Knockout of CAFFEOYL-COA 3-O-METHYLTRANSFERASE 6/6L enhances the S/G ratio of lignin monomers and disease resistance in Nicotiana tabacum

BackgroundNicotiana tabacum is an important economic crop, which is widely planted in the world. Lignin is very important for maintaining the physiological and stress-resistant functions of tobacco. However, higher lignin content will produce lignin gas, which is not conducive to the formation of tobacco quality. To date, how to precisely fine-tune lignin content or composition remains unclear.ResultsHere, we annotated and screened 14 CCoAOMTs in Nicotiana tabacum and obtained homozygous double mutants of CCoAOMT6 and CCoAOMT6L through CRSIPR/Cas9 technology. The phenotype showed that the double mutants have better growth than the wild type whereas the S/G ratio increased and the total sugar decreased. Resistance against the pathogen test and the extract inhibition test showed that the transgenic tobacco has stronger resistance to tobacco bacterial wilt and brown spot disease, which are infected by Ralstonia solanacearum and Alternaria alternata, respectively. The combined analysis of metabolome and transcriptome in the leaves and roots suggested that the changes of phenylpropane and terpene metabolism are mainly responsible for these phenotypes. Furthermore, the molecular docking indicated that the upregulated metabolites, such as soyasaponin Bb, improve the disease resistance due to highly stable binding with tyrosyl-tRNA synthetase targets in Ralstonia solanacearum and Alternaria alternata.ConclusionsCAFFEOYL-COA 3-O-METHYLTRANSFERASE 6/6L can regulate the S/G ratio of lignin monomers and may affect tobacco bacterial wilt and brown spot disease resistance by disturbing phenylpropane and terpene metabolism in leaves and roots of Nicotiana tabacum, such as soyasaponin Bb

Evaluation of Noah-MP Land-Model Uncertainties over Sparsely Vegetated Sites on the Tibet Plateau

The water budget and energy exchange over the Tibetan Plateau (TP) region play an important role on the Asian monsoon. However, it is not well presented in the current land surface models (LSMs). In this study, uncertainties in the Noah with multiparameterization (Noah-MP) LSM are assessed through physics ensemble simulations in three sparsely vegetated sites located in the central TP. The impact of soil organic matter on energy flux and water cycles, along with the influence of uncertainties in precipitation are explored using observations at those sites during the third Tibetan Plateau Experiment from 1August2014 to31July2015. The greatest uncertainties are in the subprocesses of the canopy resistance, soil moisture limiting factors for evaporation, runoff (RNF) and ground water, and surface-layer parameterization. These uncertain subprocesses do not change across the different precipitation datasets. More precipitation can increase the annual total net radiation (Rn), latent heat flux (LH) and RNF, but decrease sensible heat flux (SH). Soil organic matter enlarges the annual total LH by ~26% but lessens the annual total Rn, SH, and RNF by ~7%, 7%, and 39%, respectively. Its effect on the LH and RNF at the Nagqu site, which has a sand soil texture type, is greater than that at the other two sites with sandy loam. This study highlights the importance of precipitation uncertainties and the effect of soil organic matter on the Noah-MP land-model simulations. It provides a guidance to improve the Noah-MP LSM further and hence the land-atmosphere interactions simulated by weather and climate models over the TP region

Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record‐Breaking Meiyu Season Over the Yangtze–Huaihe River Valley in China

Abstract In 2020, the Yangtze–Huai river valley (YHRV) experienced the highest record‐breaking Meiyu season since 1961, which was mainly characterized by the longest duration of precipitation lasting from early‐June to mid‐July, with frequent heavy rainstorms that caused severe flooding and deaths in China. Many studies have investigated the causes of this Meiyu season and its evolution, but the accuracy of precipitation simulations has received little attention. It is important to provide more accurate precipitation forecasts to help prevent and reduce flood disasters, thereby facilitating the maintenance of a healthy and sustainable earth ecosystem. In this study, we determined the optimal scheme among seven land surface model (LSMs) schemes in the Weather Research and Forecasting model for simulating the precipitation in the Meiyu season during 2020 over the YHRV region. We also investigated the mechanisms in the different LSMs that might affect precipitation simulations in terms of water and energy cycling. The results showed that the simulated amounts of precipitation were higher under all LSMs than the observations. The main differences occurred in rainstorm areas (>12 mm/day), and the differences in low rainfall areas were not significant (<8 mm/day). Among all of the LSMs, the Simplified Simple Biosphere (SSiB) model obtained the best performance, with the lowest root mean square error and the highest correlation. The SSiB model even outperformed the Bayesian model averaging result. Finally, some factors responsible for the differences modeling results were investigated to understand the related physical mechanism

Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau

Land surface processes and their coupling to the atmosphere over the Tibetan Plateau (TP) play an important role in modulating the regional and global climate. Therefore, identifying and quantifying uncertainty in these land surface model (LSM) processes are essential for improving climate models. The specifications of land cover and soil texture types, intertwined with the uncertainties in associated vegetation and soil parameters in LSMs, are significant sources of uncertainty due to the lack of detailed land survey in the TP. To differentiate the effects of land cover or soil texture specifications in the Noah with Multiple Parameterizations (Noah-MP) LSM from the effects of uncertainties in the model parameters, this study first identified the most sensitive vegetation and soil parameters through global sensitivity analysis and then conducted parametric ensemble simulations using two land cover data sets and two soil texture data sets over the central TP to estimate their corresponding impacts on the overall model responses. The distinction level and the Kolmogorov-Smirnov test were then applied to assess the differences between the results from parametric ensemble simulations using different land cover or soil texture data sets. The results show that the simulated energy and water fluxes over the central TP are dominated by soil parameters. The canopy height is the most sensitive vegetation parameter, and the Clapp-Hornberger b parameter (the exponent in the function that relates soil water potential and water content) is the most sensitive soil parameter. Relative to the background parametric uncertainties, the Noah-MP LSM could not sufficiently distinguish the effects of changes between forested types or soil texture types, which highlight the need for further quantifying and reducing the parametric uncertainties in LSMs. Further analysis shows significant sensitivities of the distinction level and changes in model response to annual precipitation and vegetation fraction. This work provides a scientific reference for assessing the impacts of land cover or soil texture changes on Noah-MP simulations under future climate change conditions

A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation

Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information

A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation

Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM based on random forest considering soil moisture memory and mass conservation to improve downscaling performance. The lagged SM was added as a predictor to represent soil moisture memory, in addition to the regular predictors in previous downscaling studies. The Soil Moisture Active Passive (SMAP) SM data of the Pearl River Basin were used to test our downscaling method. The results show that the downscaling model obtained good performance on the test set (R2 = 0.848, ubRMSE = 0.034 m3/m3 and Bias = 0.008 m3/m3). The spatial and temporal performance of the RF downscaling model can be improved by adding lagged SM variables. Downscaled data obtained can retain the information of the original SMAP SM data well and show more spatial details, and mass conservation correction is considered to be useful to eliminate systematic bias of the downscaling model. Downscaled SM achieved acceptable performance in in situ validation, though it was inevitably limited by the performance of the original SMAP data. The proposed downscaling method can serve as a powerful tool for the development of high-resolution SM information

The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR–Cas9

In this study, the agricultural traits, alkaloids content and Fourier transform infrared spectroscopy (FT-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR) analysis of the tobacco after Berberine Bridge Enzyme-Like Proteins (BBLs) knockout were investigated. The knockout of BBLs has limited effect on tobacco agricultural traits. After the BBLs knockout, nicotine and most alkaloids are significantly reduced, but the content of myosmine and its derivatives increases dramatically. In order to identify the gene editing of tobacco, principal component analysis (PCA) was performed on the FT-IR and 2DCOS-IR spectroscopy data. The results showed that FT-IR can distinguish between tobacco roots and leaves but cannot classify the gene mutation tobacco from the wild one. 2DCOS-IR can enhance the characteristics of the samples due to the increased apparent resolution of the spectra. Using the autopeaks in the synchronous map for PCA analysis, we successfully identified the mutants with an accuracy of over 90%

CRISPR/Cas9-Mediated Targeted Mutagenesis of Betaine Aldehyde Dehydrogenase 2 (BADH2) in Tobacco Affects 2-Acetyl-1-pyrroline

2-acetyl-1-pyrroline (2AP) is a highly effective volatile compound that gives fragrance to numerous plant species and food. Mutation(s) in the betaine aldehyde dehydrogenase 2 (BADH2) gene results in the accumulation of 2AP. However, the function of BADH genes in tobacco (Nicotiana tabacum L.) remains poorly understood. In this study, we successfully obtained four betaine aldehyde dehydrogenase (BADH) genes from tobacco. Phylogenetic analysis of the protein sequences showed that two of the four BADH genes were closely related to the wolfberry (Lycium barbarum) BADH gene (LbBADH1), so we named them NtBADH1a and NtBADH1b, respectively. The other two BADH genes were orthologues of the tomato (Solanum lycopersicum) aminoaldehyde dehydrogenase 2 (SlAMADH2) gene, and were named NtBADH2a and NtBADH2b, respectively. Expression analysis revealed that the biological functions of NtBADH1a and NtBADH1b were different from those of genes NtBADH2a and NtBADH2b. We introduced mutations into NtBADH1a, NtBADH1b, NtBADH2a and NtBADH2b in tobacco using the CRISPR/Cas9 system and identified transgenic Ntbadh mutant tobacco lines. Single mutants (Ntbadh1a, Ntbadh1b, Ntbadh2a and Ntbadh2b) and double mutants (Ntbadh1a-Ntbadh1b and Ntbadh2a-Ntbadh2b) harbored deletion or insertion of nucleotides, both of which led to the production of a frameshift, preventing protein accumulation. A popcorn-like scent was noticeable in tobacco leaves from the Ntbadh2a-Ntbadh2b double mutant, but not from any single mutant or the Ntbadh1a-Ntbadh1b double mutant or the wild type. Consistent with this observation, we only detected 2AP in fresh leaves from the Ntbadh2a-Ntbadh2b double mutant. These findings indicate that only the combined inactivation of NtBADH2a and NtBADH2b results in 2AP accumulation in tobacco, which was not related to NtBADH1

Establishment and application of Agrobacterium-delivered CRISPR/Cas9 system for wild tobacco (Nicotiana alata) genome editing

Clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) system has been widely applied in cultivated crops, but limited in their wild relatives. Nicotiana alata is a typical wild species of genus Nicotiana that is globally distributed as a horticultural plant and well-studied as a self-incompatibility model. It also has valuable genes for disease resistance and ornamental traits. However, it lacks an efficient genetic transformation and genome editing system, which hampers its gene function and breeding research. In this study, we developed an optimized hypocotyl-mediated transformation method for CRISPR-Cas9 delivery. The genetic transformation efficiency was significantly improved from approximately 1% to over 80%. We also applied the CRISPR-Cas9 system to target the phytoene desaturase (NalaPDS) gene in N. alata and obtained edited plants with PDS mutations with over 50% editing efficiency. To generate self-compatible N. alata lines, a polycistronic tRNA-gRNA (PTG) strategy was used to target exonic regions of allelic S-RNase genes and generate targeted knockouts simultaneously. We demonstrated that our system is feasible, stable, and high-efficiency for N. alata genome editing. Our study provides a powerful tool for basic research and genetic improvement of N. alata and an example for other wild tobacco species