Non-target molecular network and putative genes of flavonoid biosynthesis in Erythrina velutina Willd., a Brazilian semiarid native woody plant.

Erythrina velutina is a Brazilian native tree of the Caatinga (a unique semiarid biome). It is widely used in traditional medicine showing anti-inflammatory and central nervous system modulating activities. The species is a rich source of specialized metabolites, mostly alkaloids and flavonoids. To date, genomic information, biosynthesis, and regulation of flavonoids remain unknown in this woody plant. As part of a larger ongoing research goal to better understand specialized metabolism in plants inhabiting the harsh conditions of the Caatinga, the present study focused on this important class of bioactive phenolics. Leaves and seeds of plants growing in their natural habitat had their metabolic and proteomic profiles analyzed and integrated with transcriptome data. As a result, 96 metabolites (including 43 flavonoids) were annotated. Transcripts of the flavonoid pathway totaled 27, of which EvCHI, EvCHR, EvCHS, EvCYP75A and EvCYP75B1 were identified as putative main targets for modulating the accumulation of these metabolites. The highest correspondence of mRNA vs. protein was observed in the differentially expressed transcripts. In addition, 394 candidate transcripts encoding for transcription factors distributed among the bHLH, ERF, and MYB families were annotated. Based on interaction network analyses, several putative genes of the flavonoid pathway and transcription factors were related, particularly TFs of the MYB family. Expression patterns of transcripts involved in flavonoid biosynthesis and those involved in responses to biotic and abiotic stresses were discussed in detail. Overall, these findings provide a base for the understanding of molecular and metabolic responses in this medicinally important species. Moreover, the identification of key regulatory targets for future studies aiming at bioactive metabolite production will be facilitated.

Paper-based optoelectronic nose for identification of indoor air pollution caused by 3D printing thermoplastic filaments.

Commercial printers based on fused deposition modeling (FDM) are widely adopted for 3D printing applications. This method consists of the heating of polymeric filaments over the melting point followed by their deposition onto a solid base to create the desirable 3D structure. Prior investigation using chromatographic techniques has shown that chemical compounds (e.g. VOCs), which can be harmful to users, are emitted during the printing process, producing adverse effects to human health and contributing to indoor air pollution. In this study, we present a simple, inexpensive and disposable paper-based optoelectronic nose (i.e. colorimetric sensor array) to identify the gaseous emission fingerprint of five different types of thermoplastic filaments (ABS, TPU, PETG, TRITAN and PLA) in the indoor environment. The optoelectronic nose is comprised of selected 15 dyes with different chemical properties deposited onto a microfluidic paper-based device with spots of 5 mm in diameter each. Digital images were obtained from an ordinary flatbed scanner, and the RGB information collected before and after air exposure was extracted by using an automated routine designed in MATLAB, in which the color changes provide a unique fingerprint for each filament in 5 min of printing. Reproducibility was obtained in the range of 2.5-10% (RSD). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were successfully employed, showing suitable discrimination of all studied filaments and the non-polluted air. Besides, air spiked with vapors of the most representative VOCs were analyzed by the optoelectronic nose and visually compared to each filament. The described study shows the potential of the paper-based optoelectronic nose to monitor possible hazard emissions from 3D printers.

Erythrina velutina Willd. alkaloids: Piecing biosynthesis together from transcriptome analysis and metabolite profiling of seeds and leaves.

Introduction: Natural products of pharmaceutical interest often do not reach the drug market due to the associated low yields and difficult extraction. Knowledge of biosynthetic pathways is a key element in the development of biotechnological strategies for plant specialized metabolite production. Erythrina species are mainly used as central nervous system depressants in folk medicine and are important sources of bioactive tetracyclic benzylisoquinoline alkaloids (BIAs), which can act on several pathology-related biological targets. Objectives: In this sense, in an unprecedented approach used with a non-model Fabaceae species grown in its unique arid natural habitat, a combined transcriptome and metabolome analyses (seeds and leaves) is presented. Methods: The Next Generation Sequencing-based transcriptome (de novo RNA sequencing) was carried out in a NextSeq 500 platform. Regarding metabolite profiling, the High-resolution Liquid Chromatography was coupled to DAD and a micrOTOF-QII mass spectrometer by using electrospray ionization (ESI) and Time of Flight (TOF) analyzer. The tandem MS/MS data were processed and analyzed through Molecular Networking approach. Results: This detailed macro and micromolecular approach applied to seeds and leaves of E. velutina revealed 42 alkaloids, several of them unique. Based on the combined evidence, 24 gene candidates were put together in a putative pathway leading to the singular alkaloid diversity of this species. Conclusion: Overall, these results could contribute by indicating potential biotechnological targets for modulation of erythrina alkaloids biosynthesis as well as improve molecular databases with omic data from a non-model medicinal plant, and reveal an interesting chemical diversity of Erythrina BIA harvested in Caatinga.

Improvement of bioactive metabolite production in microbial cultures-A systems approach by OSMAC and deconvolution-based 1 HNMR quantification.

Traditionally, the screening of metabolites in microbial matrices is performed by monocultures. Nonetheless, the absence of biotic and abiotic interactions generally observed in nature still limit the chemical diversity and leads to "poorer" chemical profiles. Nowadays, several methods have been developed to determine the conditions under which cryptic genes are activated, in an attempt to induce these silenced biosynthetic pathways. Among those, the one strain, many compounds (OSMAC) strategy has been applied to enhance metabolic production by a systematic variation of growth parameters. The complexity of the chemical profiles from OSMAC experiments has required increasingly robust and accurate techniques. In this sense, deconvolution-based 1 HNMR quantification have emerged as a promising methodology to decrease complexity and provide a comprehensive perspective for metabolomics studies. Our present work shows an integrated strategy for the increased production and rapid quantification of compounds from microbial sources. Specifically, an OSMAC design of experiments (DoE) was used to optimize the microbial production of bioactive fusaric acid, cytochalasin D and 3-nitropropionic acid, and Global Spectral Deconvolution (GSD)-based 1 HNMR quantification was carried out for their measurement. The results showed that OSMAC increased the production of the metabolites by up to 33% and that GSD was able to extract accurate NMR integrals even in heavily coalescence spectral regions. Moreover, GSD-1 HNMR quantification was reproducible for all species and exhibited validated results that were more selective and accurate than comparative methods. Overall, this strategy up-regulated important metabolites using a reduced number of experiments and provided fast analyte monitor directly in raw extracts.

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling.

INTRODUCTION: The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products. OBJECTIVE: Create a multivariate model to identify antiplasmodial metabolites from 1H NMR data of two African medicinal plants, Keetia leucantha and K. venosa. METHODS: The extracts of twigs and leaves of Keetia species were measured by 1H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation. RESULTS: Unsupervised 1H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples. CONCLUSION: NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.

Partial least squares model and design of experiments toward the analysis of the metabolome of Jatropha gossypifolia leaves: Extraction and chromatographic fingerprint optimization.

A major challenge in metabolomic studies is how to extract and analyze an entire metabolome. So far, no single method was able to clearly complete this task in an efficient and reproducible way. In this work we proposed a sequential strategy for the extraction and chromatographic separation of metabolites from leaves Jatropha gossypifolia using a design of experiments and partial least square model. The effect of 14 different solvents on extraction process was evaluated and an optimized separation condition on liquid chromatography was estimated considering mobile phase composition and analysis time. The initial conditions of extraction using methanol and separation in 30 min between 5 and 100% water/methanol (1:1 v/v) with 0.1% of acetic acid, 20 µL sample volume, 3.0 mL min(-1) flow rate and 25°C column temperature led to 107 chromatographic peaks. After the optimization strategy using i-propanol/chloroform (1:1 v/v) for extraction, linear gradient elution of 60 min between 5 and 100% water/(acetonitrile/methanol 68:32 v/v with 0.1% of acetic acid), 30 µL sample volume, 2.0 mL min(-1) flow rate, and 30°C column temperature, we detected 140 chromatographic peaks, 30.84% more peaks compared to initial method. This is a reliable strategy using a limited number of experiments for metabolomics protocols.