four inbred lines identified in the three stages ranged from 1694 to 7193. By analyzing the common and specific DEGs of the four materials, we found that there were 321 upregulated genes and 386 downregulated genes identified in the high-regeneration lines (141 and DH40), whereas 611 upregulated genes and 500 downregulated genes were specifically expressed in the low-regeneration lines (ZYDH381–1 and DH3732). Analysis of the DEG expression patterns indicated a sharp change at stage I in both the high- and low-regeneration lines, which suggested that stage I constitutes a crucial period for EC regeneration. Notably, the specific common DEGs of 141 and DH40 were mainly associated with photosynthesis, porphyrin and chlorophyll metabolism, ribosomes, and plant hormone signal transduction. In contrast, the DEGs in ZYDH381–1 and DH3732 were mainly related to taurine and hypotaurine metabolism, nitrogen metabolism, fatty acid elongation, starch and sucrose metabolism, phenylpropanoid biosynthesis, and plant circadian rhythm. More importantly, WOX genes, which have an ancestral role in embryo development in seed plants and promote the regeneration of transformed calli, were specifically upregulated in the two high-regeneration lines. Conclusions: Our research contributes to the elucidation of molecular regulation during early redifferentiation in the maize embryonic callus. [ABSTRACT FROM AUTHOR]