<Superscript>1</Superscript>H NMR based metabolomic approach to monitoring of the head and neck cancer treatment toxicity

Introduction

Anticancer treatment results in temporary or permanent toxicity considered as changes in normal tissues and/or involved regions. The net effect is mirrored in morphological, functional and molecular disturbances—thus in a systemic response of the human body. To date, specific NMR biomarkers of radiation therapy toxicity in head and neck squamous cell carcinoma (HNSCC) patients are scarce or even missing.

Objectives

We aimed to investigate molecular processes reflecting acute radiation sequelae (ARS) in HNSCC patients using NMR-based metabolomics of blood serum.

Methods

45 patients with HNSCC were treated with radiotherapy (RT) or chemoradiotherapy (CHRT). Blood samples were collected within a week after RT/CHRT completion. Patients were divided into two classes (of high and low ARS) on the basis of the highest individual ARS value observed during the treatment. ¹H NMR spectra of serum samples were acquired on a Bruker 400.13 MHz spectrometer at 310 K and analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. Additional statistical analyses were performed on quantified metabolites.

Results

1D projections of the J-resolved NMR spectra seem to be of the great potential in the quest for the HNSCC treatment toxicity biomarker. The metabolic features characteristic for high ARS are the increased signals of N-acetyl-glycoprotein and acetate, as well as decrease of choline and the metabolites involved in energy metabolism: branched chain amino acids (BCAAs), alanine, creatinine and carnitine. Furthermore, we observed significant correlations between N-acetyl-glycoprotein and clinical markers of inflammation as well as acetate and a percentage-weight-loss during the treatment. CRP was also negatively correlated with alanine and BCAAs.

Conclusion

NMR-based metabolomics provides relevant biomarkers of RT/CHRT toxicity (ARS) in HNSCC patients. The results indicate at least three concomitant processes related to high ARS: inflammation, altered energy metabolism and disturbed membrane metabolism, and indicate an exciting potential of J-resolved NMR spectroscopy combined with multivariate projection techniques.

     

A fast NMR method for resonance assignments: application to metabolomics

We present a new method for rapid NMR data acquisition and assignments applicable to unlabeled (¹²C) or ¹³C-labeled biomolecules/organic molecules in general and metabolomics in particular. The method involves the acquisition of three two dimensional (2D) NMR spectra simultaneously using a dual receiver system. The three spectra, namely: (1) G-matrix Fourier transform (GFT) (3,2)D [¹³C, ¹H] HSQC–TOCSY, (2) 2D ¹H–¹H TOCSY and (3) 2D ¹³C–¹H HETCOR are acquired in a single experiment and provide mutually complementary information to completely assign individual metabolites in a mixture. The GFT (3,2)D [¹³C, ¹H] HSQC–TOCSY provides 3D correlations in a reduced dimensionality manner facilitating high resolution and unambiguous assignments. The experiments were applied for complete ¹H and ¹³C assignments of a mixture of 21 unlabeled metabolites corresponding to a medium used in assisted reproductive technology. Taken together, the experiments provide time gain of order of magnitudes compared to the conventional data acquisition methods and can be combined with other fast NMR techniques such as non-uniform sampling and covariance spectroscopy. This provides new avenues for using multiple receivers and projection NMR techniques for high-throughput approaches in metabolomics.     

Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Background

Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in ¹H NMR spectra has previously been successfully employed. Similar correlation of 2D ¹H-¹³C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results

From 50 ¹H-¹³C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions

Correlation plots prepared by statistically correlating ¹H-¹³C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0413-z) contains supplementary material, which is available to authorized users.     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.

     

生态代谢组学研究进展

代谢组学指某一生物系统中产生的或已存在的代谢物组的研究,以质谱和核磁共振技术为分析平台,以信息建模与系统整合为目标。随着代谢组学中的研究方法与技术成为生态学研究的有力工具,生态代谢组学概念应运而生,即研究某一个生物体对环境变化的代谢物组水平的响应。理清代谢组学与生态代谢组学学科发展的脉络,综述代谢组学研究中的常用技术及其优势与局限性,论述代谢组学技术在生态学研究中的应用现状,展望代谢组学技术与其他系统生物学组学技术的结合在生态学中的应用前景,提出生态代谢组学研究者未来要完成的任务和面对的挑战。     

Structural analysis and properties of dextran produced by Leuconostoc mesenteroides NRRL B-640

A water-soluble dextran was produced by purified dextransucrase from Leuconostoc mesenteroides NRRL B-640. The dextran was purified by alcohol precipitation. The structure of dextran was determined by FT-IR, ¹H NMR, ¹³C NMR and 2-dimensional NMR spectroscopic techniques. NMR techniques (1D ¹H, ¹³C and 2D HMQC) were used to fully assign the ¹H and ¹³C spectra. All the spectral data showed that the dextran contains d-glucose residues in a linear chain with consecutive α(1 → 6) linkages. No branching was observed in the dextran structure. The viscosity of dextran solution decreased with the increase in shear rate exhibiting a typical non-Newtonian pseudoplastic behavior. The surface morphology of dried and powdered dextran studied using Scanning electron microscopy revealed the cubical porous structure.     

Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics

Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method. C. Y. Lin and H. Wu contributed equally.     

Improved methods for the acquisition and interpretation of NMR metabolomic data

The high spectral congestion typically observed in one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectra of tissue extracts and biofluids limits the metabolic information that can be extracted. This study evaluates the application of two-dimensional J-resolved (JRES) spectroscopy for metabolomics, which can provide proton-decoupled projected 1D spectra (p-JRES). This approach is illustrated by an investigation of embryogenesis in Japanese medaka (Oryzias latipes), an established fish model for developmental toxicology. When combined with optimized spectral pre-processing,(2) including a 0.005-ppm bin width for data segmentation and a logarithmic transformation, the reduced congestion in the p-JRES spectra increases the likelihood that a specific metabolite can be accurately integrated and thus increases the extractable information content of the spectra. Principal components analysis of the p-JRES spectra reveals the concept of a developmental trajectory that summarizes the changes in the NMR-visible metabolome throughout medaka embryogenesis. Advantages and potential disadvantages of the p-JRES approach are discussed.     

High-Resolution 1H NMR Spectroscopy of Fish Muscle,Eggs and Small Whole Fish via Hadamard-Encoded Intermolecular Multiple-Quantum Coherence

Background and Purpose

Nuclear magnetic resonance (NMR) spectroscopy has become an important technique for tissue studies. Since tissues are in semisolid-state, their high-resolution (HR) spectra cannot be obtained by conventional NMR spectroscopy. Because of this restriction, extraction and high-resolution magic angle spinning (HR MAS) are widely applied for HR NMR spectra of tissues. However, both of the methods are subject to limitations. In this study, the feasibility of HR ¹H NMR spectroscopy based on intermolecular multiple-quantum coherence (iMQC) technique is explored using fish muscle, fish eggs, and a whole fish as examples.

Materials and Methods

Intact salmon muscle tissues, intact eggs from shishamo smelt and a whole fish (Siamese algae eater) are studied by using conventional 1D one-pulse sequence, Hadamard-encoded iMQC sequence, and HR MAS.

Results

When we use the conventional 1D one-pulse sequence, hardly any useful spectral information can be obtained due to the severe field inhomogeneity. By contrast, HR NMR spectra can be obtained in a short period of time by using the Hadamard-encoded iMQC method without shimming. Most signals from fatty acids and small metabolites can be observed. Compared to HR MAS, the iMQC method is non-invasive, but the resolution and the sensitivity of resulting spectra are not as high as those of HR MAS spectra.

Conclusion

Due to the immunity to field inhomogeneity, the iMQC technique can be a proper supplement to HR MAS, and it provides an alternative for the investigation in cases with field distortions and with samples unsuitable for spinning. The acquisition time of the proposed method is greatly reduced by introduction of the Hadamard-encoded technique, in comparison with that of conventional iMQC method.     

Selective suppression of excipient signals in 2D <Superscript>1</Superscript>H–<Superscript>13</Superscript>C methyl spectra of biopharmaceutical products

While the use of ¹H–¹³C methyl correlated NMR spectroscopy at natural isotopic abundance has been demonstrated as feasible on protein therapeutics as large as monoclonal antibodies, spectral interference from aliphatic excipients remains a significant obstacle to its widespread application. These signals can cause large baseline artifacts, obscure protein resonances, and cause dynamic range suppression of weak peaks in non-uniform sampling applications, thus hampering both traditional peak-based spectral analyses as well as emerging chemometric methods of analysis. Here we detail modifications to the 2D ¹H–¹³C gradient-selected HSQC experiment that make use of selective pulsing techniques for targeted removal of interfering excipient signals in spectra of the NISTmAb prepared in several different formulations. This approach is demonstrated to selectively reduce interfering excipient signals while still yielding 2D spectra with only modest losses in protein signal. Furthermore, it is shown that spectral modeling based on the SMILE algorithm can be used to simulate and subtract any residual excipient signals and their attendant artifacts from the resulting 2D NMR spectra.     

Application of 1D and 2D NMR techniques to the structure elucidation of the O-polysaccharide from Proteus mirabilis O: 57

Summary The LPS O-polysaccharide (O-PS) produced by Proteus mirabilis serotype O: 57 (ATCC 49995) was shown by NMR spectroscopy and chemical analysis to be a high-molecular-weight acidic branched polymer of pentasaccharide repeating units, composed of d-glucose, d-galactose, 2-acetamido-2-deoxy-d-galactose and glycerophosphate residues (1:2:2:2:1). Application of one-and two-dimensional NMR methods allowed the complete assignment of notoriously crowded ¹H and ¹³C spectra of the O-PS, leading to the determination of its structure. Several of the NMR techniques used were applied for the first time to the structure elucidation of polysaccharides. The resonances of galactose H5, H6 and H6 were identified by a 1D analog of 3D NOESY-TOCSY and 2D {¹H, ¹H} triple-quantum experiments. The position and the nature of the phosphate group were determined from 2D ³¹P (₁)-half-filtered COSY and 2D ³¹P-relayed COSY spectra. 2D HMQC-TOCSY and 2D single-quantum proton-carbon long-range correlation techniques were used to overcome the difficulties of severe overlap and higher order effects in the ¹H NMR spectrum of the O-PS. The latter technique, together with 2D NOESY, enabled us to identify the substitution positions, the anomeric configurations and the sequence of the component glycose residues in the O-PS.     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

A high-resolution 1H NMR approach for structure determination of membrane peptides and proteins in non-deuterated detergent: Application to mastoparan X solubilized in n-octylglucoside

Summary Application of ¹H 2D NMR methods to solubilized membrane proteins and peptides has up to now required the use of selectively deuterated detergents. The unavailability of any of the common biochemical detergents in deuterated form has therefore limited to some extent the scope of this approach. Here a ¹H NMR method is described which allows structure determination of membrane peptides and small membrane proteins by ¹H 2D NMR in any type of non-deuterated detergent. The approach is based on regioselective excitation of protein resonances with DANTE-Z or spin-pinging pulse trains. It is shown that regioselective excitation of the amide-aromatic region of solubilized membrane proteins and peptides leads to an almost complete suppression of the two orders of magnitude higher contribution of the protonated detergent to the ¹H NMR spectrum. Consistently TOCSY, COSY and NOESY sequences incorporating such regioselective excitation in the F2 dimension yield protein ¹H 2D NMR spectra of quality comparable to those obtained in deuterated detergents. Regioselective TOCSY and NOESY spectra display all through-bond and through-space correlations within amide-aromatic protons and between these protons and aliphatic and -protons. Regioselective COSY spectra provide scalar coupling constants between amide and -protons. Application of the method to the membrane-active peptide mastoparan X, solubilized in n-octylglucoside, yields complete sequence-specific assignments and extensive secondary structure-related spatial proximities and coupling constants. It is shown that mastoparan adopts an -helical conformation when bound to nonionic detergent micelles. The present method is expected to increase the applicability of ¹H solution NMR methods to membrane proteins and peptides.Abbreviations 2D NMR two-dimensional NMR - COSY correlated spectroscopy - DANTE delays alternating nutations for tailored excitation - NOESY nuclear Overhauser enhancement spectroscopy - TOCSY total correlation spectroscopy     

NMR-derived developmental metabolic trajectories: an approach for visualizing the toxic actions of trichloroethylene during embryogenesis

Fish embryo toxicity tests for chemical risk assessment have traditionally been based upon non-specific endpoints including morphological abnormalities, hatching success, and mortality. Here we extend the application of ¹H NMR-based metabolomics in environmental toxicology by adding a suite of metabolic endpoints to the Japanese medaka (Oryzias latipes) embryo assay, with the goal to provide more sensitive, specific and unbiased biomarkers of toxicity. Medaka were exposed throughout embryogenesis to five concentrations of trichloroethylene (TCE; 0, 8.76, 21.9, 43.8, 87.6, 175 mg/L) and the relative sensitivities of the traditional and metabolomic endpoints compared. While the no-observable-adverse-effect-level for hatching success, the most sensitive traditional indicator, was 164 mg/L TCE, metabolic perturbations were detected at all exposure concentrations. Principal components analysis (PCA) highlighted a dose-response relationship between the NMR spectra of medaka extracts. In addition, 12 metabolites that exhibited highly significant dose-response relationships were identified, which indicated an energetic cost to TCE exposure. Next, embryos were exposed to 0, 0.88, 8.76 mg/L TCE and sampled on each of the 8 days of development. Projections of 66 two-dimensional J-resolved NMR spectra were obtained, and PCA revealed developmental metabolic trajectories that characterized the basal and TCE-perturbed changes in the entire NMR-visible metabolome throughout embryogenesis. Although no significant increases in mortality, gross deformity or developmental retardation were observed relative to the control group, TCE-induced metabolic perturbations were observed on day 8. In conclusion, these results support the continued development of NMR-based metabolomics as a rapid and reproducible tool for biomarker discovery and environmental risk assessment.     

Peak fitting in 2D <Superscript>1</Superscript>H–<Superscript>13</Superscript>C HSQC NMR spectra for metabolomic studies

A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) ¹H–¹³C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that ¹H–¹³C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the ¹H–¹³C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of ¹³C and ¹H chemical shift correlations from known metabolites.     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection

In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [¹H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [¹H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.     

Neuraminidase Inhibitors from marine‐derived actinomycete Streptomyces seoulensis

Aims

This work was performed to characterize new secondary metabolites with neuraminidase (NA) inhibitory activity from marine actinomycete strains.

Methods and Results

An actinomycete strain IFB‐A01, capable of producing new NA inhibitors, was isolated from the gut of shrimp Penasus orientalis and identified as Streptomyces seoulensis according to its 16S rRNA sequence (over 99% homology with that of the standard strain). Repeated chromatography of the methanol extract of the solid‐substrate culture of S. seoulensis IFB‐A01 led to the isolation of streptoseolactone ( 1 ), and limazepines G ( 2 ) and H ( 3 ). The structures of 1 – 3 were determined by a combination of IR, ESI‐MS, 1D (¹H and ¹³C NMR, and DEPT) and 2D NMR experiments (HMQC, HMBC, ¹H‐¹H COSY and NOESY). Compounds 1 – 3 showed significant inhibition on NA in a dose‐dependent manner with IC₅₀ values of 3·92, 7·50 and 7·37 μmol l^?1, respectively.

Conclusions

This is the first report of two new ( 1 and 2 ) and known ( 3 , recovered as a natural product for the first time in the work) NA inhibitors from the marine‐derived actinomycete S. seoulensis IFB‐A01.

Significance and Impact of the Study

The three natural NA inhibitors maybe of value for the development of drug(s) necessitated for the treatment of viral infections.