Comparative Metabolomic Analysis of the Cambium Tissue of Non-transgenic and Multi-Gene Transgenic Poplar (Populus × euramericana 'Guariento')

Comparative Analysis of the Wood Metabolites of Three Poplar Clones Using UPLC-Triple-TOF-MS

Poplar, a woody tree species, is widely used for industrial production and as a protective forest belt. Different clones of poplar exhibit clear variation in terms of morphological and physiological features, however, the impact of the genetic variation on the composition and abundance of wood metabolite have not been fully determined. In this study, ultra-high pressure liquid chromatography-triple time of flight-mass spectrometer (UPLC-Triple-TOF-MS) was used to explore the metabolite changes in poplar wood from three clones, including Populus deltoides CL. '55/65', P. deltoides CL. 'Danhong', and P. nigra CL. 'N179'. A total of 699 metabolites were identified. Clustering analysis and principal component analysis display that the metabolic differences of wood have allowed distinguishing different species of poplar. Meanwhile, eight significantly different metabolites were screened between P. deltoides and P. nigra, which may be considered as valuable markers for chemotaxonomy. In addition, the highly discriminant 352 metabolites were obtained among the three clones, and those may be closely related to the distinction in unique properties (e.g., growth, rigidity and tolerance) of the poplar wood cultivars. This study provides a foundation for further studies on wood metabolomics in poplar, and offers chemotaxonomic markers that will stimulate the early screening of potentially superior trees.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

Transcriptomic Analysis of Mature Transgenic Poplar Expressing the Transcription Factor JERF36 Gene in Two Different Environments

During the last several decades, a number of transgenic or genetically modified tree varieties with enhanced characteristics and new traits have been produced. These trees have become associated with generally unsubstantiated concerns over health and environmental safety. We conducted transcriptome sequencing of transgenic Populus alba × P. berolinensis expressing the transcription factor JERF36 gene (ABJ01) and the non-transgenic progenitor line (9#) to compare the transcriptional changes in the apical buds. We found that 0.77% and 1.31% of the total expressed genes were significant differentially expressed in ABJ01 at the Daqing and Qiqihar sites, respectively. Among them, 30%–50% of the DEGs contained cis-elements recognized by JERF36. Approximately 5% of the total number of expressed genes showed significant differential expression between Daqing and Qiqihar in both ABJ01 and 9#. 10 DEGs resulting from foreign gene introduction, 394 DEGs that resulted solely from the environmental differences, and 47 DEGs that resulted from the combination of foreign gene introduction and the environment were identified. The number of DEGs resulting from environmental factors was significantly greater than that resulting from foreign gene introduction, and the combined effect of the environmental effects with foreign gene introduction was significantly greater than resulting from the introduction of JERF36 alone. GO and KEGG annotation showed that the DEGs mainly participate in the photosynthesis, oxidative phosphorylation, plant hormone signaling, ribosome, endocytosis, and plant-pathogen interaction pathways, which play important roles in the responses to biotic and abiotic stresses ins plant. To enhance its adaptability to salt-alkali stress, the transgenic poplar line may regulate the expression of genes that participate in the photosynthesis, oxidative phosphorylation, MAPK, and plant hormone signaling pathways. The crosstalk between biotic and abiotic stress responses by plant hormones may improve the ability of both transgenic and non-transgenic poplars to defend against pathogens. The results of our study provide a basis for further studies on the molecular mechanisms behind improved stress resistance and the unexpected effects of transgenic gene expression in poplars, which will be significant for improving the biosafety evaluation of transgenic trees and accelerating the breeding of new varieties of forest trees resistant to environmental stresses.

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

Integrated Transcriptomic and Proteomic Analysis in the Roadmap of the Xylem Development Stage in Populus tomentosa

Xylem development plays an important role in the wood formation of plants. In this study, we found that xylem development was a rapid thickening process characterized by initially rapid increases in the number of tracheary elements and fiber cells and the thickness of the secondary walls that later plateaued. Transcriptome analysis showed that the xylan and lignin biosynthetic pathways, which are involved in the early rapid thickening of the xylem, were mainly upregulated in the second month. The expression of a total of 124 transcription factors (TFs), including 28 NAC TFs and 31 MYB TFs, peaked in 2- and 3-month-old plants compared with 1-month-old plants. Based on previous studies and the key cis-acting elements secondary wall NAC-binding elements, secondary wall MYB-responsive elements, W-box and TGTG[T/G/C], 10 TFs related to xylem development, 50 TFs with unknown function, 98 cell wall biosynthetic genes, and 47 programmed cell death (PCD) genes were used to construct a four-layer transcriptional regulatory network (TRN) with poplar NAC domain TFs to characterize the transcriptional regulation of cell wall biosynthesis and PCD in Populus tomentosa. The proteome revealed that post-transcriptional modification may be widely involved in lignification development. Overall, our results revealed that xylem development is a rapid thickening process in P. tomentosa, and expression patterns varied temporally from cell division to cell death.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

A metabolite roadmap of the wood-forming tissue in Populus tremula

Wood, or secondary xylem, is the product of xylogenesis, a developmental process that begins with the proliferation of cambial derivatives and ends with mature xylem fibers and vessels with lignified secondary cell walls. Fully mature xylem has undergone a series of cellular processes, including cell division, cell expansion, secondary wall formation, lignification and programmed cell death. A complex network of interactions between transcriptional regulators and signal transduction pathways controls wood formation. However, the role of metabolites during this developmental process has not been comprehensively characterized. To evaluate the role of metabolites during wood formation, we performed a high spatial resolution metabolomics study of the wood-forming zone of Populus tremula, including laser dissected aspen ray and fiber cells. We show that metabolites show specific patterns within the wood-forming zone, following the differentiation process from cell division to cell death. The data from profiled laser dissected aspen ray and fiber cells suggests that these two cell types host distinctly different metabolic processes. Furthermore, by integrating previously published transcriptomic and proteomic profiles generated from the same trees, we provide an integrative picture of molecular processes, for example, deamination of phenylalanine during lignification is of critical importance for nitrogen metabolism during wood formation.

Negligible transcriptome and metabolome alterations in RNAi insecticidal maize against Monolepta hieroglyphica

RNAi-based genetically modified maize resistant to Monolepta hieroglyphica (Motschulsky) was demonstrated with negligible transcriptome and metabolome alterations compared to its unmodified equivalent. As one of the most prevalent insect pests afflicting various crops, Monolepta hieroglyphica (Motschulsky) causes severe loss of agricultural and economic productivity for many years in China. In an effort to reduce damages, in this study, an RNA interference (RNAi)-based genetically modified (GM) maize was developed. It was engineered to produce MhSnf7 double-stranded RNAs (dsRNAs), which can suppress the Snf7 gene expression and then lead M. hieroglyphica to death. Field trail analysis confirmed the robustly insecticidal ability of the MhSnf7 GM maize to resist damages by M. hieroglyphica. RNA sequencing analysis identified that only one gene was differentially expressed in the MhSnf7 GM maize compared to non-GM maize, indicating that the transcriptome in MhSnf7 GM maize is principally unaffected by the introduction of the MhSnf7 dsRNA expression vector. Likewise, metabolomics analysis identified that only 8 out of 5787 metabolites were significantly changed. Hence, the integration of transcriptomics and metabolomics demonstrates that there are negligible differences between MhSnf7 GM maize and its unmodified equivalent. This study not only presents a comprehensive assessment of cellular alteration in terms of gene transcription and metabolite abundance in RNAi-based GM maize, but also could be used as a reference for evaluating the unintended effect of GM crops.