Under different concentrations of the base potassium deuteroxide KOD, the progress of reactions, such as enolization, D-substitution, isomerization, and conformational changes of diketopiperazine cyclo(L-Pro-L-Xxx) and cyclo(D-Pro-L-Xxx) (Xxx = Phe, Tyr) in D2O solution, was investigated by 1H nuclear magnetic resonance (NMR). Cyclo(L-Pro-L-Xxx) is mostly isomerized to cyclo(D-Pro-L-Xxx) in D2O solution, whereas cyclo(D-Pro-L-Xxx) is only slightly isomerized to cyclo(L-Pro-L-Xxx) even under stronger basic conditions. After adding a deuterated organic solvent (CD3COCD3, CD3SOCD3 or CD3OD) to a D2O solution of cyclo(L-Pro-L-Xxx), cyclo(D-Pro-L-Xxx), or increasing the temperature of the D2O solution, CH-π interaction between H9 and the benzene ring of cyclo (D-Pro-L-Xxx) was stronger than that between H8α and the benzene ring of cyclo(L-Pro-L-Xxx).
{"title":"Analysis of the isomerization of diketopiperazine consisting of proline and aromatic amino acid residues using nuclear magnetic resonance","authors":"Takashi Ishizu, Popuri Sato, Shiori Tsuyama, Ryosuke Nagao, Kanae Fujiki, Amia Yamaji","doi":"10.1002/ansa.202100047","DOIUrl":"10.1002/ansa.202100047","url":null,"abstract":"<p>Under different concentrations of the base potassium deuteroxide KOD, the progress of reactions, such as enolization, D-substitution, isomerization, and conformational changes of diketopiperazine cyclo(L-Pro-L-Xxx) and cyclo(D-Pro-L-Xxx) (Xxx = Phe, Tyr) in D<sub>2</sub>O solution, was investigated by <sup>1</sup>H nuclear magnetic resonance (NMR). Cyclo(L-Pro-L-Xxx) is mostly isomerized to cyclo(D-Pro-L-Xxx) in D<sub>2</sub>O solution, whereas cyclo(D-Pro-L-Xxx) is only slightly isomerized to cyclo(L-Pro-L-Xxx) even under stronger basic conditions. After adding a deuterated organic solvent (CD<sub>3</sub>COCD<sub>3</sub>, CD<sub>3</sub>SOCD<sub>3</sub> or CD<sub>3</sub>OD) to a D<sub>2</sub>O solution of cyclo(L-Pro-L-Xxx), cyclo(D-Pro-L-Xxx), or increasing the temperature of the D<sub>2</sub>O solution, CH-π interaction between H<sub>9</sub> and the benzene ring of cyclo (D-Pro-L-Xxx) was stronger than that between H<sub>8α</sub> and the benzene ring of cyclo(L-Pro-L-Xxx).</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202100047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43539860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medicinal plant metabolomics has emerged as a goldmine for the natural product chemists. It provides a pool of bioactive phytoconstituents leading to accelerated novel discoveries and the elucidation of a variety of biosynthetic pathways. Further, it also acts as an innovative tool for herbal medicine's scientific validation and quality assurance. This review highlights different strategies and analytical techniques employed in the practice of metabolomics. Further, it also discusses several other applications and advantages of metabolomics in the area of natural product chemistry. Additional examples of integrating metabolomics with multivariate data analysis techniques for some Indian medicinal plants are also reviewed. Recent technical advances in mass spectrometry-based hyphenated techniques, nuclear magnetic resonance-based techniques, and comprehensive hyphenated technologies for phytometabolite profiling studies have also been reviewed. Mass Spectral Imaging (MSI) has been presented as a highly promising method for high precision in situ spatiotemporal monitoring of phytometabolites. We conclude by introducing GNPS (Global Natural Products Social Molecular Networking) as an emerging platform to make social networks of related molecules, to explore data and to annotate more metabolites, and expand the networks to novel “predictive” metabolites that can be validated.
药用植物代谢组学已成为天然产物化学家的一座金矿。它提供了一个生物活性植物成分池,导致加速新发现和各种生物合成途径的阐明。此外,它还作为草药科学验证和质量保证的创新工具。这篇综述强调了在代谢组学实践中采用的不同策略和分析技术。此外,还讨论了代谢组学在天然产物化学领域的其他几个应用和优势。本文还回顾了将代谢组学与一些印度药用植物的多变量数据分析技术相结合的其他例子。本文还综述了基于质谱的联线技术、基于核磁共振的联线技术以及用于植物代谢物谱分析研究的综合联线技术的最新技术进展。质谱成像(MSI)是一种非常有前途的高精度植物代谢物原位时空监测方法。最后,我们介绍了GNPS (Global Natural Products Social Molecular Networking,全球天然产物社会分子网络)作为一个新兴的平台,可以建立相关分子的社会网络,探索数据并注释更多的代谢物,并将网络扩展到新的“预测性”代谢物,这些代谢物可以被验证。
{"title":"A conversation between hyphenated spectroscopic techniques and phytometabolites from medicinal plants","authors":"Shivani Puri, Dinkar Sahal, Upendra Sharma","doi":"10.1002/ansa.202100021","DOIUrl":"10.1002/ansa.202100021","url":null,"abstract":"<p>Medicinal plant metabolomics has emerged as a goldmine for the natural product chemists. It provides a pool of bioactive phytoconstituents leading to accelerated novel discoveries and the elucidation of a variety of biosynthetic pathways. Further, it also acts as an innovative tool for herbal medicine's scientific validation and quality assurance. This review highlights different strategies and analytical techniques employed in the practice of metabolomics. Further, it also discusses several other applications and advantages of metabolomics in the area of natural product chemistry. Additional examples of integrating metabolomics with multivariate data analysis techniques for some Indian medicinal plants are also reviewed. Recent technical advances in mass spectrometry-based hyphenated techniques, nuclear magnetic resonance-based techniques, and comprehensive hyphenated technologies for phytometabolite profiling studies have also been reviewed. Mass Spectral Imaging (MSI) has been presented as a highly promising method for high precision in situ spatiotemporal monitoring of phytometabolites. We conclude by introducing GNPS (Global Natural Products Social Molecular Networking) as an emerging platform to make social networks of related molecules, to explore data and to annotate more metabolites, and expand the networks to novel “predictive” metabolites that can be validated.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202100021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47704172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pharmaco-metabolomics uses metabolic phenotypes for the prediction of inter-individual variations in drug response and helps in understanding the mechanisms of drug action. The field has made significant progress over the last 14 years with numerous studies providing clinical evidence for personalised medicine. However, discovered pharmaco-metabolomic biomarkers are not yet translated into clinics due to a lack of large-scale validation. Integration of targeted and untargeted metabolomics workflows into pharmacokinetic analysis and drug development can advance the field from bench to bedside. Also, Indian pharmaceutical research and its bioanalytical infrastructure are in a position to take on these opportunities by addressing challenges such as appropriate training and regulatory compliance.
{"title":"Pharmaco-metabolomics opportunities in drug development and clinical research","authors":"Prasad Phapale","doi":"10.1002/ansa.202000178","DOIUrl":"10.1002/ansa.202000178","url":null,"abstract":"<p>Pharmaco-metabolomics uses metabolic phenotypes for the prediction of inter-individual variations in drug response and helps in understanding the mechanisms of drug action. The field has made significant progress over the last 14 years with numerous studies providing clinical evidence for personalised medicine. However, discovered pharmaco-metabolomic biomarkers are not yet translated into clinics due to a lack of large-scale validation. Integration of targeted and untargeted metabolomics workflows into pharmacokinetic analysis and drug development can advance the field from bench to bedside. Also, Indian pharmaceutical research and its bioanalytical infrastructure are in a position to take on these opportunities by addressing challenges such as appropriate training and regulatory compliance.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202000178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45923050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inborn errors of metabolism (IEMs) are a group of disorders caused by disruption of metabolic pathways, which leads to accumulation, decreased circulating levels, or increased excretion of metabolites as a consequence of the underlying genetic defects. These heterogeneous groups of disorders cause significant neonatal and infant mortality across the whole world and it is of utmost concern for developing countries like India owing to lack of awareness and standard preventive strategies like newborn screening (NBS). Though the predictive cumulative incidence of IEMs is said to be ∼1:800 newborns, data pertaining to the true prevalence of individual IEMs is not available in the context of Indian population. There is a need for a large population-based study to get a clear picture of the prevalence of different IEMs. One of the best ways to screen for IEMs is by applying advanced liquid chromatography-mass spectrometry (LC-MS) technology using a quantitative metabolomics approaches such as selected or multiple reaction monitoring (SRM or MRM). Recent developments in LC-MS/MRM based quantification of marker metabolites in newborns have opened a novel opportunity to screen multiple disorders simultaneously from a minuscule volume of biological fluids. In this review article, we have highlighted how LC-MS/MRM based metabolomics approach with its high sensitivity and diagnostic capability can make an impact on the nation's public health through NBS programs.
{"title":"Applications of quantitative metabolomics to revolutionize early diagnosis of inborn errors of metabolism in India","authors":"Jisha Chandran, Anikha Bellad, Madan Gopal Ramarajan, Kannan Rangiah","doi":"10.1002/ansa.202100010","DOIUrl":"10.1002/ansa.202100010","url":null,"abstract":"<p>Inborn errors of metabolism (IEMs) are a group of disorders caused by disruption of metabolic pathways, which leads to accumulation, decreased circulating levels, or increased excretion of metabolites as a consequence of the underlying genetic defects. These heterogeneous groups of disorders cause significant neonatal and infant mortality across the whole world and it is of utmost concern for developing countries like India owing to lack of awareness and standard preventive strategies like newborn screening (NBS). Though the predictive cumulative incidence of IEMs is said to be ∼1:800 newborns, data pertaining to the true prevalence of individual IEMs is not available in the context of Indian population. There is a need for a large population-based study to get a clear picture of the prevalence of different IEMs. One of the best ways to screen for IEMs is by applying advanced liquid chromatography-mass spectrometry (LC-MS) technology using a quantitative metabolomics approaches such as selected or multiple reaction monitoring (SRM or MRM). Recent developments in LC-MS/MRM based quantification of marker metabolites in newborns have opened a novel opportunity to screen multiple disorders simultaneously from a minuscule volume of biological fluids. In this review article, we have highlighted how LC-MS/MRM based metabolomics approach with its high sensitivity and diagnostic capability can make an impact on the nation's public health through NBS programs.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ansa.202100010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42233061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The compressed sensing (CS) theory requires the signal to be sparse under some transform. For most signals (e.g., speech and photos), the non-adaptive transform bases, such as discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT), can meet this requirement and perform quite well. However, one limitation of these non-adaptive transforms is that we cannot leverage domain-specific knowledge to improve CS efficiency. This study presents a task-adaptive eigenvector-based projection (EBP) transform. The EBP basis has an equivalent effect of the principal component loading matrix and can generate a sparse representation in the latent space. In a Raman spectroscopic profiling case study, EBP demonstrates better performance than its non-adaptive counterparts. At the 1% CS sampling ratio (k), the reconstruction relative mean square errors of DCT, DFT, WHT and EBP are 0.33, 0.68, 0.32, and 0.00, respectively. At a fixed k, EBP achieves much better reconstruction quality than the non-adaptive counterparts. For specific domain tasks, EBP can significantly lower the CS sampling ratio and reduce the overall measurement cost.
{"title":"Task-adaptive eigenvector-based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor","authors":"Yinsheng Zhang, Haiyan Wang, Yongbo Cheng, Xiaolin Qin","doi":"10.1002/ansa.202100018","DOIUrl":"10.1002/ansa.202100018","url":null,"abstract":"<p>The compressed sensing (CS) theory requires the signal to be sparse under some transform. For most signals (e.g., speech and photos), the non-adaptive transform bases, such as discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT), can meet this requirement and perform quite well. However, one limitation of these non-adaptive transforms is that we cannot leverage domain-specific knowledge to improve CS efficiency. This study presents a task-adaptive eigenvector-based projection (EBP) transform. The EBP basis has an equivalent effect of the principal component loading matrix and can generate a sparse representation in the latent space. In a Raman spectroscopic profiling case study, EBP demonstrates better performance than its non-adaptive counterparts. At the 1% CS sampling ratio (<i>k</i>), the reconstruction relative mean square errors of DCT, DFT, WHT and EBP are 0.33, 0.68, 0.32, and 0.00, respectively. At a fixed <i>k</i>, EBP achieves much better reconstruction quality than the non-adaptive counterparts. For specific domain tasks, EBP can significantly lower the CS sampling ratio and reduce the overall measurement cost.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/ansa.202100018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49429016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}