Distinguishing transgenic from non-transgenic Arabidopsis plants by (1)H NMR-based metabolic fingerprinting

Untargeted Proteomics-Based Approach to Investigate Unintended Changes in Genetically Modified Maize for Environmental Risk Assessment Purpose

Profiling technologies, such as proteomics, allow the simultaneous measurement and comparison of thousands of plant components without prior knowledge of their identity. The combination of these non-targeted methods facilitates a more comprehensive approach than targeted methods and thus provides additional opportunities to identify genotypic changes resulting from genetic modification, including new allergens or toxins. The purpose of this study was to investigate unintended changes in GM Bt maize grown in South Africa. In the present study, we used bi-dimensional gel electrophoresis based on fluorescence staining, coupled with mass spectrometry in order to compare the proteome of the field-grown transgenic hybrid (MON810) and its near-isogenic counterpart. Proteomic data showed that energy metabolism and redox homeostasis were unequally modulated in GM Bt and non-GM maize variety samples. In addition, a potential allergenic protein—pathogenesis related protein −1 has been identified in our sample set. Our data shows that the GM variety is not substantially equivalent to its non-transgenic near-isogenic variety and further studies should be conducted in order to address the biological relevance and the potential risks of such changes. These finding highlight the suitability of unbiased profiling approaches to complement current GMO risk assessment practices worldwide.

Comparison of Metabolic Profiling of Arabidopsis Inflorescences Between Landsberg erecta and Columbia, and Meiosis-Defective Mutants by 1H-NMR Spectroscopy

With the rapid development of omics technologies during the last several decades, genomics, transcriptomics, and proteomics have been extensively used to characterize gene or protein functions in many organisms at the cell or tissue level. However, metabolomics has not been conducted in reproductive organs, with a focus on meiosis in plants. In this study, we adopted a nuclear magnetic resonance (NMR)-based metabolomics approach to reveal the metabolic profile of inflorescences from two Arabidopsis accessions, Columbia (Col) and Landsberg erecta (Ler), and several sterile mutants caused by meiosis defects. We identified 68 dominant metabolites in the samples. Col and Ler displayed distinct metabolite profiles. Interestingly, mutants with similar meiotic defects, such as Atrad51-3, Atrfc1-2, and Atpol2a-2, exhibited similar alterations in metabolites, including upregulation of energy metabolites and promotion of compounds related to maintenance of genomic stability, cytoplasmic homeostasis, and membrane integrity. The collective data reveal distinct changes in metabolites in Arabidopsis inflorescences between the Col and Ler wild type accessions. NMR-based metabolomics could be an effective tool for molecular phenotyping in studies of aspects of plant reproductive development such as meiosis.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s43657-021-00012-3.

Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases

In the recent years, there has been an increase in the number of metabolomic approaches used, in parallel with proteomic and functional genomic studies. The wide variety of chemical types of metabolites available has also accelerated the use of different techniques in the investigation of the metabolome. At present, metabolomics is applied to investigate several human diseases, to improve their diagnosis and prevention, and to design better therapeutic strategies. In addition, metabolomic studies are also being carried out in areas such as toxicology and pharmacology, crop breeding, and plant biotechnology. In this review, we emphasize the use and application of metabolomics in human diseases and plant research to improve human health.

Overexpression of ORCA3 and G10H in Catharanthus roseus plants regulated alkaloid biosynthesis and metabolism revealed by NMR-metabolomics

In order to improve the production of the anticancer dimeric indole alkaloids in Catharanthuse roseus, much research has been dedicated to culturing cell lines, hairy roots, and efforts to elucidate the regulation of the monoterpenoid indole alkaloid (MIA) biosynthesis. In this study, the ORCA3 (Octadecanoid-derivative Responsive Catharanthus AP2-domain) gene alone or integrated with the G10H (geraniol 10-hydroxylase) gene were first introduced into C. roseus plants. Transgenic C. roseus plants overexpressing ORCA3 alone (OR lines), or co-overexpressing G10H and ORCA3 (GO lines) were obtained by genetic modification. ORCA3 overexpression induced an increase of AS, TDC, STR and D4H transcripts but did not affect CRMYC2 and G10H transcription. G10H transcripts showed a significant increase under G10H and ORCA3 co-overexpression. ORCA3 and G10H overexpression significantly increased the accumulation of strictosidine, vindoline, catharanthine and ajmalicine but had limited effects on anhydrovinblastine and vinblastine levels. NMR-based metabolomics confirmed the higher accumulation of monomeric indole alkaloids in OR and GO lines. Multivariate data analysis of (1)H NMR spectra showed change of amino acid, organic acid, sugar and phenylpropanoid levels in both OR and GO lines compared to the controls. The result indicated that enhancement of MIA biosynthesis by ORCA3 and G10H overexpression might affect other metabolic pathways in the plant metabolism of C. roseus.

Advantage of Applying OSC to (1)H NMR-Based Metabonomic Data of Celiac Disease

BACKGROUND

Celiac disease (CD) is a disorder associated with body reaction to gluten. After the gluten intake, an immune reaction against the protein occurs and damages villi of small intestine in celiac patients gradually.

OBJECTIVES

The OSC, a filtering method for minimization of inter- and intra-spectrometer variations that influence on data acquisition, was applied to biofluid NMR data of CD patients.

PATIENTS AND METHODS

In this study, metabolites of total 56 serum samples from 12 CD patients, 15 CD patients taking gluten-free diet (GFD), and 29 healthy cases were analyzed using nuclear magnetic resonance (NMR) and associated theoretical analysis. Employing ProMetab (version ProMetab_v3_3) software, data obtained from NMR spectra were reduced and orthogonal signal correction (OSC) effect on celiac disease metabonomics before and after the separation by principle component analysis (PCA) was investigated.

RESULTS

The three groups were separated by OSC and findings were analyzed by partial least squares discriminant analysis (PLS-DA) method. Root mean square error of calibration (RMSEc) and correlation coefficient of calibration (Rc) for PLS-DA referred to an efficient group separation filtered by OSC.

CONCLUSIONS

The applied leave-one-out cross-validation to PLS-DA method performed along with OSC confirmed validation of data analysis. Finally four metabolites are introduced as CD biomarkers.

A rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by direct analysis in real-time mass spectrometry

BACKGROUND

Efficient high throughput screening systems of useful mutants are prerequisite for study of plant functional genomics and lots of application fields. Advance in such screening tools, thanks to the development of analytic instruments. Direct analysis in real-time (DART)-mass spectrometry (MS) by ionization of complex materials at atmospheric pressure is a rapid, simple, high-resolution analytical technique. Here we describe a rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by DART-MS.

RESULTS

To determine whether this DART-MS combined by multivariate analysis can perform genetic discrimination based on global metabolic profiling, intact Arabidopsis thaliana mutant seeds were subjected to DART-MS without any sample preparation. Partial least squares-discriminant analysis (PLS-DA) of DART-MS spectral data from intact seeds classified 14 different lines of seeds into two distinct groups: Columbia (Col-0) and Landsberg erecta (Ler) ecotype backgrounds. A hierarchical dendrogram based on partial least squares-discriminant analysis (PLS-DA) subdivided the Col-0 ecotype into two groups: mutant lines harboring defects in the phenylpropanoid biosynthetic pathway and mutants without these defects. These results indicated that metabolic profiling with DART-MS could discriminate intact Arabidopsis seeds at least ecotype level and metabolic pathway level within same ecotype.

CONCLUSION

The described DART-MS combined by multivariate analysis allows for rapid screening and metabolic characterization of lots of Arabidopsis mutant seeds without complex metabolic preparation steps. Moreover, potential novel metabolic markers can be detected and used to clarify the genetic relationship between Arabidopsis cultivars. Furthermore this technique can be applied to predict the novel gene function of metabolic mutants regardless of morphological phenotypes.