Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars

Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (Citrus reticulata Blanco) and orange (Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus

Simulating the diameter growth responses of Larix gmelini Rupr. and Betula platyphylla Suk. to biotic and abiotic factors in secondary forests in Northeast China

Abstract Key message The diameter growth of Dahurian larch (Larix gmelini Rupr.) and white birch (Betula platyphylla Suk.) species in secondary forest of Northeast China was not only influenced by biological factors such as tree size and stand characteristics, but also significantly affected by topographic and climatic factors such as temperature and precipitation. It is necessary to consider the abiotic factors in simulating the diameter growth. Context Climate change, such as global temperature rise, increased frequency of extreme weather events, and rising sea levels, has put forest ecosystems in an unstable state and has an impact on species composition, growth harvest, productivity and other functions of forests. And this impact varies in climate scenarios, regions and forest types. Aims To gain a comprehensive understanding of the adaptation for key species to their environment in secondary forests in Northeast China, the diameter growth responses of Dahurian larch and white birch to biotic and abiotic factors were simulated to assess the effects of climate on diameter growth. Methods China’s National Forest Continuous Inventory (NFCI) data from 2005 to 2015 were used to develop linear mixed-effects diameter growth models with plot-level random effects, and leave-one-out cross-validation was applied to evaluate the developed models. At the beginning of modeling, correlation analysis and best-subset regression were used to analyze the correlation between the diameter increment and the biotic and abiotic factors. Results (i) Sorting the categories of predictors in descending order based on the relative importance of the significant predictors, diameter growth of Dahurian larch was affected by competition, tree size, topographic conditions, stand attributes, diversity index, and climate factors, while the white birch species was affected by competition, tree size, stand attributes, climate factors, diversity index, and topographic conditions; (ii) the plot-level mixed-effects model, which achieved better fit and prediction performance than did basic linear models of individual-tree diameter growth in the cases of prediction calibration, was preferable for modeling individual-tree diameter growth; (iii) the prediction accuracy of the mixed-effects model increased gradually with increasing size of calibration sample, and the best sampling strategy was the use of nine random trees to calibrate and make predictions with the mixed-effects model for the larch and birch species; (iv) Dahurian larch was dominant in terms of interspecific competition, and the growth of this species was enhanced when it was grown with the birch. Conclusion In addition to biotic factors such as tree size and stand characteristics, the impact of climate on the growth of Dahurian larch and white birch should be considered in future management policies

SERS-Based Immunoassay of Myocardial Infarction Biomarkers on a Microfluidic Chip with Plasmonic Nanostripe Microcones

We developed a new plasmonic nanostripe microcone array (PNMA) substrate-integrated microfluidic chip for the simultaneous surface-enhanced Raman scattering (SERS)-based immunoassay of the creatine kinase MB isoenzyme (CK-MB) and cardiac troponin (cTnI) cardiac markers. The conventional immunoassay usually employs a microtiter plate as the solid capture plate to form the immunocomplexes. However, the two-dimensional (2D) surface of the microtiter plate limits the capture efficiency of the target antigens due to the steric hindrance effect. To address this issue, a gold film-coated microcone array with nanostripes was developed that can provide a large surface area for capture antibody conjugation and serve as a SERS-active substrate. This unique nano–microhierarchical structure showed an excellent light trapping effect and induced surface plasmon resonance to further enhance the Raman signals of the SERS nanoprobes. It significantly improved the sensitivity and applicability of SERS-based immunoassay on the microfluidic chip. With this integrated microfluidic chip, we successfully performed the simultaneous detection of CK-MB and cTnI, and the detection limit can reach 0.01 ng mL–1. It is believed that the PNMA substrate-integrated microfluidic chip would play a critical role in the rapid and sensitive diagnostics of cardiac diseases

DataSheet_1_Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars.docx

Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (Citrus reticulata Blanco) and orange (Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus.</p

Table_1_Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars.xlsx

Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (Citrus reticulata Blanco) and orange (Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus.</p