Key messageResults from various expansin related studies have demonstrated that expansins present an opportunity to improve various crops in many different aspects ranging from yield and fruit ripening to improved stress tolerance.AbstractThe recent advances in expansin studies were reviewed. Besides producing the strength that is needed by the plants, cell walls define cell shape, cell size and cell function. Expansins are cell wall proteins which consist of four sub families; α-expansin, β-expansin, expansin-like A and expansin-like B. These proteins mediate cell wall loosening and they are present in all plants and in some microbial organisms and other organisms like snails. Decades after their initial discovery in cucumber, it is now clear that these small proteins have diverse biological roles in plants. Through their ability to enable the local sliding of wall polymers by reducing adhesion between adjacent wall polysaccharides and the part they play in cell wall remodeling after cytokinesis, it is now clear that expansins are required in almost all plant physiological development aspects from germination to fruiting. This is shown by the various reports from different studies using various molecular biology approaches such as gene achieve these many roles through their non-enzymatic wall loosening ability. This paper reviews and summarizes some of the reported functions of expansins and outlines the potential uses of expansins in crop improvement programs.

A novel high-density consensus wheat genetic map was obtained based on three related RIL populations, and the important chromosomal regions affecting yield and related traits were specified. A prerequisite for mapping quantitative trait locus (QTL) is to build a genetic linkage map. In this study, three recombinant inbred line populations (represented by WL, WY, and WJ) sharing one common parental line were used for map construction and subsequently for QTL detection of yield-related traits. PCR-based and diversity arrays technology markers were screened in the three populations. The integrated genetic map contains 1,127 marker loci, which span 2,976.75 cM for the whole genome, 985.93 cM for the A genome, 922.16 cM for the B genome, and 1,068.65 cM for the D genome. Phenotypic values were evaluated in four environments for populations WY and WJ, but three environments for population WL. Individual and combined phenotypic values across environments were used for QTL detection. A total of 165 putative additive QTL were identified, 22 of which showed significant additive-by-environment interaction effects. A total of 65 QTL (51.5%) were stable across environments, and 23 of these (35.4%) were common stable QTL that were identified in at least two populations. Notably, QTkw-5B.1, QTkw-6A.2, and QTkw-7B.1 were common major stable QTL in at least two populations, exhibiting 11.28-16.06, 5.64-18.69, and 6.76-21.16% of the phenotypic variance, respectively. Genetic relationships between kernel dimensions and kernel weight and between yield components and yield were evaluated. Moreover, QTL or regions that commonly interact across genetic backgrounds were discussed by comparing the results of the present study with those of previous similar studies. The present study provides useful information for marker-assisted selection in breeding wheat varieties with high yield.

Plant height (PH) in wheat is a complex trait; its components include spike length (SL) and internode lengths. To precisely analyze the factors affecting PH, two F(8:9) recombinant inbred line (RIL) populations comprising 485 and 229 lines were generated. Crosses were performed between Weimai 8 and Jimai 20 (WJ) and between Weimai 8 and Yannong 19 (WY). Possible genetic relationships between PH and PH components (PHC) were evaluated at the quantitative trait locus (QTL) level. PH and PHC (including SL and internode lengths from the first to the fourth counted from the top, abbreviated as FIITL, SITL, TITL, and FOITL, respectively) were measured in four environments. Individual and the pooled values from four trials were used in the present analysis. A QTL for PH was mapped using data on PH and on PH conditioned by PHC using IciMapping V2.2. All 21 chromosomes in wheat were shown to harbor factors affecting PH in two populations, by both conditional and unconditional QTL mapping methods. At least 11 pairwise congruent QTL were identified in the two populations. In total, ten unconditional QTL and five conditional QTL that could be detected in the conditional analysis only have been verified in no less than three trials in WJ and WY. In addition, three QTL on the short arms of chromosomes 4B, 4D, and 7B were mapped to positions similar to those of the semi-dwarfing genes Rht-B1, Rht-D1 and Rht13, respectively. Conditional QTL mapping analysis in WJ and WY proved that, at the QTL level, SL contributed the least to PH, followed by FIITL; TITL had the strongest influence on PH, followed by SITL and FOITL. The results above indicated that the conditional QTL mapping method can be used to evaluate possible genetic relationships between PH and PHC, and it can efficiently and precisely reveal counteracting QTL, which will enhance the understanding of the genetic basis of PH in wheat. The combination of two related populations with a large/moderate population size made the results authentic and accurate.

Spike-related traits contribute greatly to grain yield in wheat. To localize wheat chromosomes for factors affecting the seven spike-related traitsi.e., the spike length (SL), the basal sterile spikelet number (BSSN), the top sterile spikelet number (TSSN), the sterile spikelet number in total (SSN), the spikelet number per spike (SPN), the fertile spikelet number (FSN) and the spike density (SD)-two F 8:9 recombinant inbred line (RIL) populations were generated. They were derived from crosses between Weimai 8 and Jimai 20 (WJ) and between Weimai 8 and Yannong 19 (WY), comprising 485 and 229 lines, respectively. Combining the two new linkage maps and the phenotypic data collected from the four environments, we conducted quantitative trait locus (QTL) detection for the seven spike-related traits and evaluated their genetic correlations. Up to 190 putative additive QTL for the seven spike-related traits were detected in WJ and WY, distributing across all the 21 wheat chromosomes. Of these, at least nine pairwise QTL were common to the two populations. In addition, 38 QTL showed significance in at least two of the four different environments, and 18 of these were major stable QTL. Thus, they will be of great value for marker assisted selection (MAS) in breeding programs. Though co-located QTL were universal, every trait owned its unique QTL and even two closely related traits were not excluded. The two related populations with a large/moderate population size F. Cui, A. Ding, J. Li and C. Zhao contributed equally to this work.Electronic supplementary material The online version of this article (made the results authentic and accurate. This study will enhance the understanding of the genetic basis of spike-related traits.

Expansins are pH-dependent cell wall loosening proteins which form a large family in plants. They have been shown to be involved in various developmental processes and been implicated in enabling plants' ability to absorb nutrients from the soil as well as conferring biotic and abiotic stress resistances. It is therefore clear that they can be potential targets in genetic engineering for crop improvement. Tobacco (Nicotiana tabacum) is a major crop species as well as a model organism. Considering that only a few tobacco expansins have been studied, a genome-wide analysis of the tobacco expansin gene family is necessary. In this study, we identified 52 expansins in tobacco, which were classified into four subfamilies: 36 NtEXPAs, 6 NtEXPBs, 3 NtEXLAs and 7 NtEXLBs. Compared to other species, the NtEXLB subfamily size was relatively larger. Phylogenetic analysis showed that the 52 tobacco expansins were divided into 13 subgroups. Gene structure analysis revealed that genes within subfamilies/subgroups exhibited similar characteristics such as gene structure and protein motif arrangement. Whole-genome duplication and tandem duplication events may have played important roles in the expanding of tobacco expansins. Cis-Acting element analysis revealed that each expansin gene was regulated or several expansin genes were co-regulated by both internal and environmental factors. 35 of these genes were identified as being expressed according to a microarray analysis. In contrast to most NtEXPAs which had higher expression levels in young organs, NtEXLAs and NtEXLBs were preferentially expressed in mature or senescent tissues, suggesting that they might play different roles in different organs or at different developmental stages. As the first step towards genome-wide analysis of the tobacco expansin gene family, our work provides solid background information related to structure, evolution and expression as well as regulatory cis-acting elements of the tobacco expansins. This information will provide a strong foundation for cloning and functional exploration of expansin genes in tobacco.

Xyloglucan endotransglucosylase/hydrolase genes (XTHs) encode enzymes required for the reconstruction and modification of xyloglucan backbones, which will result in changes of cell wall extensibility during growth. A total of 56 NtXTH genes were identified from common tobacco, and 50 cDNA fragments were verified by PCR amplification. The 56 NtXTH genes could be classified into two subfamilies: Group I/II and Group III according to their phylogenetic relationships. The gene structure, chromosomal localization, conserved protein domains prediction, sub-cellular localization of NtXTH proteins and evolutionary relationships among Nicotiana tabacum, Nicotiana sylvestrisis, Nicotiana tomentosiformis, Arabidopsis, and rice were also analyzed. The NtXTHs expression profiles analyzed by the TobEA database and qRT-PCR revealed that NtXTHs display different expression patterns in different tissues. Notably, the expression patterns of 12 NtXTHs responding to environment stresses, including salinity, alkali, heat, chilling, and plant hormones, including IAA and brassinolide, were characterized. All the results would be useful for the function study of NtXTHs during different growth cycles and stresses.

Grain protein content (GPC) and gluten quality are the most important factors determining the end-use quality of wheat for pasta-making. Both GPC and gluten quality are considered to be polygenic traits influenced by environmental factors and other agricultural practices. Two related F 8:9 recombinant inbred line (RIL) populations were generated to localise genetic factors controlling seven quality traits: GPC, wet gluten content (WGC), flour whiteness (FW), kernel hardness (KH), water absorption (Abs), dough development time (DDT) and dough stability time (DST). These lines were derived by crossing Weimai 8 and Jimai 20 (WJ) and by crossing Weimai 8 and Yannong 19 (WY). In total, WJ comprised 485 lines, while WY comprised 229 lines. Data on these seven quality traits were collected from each line in five different environments. Up to 85 putative QTLs for the seven traits were detected in WJ and 65 putative QTLs were detected in WY. Of these QTLs, 31 QTLs (36.47%) were detected in at least two trials in WJ, while 24 QTLs (36.92%) were detected in at least two trials in WY. Three QTLs from WJ and 25 from WY accounted for more than 10% of the phenotypic variance. The total 150 QTLs were spread throughout all 21 wheat chromosomes. Of these, at least thirteen pairwise were common to both populations, accounting for 20.00 and 15.29% of the total QTLs in WJ and WY, respectively. A major QTL for GPC, accounting for 53.04% of the phenotypic variation, was detected on chromosome 5A. A major QTL for WGC also shared this interval, explained more than 36% of the phenotypic variation, and was significant in two environments. Though colocated QTLs were common, every trait had its unique control mechanism, even for two closely related traits. Due to the different sizes of the two line populations, we also assessed the effects of population size on the efficiency and precision of QTL detection. In sum, this study will enhance our understanding of the genetic basis of these seven pivotal quality traits and facilitate the breeding of improved wheat varieties.

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