Natural variation of Dt2 determines branching in soybean.

Shoot branching is fundamentally important in determining soybean yield. Here, through genome-wide association study, we identify one predominant association locus on chromosome 18 that confers soybean branch number in the natural population. Further analyses determine that Dt2 is the corresponding gene and the natural variations in Dt2 result in significant differential transcriptional levels between the two major haplotypes. Functional characterization reveals that Dt2 interacts with GmAgl22 and GmSoc1a to physically bind to the promoters of GmAp1a and GmAp1d and to activate their transcription. Population genetic investigation show that the genetic differentiation of Dt2 display significant geographic structure. Our study provides a predominant gene for soybean branch number and may facilitate the breeding of high-yield soybean varieties.

Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system.

BACKGROUND: Measurement of plant structure is useful in monitoring plant conditions and understanding the responses of plants to environmental changes. 3D imaging technologies, especially the passive-SfM (Structure from Motion) algorithm combined with a multi-camera photography (MCP) system has been studied to measure plant structure due to its low-cost, close-range, and rapid image capturing ability. However, reconstruction of 3D plant models with complex structure is a time-consuming process and some systems have failed to reconstruct 3D models properly. Therefore, an MCP based SfM system was developed and an appropriate reconstruction method and optimal range of camera-shooting angles were investigated. RESULTS: An MCP system which utilized 10 cameras and a rotary table for plant was developed. The 3D mesh model of a single leaf reconstruction using a set of images taken at each viewing zenith angle (VZA) from 12° (C2 camera) to 60° (C6 camera) by the MCP based SfM system had less undetected or unstable regions in comparison with other VZAs. The 3D mesh model of a whole plant, which merged 3D dense point cloud models built from a set of images taken at each appropriate VZA (Method 1), had high accuracy. The Method 1 error percentages for leaf area, leaf length, leaf width, stem height, and stem width are in the range of 2.6-4.4%, 0.2-2.2%, 1.0-4.9%, 1.9-2.8%, and 2.6-5.7% respectively. Also, the error of the leaf inclination angle was less than 5°. Conversely, the 3D mesh model of a whole plant built directly from a set of images taken at all appropriate VZAs (Method 2) had lower accuracy than that of Method 1. For Method 2, the error percentages of leaf area, leaf length, and leaf width are in the range of 3.1-13.3%, 0.4-3.3%, and 1.6-8.6%, respectively. It was difficult to obtain the error percentages of stem height and stem width because some information was missing in this model. In addition, the calculation time for Method 2 was 1.97 times longer computational time in comparison to Method 1. CONCLUSIONS: In this study, we determined the optimal shooting angles on the MCP based SfM system developed. We found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct 3D model by using images taken at all appropriate VZAs (Method 2). This is because utilization of incorporation of incomplete images to match feature points could result in reduced accuracy in 3D models and the increase in computational time for 3D model reconstruction.

[Interaction between wheat translationally controlled tumor protein TCTP and SNF1-related protein kinase SnRK1].

Translationally controlled tumor proteins (TCTP) and SNF1- related protein kinase (SnRK1) are conserved and widely present in eukaryotic cells. TCTP regulates cell division, plant growth and development, and mediates plant resistance against pathogen infection. SnRK1 participates in a range of physiological processes including sugar metabolism and resistance to abiotic and biotic stresses. Previous work in our laboratory demonstrated that wheat TCTP can respond to Puccinia triticina infection and induce host defense responses. In order to further investigate the mechanism of TaTCTP in wheat resistance to Puccinia triticina infection, we used TAP (tandem affinity purification) and mass spectrometry to screen the potential interactants of TaTCTP. A SNF1- related protein kinase (SnRK1) was identified as a potential interacting protein of TaTCTP. The results of yeast two-hybrid assay showed that TCTP could interact with SnRK1 in yeast, and the yeast carrying TCTP and SnRK1 could grow on SD/-Leu/-Trp/-His/-Ade (SD/-LWHA) medium. The fluorescence signal of the interaction between TCTP and SnRK1 was found to be distributed in the cytoplasm in the Bi-fluorescense complementation experiment. Co-IP experiments further showed that TCTP and SnRK1 could interact in plant cells. This study lays an important foundation for further studying the mechanism of TaTCTP in the interaction between wheat and Puccinia triticina, and it play a great influence on further improving the molecular mechanism of wheat resistant to Puccinia triticina.

Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces.

Quick non-destructive assessment of leaf chlorophyll content (LCC) is important for studying phenotypes related to plant growth and stress resistance. This study was undertaken to investigate the quantitative relationship between LCC and different vegetation indices (VIs) on both adaxial and abaxial surfaces of white poplar (Populus alba), which has dense tubular hairs on its abaxial surface, and Chinese elm (Ulmus pumila var. pendula), which does not show obvious superficial differences except for lighter colour on the abaxial surface. Some published and newly developed VIs were tested to relate them to LCC. The results showed that most of the published VIs had strong relationships with LCC on the one-surface dataset, but did not show a clear relationship with LCC when both adaxial and abaxial surface reflectance data were included. Among the reflectance indices tested, the modified Datt index, (R719-R726)/(R719-R743), performed best and is proposed as a new index for remote estimation of chlorophyll content in plants with varying leaf surface structures. It explained 92% of LCC variation in this research, and the root mean square error of the LCC prediction was 5.23 µg/cm(2). This new index is insensitive to the effects of adaxial and abaxial leaf surface structures and is strongly related to the variation in reflectance caused by chlorophyll content.