Identifying Natural Products (NPs) as potential UPR inhibitors for crop protection

Bruguière, Antoine; Ray, A-M Le; Bréard, Dimitri; Blon, N; Bataillé, Nelly; Guillemette, Thomas; Simoneau, Philippe; Richomme, Pascal

doi:10.1055/s-0036-1596783

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the former example, one may wonder if a fractionation step is required before 13 C NMR dereplication of complex mixtures, including compounds exhibiting similar backbones. Thus, in the framework of a project that aimed at discovering new Unfolded Protein Response (UPR) modulators, − the same methodology was finally applied to a G. mangostana fruit peel apolar extract together with one chromatography fraction. Mangosteen is well-known to contain bioactive prenylated xanthones.…”

Section: Resultsmentioning

confidence: 99%

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

et al. 2020

View full text Add to dashboard Cite

Whether chemists or biologists, researchers dealing with metabolomics require tools to decipher complex mixtures. As a part of metabolomics and initially dedicated to identifying bioactive natural products, dereplication aims at reducing the usual timeconsuming process of known compounds isolation. Mass spectrometry and nuclear magnetic resonance are the most commonly reported analytical tools during dereplication analysis. Though it has low sensitivity, 13 C NMR has many advantages for such a study. Notably, it is nonspecific allowing simultaneous high-resolution analysis of any organic compounds including stereoisomers. Since NMR spectrometers nowadays provide useful data sets in a reasonable time frame, we have embarked upon writing software dedicated to 13 C NMR dereplication. The present study describes the development of a freely distributed algorithm, namely MixONat and its ability to help researchers decipher complex mixtures. Based on Python 3.5, MixONat analyses a { 1 H}-13 C NMR spectrum optionally combined with DEPT-135 and 90 datato distinguish carbon types (i.e., CH 3 , CH 2 , CH, and C)as well as a MW filtering. The software requires predicted or experimental carbon chemical shifts (δc) databases and displays results that can be refined based on user interactions. As a proof of concept, this 13

show abstract

Section: Resultsmentioning

confidence: 99%

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To confirm that the metabolites predicted by the MixONat software correspond to those present in the analysed sample, the experimental chemical shifts must be compared with spectroscopic data reported in the literature (in the same solvent) for the proposed metabolite; a difference no greater than 0.4 ppm between the experimental and reported data will confirm the identification. 21 The 13 C-NMR dereplication analysis of the macluraxanthone (1) rich fraction 1K predicted the presence of pyranoxanthones, ranking blancoxanthone (2) [score: 0.74 (17/23C)] and jacareubin (3) [score: 0.72 (13/18C)] in the first and second positions, with macluraxanthone (1) [score: 0.65 (15/23C)] appearing in the third position when using a Calophyllum DB (Figure S8), and in the sixth position when using Xanthones DB (Figure S9). These results can be expected when taking into account the similarities in the chemical structures of the xanthones 1, 2, and 3 (Figure 1); however, comparing the experimental chemical shifts with those reported for 1, 24,25 2, 25 and 3 45 in the literature (Table S2) confirmed the identification of macluraxanthone (1) as the major component in fraction 1K…”

Section: Resultsmentioning

confidence: 99%

Using ¹³C‐NMR dereplication to aid in the identification of xanthones present in the stem bark extract of Calophyllum brasiliense

Silva-Castro

Derbré

Ray

et al. 2021

Phytochemical Analysis

View full text Add to dashboard Cite

Introduction: Xanthones are metabolites with a variety of biological properties. The Clusiaceae family, which until recently included the genus Calophyllum, is recognised for its production of monohydroxylated and polyhydroxylated xanthones. Presently, C. brasiliense is the only Calophyllum spp. known to occur in the Yucatan peninsula. Objective: To use a combination of traditional phytochemical methods and carbon-13 nuclear magnetic resonance ( 13 C-NMR) dereplication analysis to identify xanthones in the stem bark of C. brasiliense. Material and methods: Initial fractionation and purification of the stem bark extract of C. brasiliense produced macluraxanthone (1). Additional xanthones, together with chromanones and terpenoids, were identified using 13 C-NMR dereplication analysis in different semipurified fractions obtained from the low and medium polarity fractions of the stem bark extract of C. brasiliense.Results: Initial identification of macluraxanthone (1) was confirmed by 13 C-NMR dereplication analysis; additionally, 13 C-NMR dereplication analysis allowed the identification of a number of monohydroxylated and polyhydroxylated xanthones, together with chromanones and terpenoids. Conclusion:This study confirms C. brasiliense as a rich source of xanthones and the 13 C-NMR dereplication analysis as a suitable method to quickly identify the presence of different families of secondary metabolites in semipurified fractions.

show abstract

A review on Cotton Leaf Disease Detection with Balanced and Imbalanced Data

2023

cset

View full text Add to dashboard Cite

Cotton is a major crop in India, and its production is essential to the country's economy. However, cotton plants are susceptible to a number of diseases, which can cause significant yield losses. Early detection of these diseases is crucial, but manual identification can be challenging. This paper reviews the use of machine learning for cotton leaf disease detection. A number of classification methods and algorithms are discussed, including convolutional neural networks (CNNs), random forests, and SMOTE. The results show that machine learning can be used to achieve high accuracy in detecting cotton leaf diseases, even in the case of imbalanced datasets. The paper also discusses the importance of balancing class distributions in order to improve model performance. A number of data-level and classifier-level techniques are evaluated, and the results show that the Weighted Random Forest algorithm is effective in handling imbalanced datasets. The findings of this paper suggest that machine learning is a promising tool for the early detection of cotton leaf diseases. This could help to reduce yield losses and improve the profitability of cotton farming in India.

show abstract

Identifying Natural Products (NPs) as potential UPR inhibitors for crop protection

Cited by 3 publications

References 0 publications

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

Using ¹³C‐NMR dereplication to aid in the identification of xanthones present in the stem bark extract of Calophyllum brasiliense

A review on Cotton Leaf Disease Detection with Balanced and Imbalanced Data

Contact Info

Product

Resources

About

Identifying Natural Products (NPs) as potential UPR inhibitors for crop protection

Cited by 3 publications

References 0 publications

MixONat, a Software for the Dereplication of Mixtures Based on 13C NMR Spectroscopy

MixONat, a Software for the Dereplication of Mixtures Based on 13C NMR Spectroscopy

Using 13C‐NMR dereplication to aid in the identification of xanthones present in the stem bark extract of Calophyllum brasiliense

A review on Cotton Leaf Disease Detection with Balanced and Imbalanced Data

Contact Info

Product

Resources

About

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

Using ¹³C‐NMR dereplication to aid in the identification of xanthones present in the stem bark extract of Calophyllum brasiliense