Search

This study investigates the potential and complementarity of high-throughput multipulse and multidimensional NMR methods for metabolomics. Through a chemical ecology case study, three methods are investigated, offering a continuum of methods with complementary features in terms of resolution, sensitivity and experiment time. Ultrafast 2D COSY, adiabatic INEPT and SYMAPS HSQC are shown to provide a very good classification ability, comparable to the reference 1D 1 H NMR method. Moreover, a detailed analysis of discriminant buckets upon supervised statistical analysis shows that all methods are highly complementary, since they are able to highlight discriminant signals that could not be detected by 1D 1 H NMR. In particular, fast 2D methods appear very efficient to discriminate signals located in highly crowded regions of the 1 H spectrum. Overall, the combination of these recent methods within a single NMR metabolomics workflow allows to maximize the accessible metabolic information, and also raises exciting challenges in terms of NMR data analysis for chemical ecology.

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers ( [Formula: see text]). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on recently introduced complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared to the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial in vivo data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of [Formula: see text] for younger mice, representing an improvement of 25.8%, while the resolution was improved by 13.9% for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.

Introduction: Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling., Objectives: An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision., Methods: A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool., Results: For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables., Conclusion: External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools., (© 2023. The Author(s).)

Vanillin, one of the world's most popular flavor used in food and pharmaceutical industries, is extracted from vanilla beans or obtained (bio)-synthetically. The price of natural vanillin is considerably higher than that of its synthetic alternative which leads increasingly to counterfeit vanillin. Here, we describe the workflow of combining carbon isotope ratio combustion mass spectrometry with quantitative carbon nuclear magnetic resonance spectrometry ( 13 C-qNMR) to obtain carbon isotope measurements traceable to the Vienna Peedee Belemnite (VPDB) with 0.7‰ combined standard uncertainty (or expanded uncertainty of 1.4‰ at 95% confidence level). We perform these measurements on qualified Bruker 400 MHz instruments to certify site-specific carbon isotope delta values in two vanillin materials, VANA-1 and VANB-1, believed to be the first intramolecular isotopic certified reference material (CRMs)., (© 2022. Crown.)

Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology., Competing Interests: SP, DM, AL, EM, KG, JF, SR, MN, LE No competing interests declared, TM Reviewing editor, eLife, (© 2021, Panigrahi et al.)

From a general public health perspective, a strategy combining non-targeted and targeted lipidomics MS-based approaches is proposed to identify disrupted patterns in serum lipidome upon growth promoter treatment in pigs. Evaluating the relative contributions of the platforms involved, the study aims at investigating the potential of innovative analytical approaches to highlight potential chemical food safety threats. Serum samples collected during an animal experiment involving control and treated pigs, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer™ platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The strategy enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation to cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of non-targeted and targeted MS approaches, various compartments of the pig serum lipidome could be explored, including commonly characterised lipids (Lipidyzer™), triglyceride isomers (Triglyceride platform) and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced coverage, level of characterization and applicability.

Across the evolutionary history of insects, the shift from nitrogen-rich carnivore/omnivore diets to nitrogen-poor herbivorous diets was made possible through symbiosis with microbes. The herbivorous turtle ants Cephalotes possess a conserved gut microbiome which enriches the nutrient composition by recycling nitrogen-rich metabolic waste to increase the production of amino acids. This enrichment is assumed to benefit the host, but we do not know to what extent. To gain insights into nitrogen assimilation in the ant cuticle we use gut bacterial manipulation, 15 N isotopic enrichment, isotope-ratio mass spectrometry, and 15 N nuclear magnetic resonance spectroscopy to demonstrate that gut bacteria contribute to the formation of proteins, catecholamine cross-linkers, and chitin in the cuticle. This study identifies the cuticular components which are nitrogen-enriched by gut bacteria, highlighting the role of symbionts in insect evolution, and provides a framework for understanding the nitrogen flow from nutrients through bacteria into the insect cuticle.

Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high precision and exceptional resolution of chemical information. Most NMR metabolomic studies are based on 1 H 1D spectroscopy, severely limited by peak overlap. 13 C NMR benefits from a larger signal dispersion but is barely used in metabolomics due to ca. 6000-fold lower sensitivity. We introduce a new approach, based on hyperpolarized 13 C NMR at natural abundance, that circumvents this limitation. A new untargeted NMR-based metabolomic workflow based on dissolution dynamic nuclear polarization (d-DNP) for the first time enabled hyperpolarized natural abundance 13 C metabolomics. Statistical analysis of resulting hyperpolarized 13 C data distinguishes two groups of plant (tomato) extracts and highlights biomarkers, in full agreement with previous results on the same biological model. We also optimize parameters of the semiautomated d-DNP system suitable for high-throughput studies.

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described., (© 2019 John Wiley & Sons, Ltd.)

Introduction: The use of 2D NMR data sources (COSY in this paper) allows to reach general metabolomics results which are at least as good as the results obtained with 1D NMR data, and this with a less advanced and less complex level of pre-processing. But a major issue still exists and can largely slow down a generalized use of 2D data sources in metabolomics: the experiment duration., Objective: The goal of this paper is to overcome the experiment duration issue in our recently published MIC strategy by considering faster 2D COSY acquisition techniques: a conventional COSY with a reduced number of transients and the use of the Non-Uniform Sampling (NUS) method. These faster alternatives are all submitted to novel 2D pre-processing workflows and to Metabolomic Informative Content analyses. Eventually, results are compared to those obtained with conventional COSY spectra., Methods: To pre-process the 2D data sources, the Global Peak List (GPL) workflow and the Vectorization workflow are used. To compare this data sources and to detect the more informative one(s), MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality., Results: Results are discussed according to a multi-factor experimental design (which is unsupervised and based on human urine samples). Descriptive PCA results and MIC indexes are shown, leading to the direct and objective comparison of the different data sets., Conclusion: In conclusion, it is demonstrated that conventional COSY spectra recorded with only one transient per increment and COSY spectra recorded with 50% of non-uniform sampling provide very similar MIC results as the initial COSY recorded with four transients, but in a much shorter time. Consequently, using techniques like the reduction of the number of transients or NUS can really open the door to a potential high-throughput use of 2D COSY spectra in metabolomics.

Introduction: The pre-processing of analytical data in metabolomics must be considered as a whole to allow the construction of a global and unique object for any further simultaneous data analysis or multivariate statistical modelling. For 1D 1 H-NMR metabolomics experiments, best practices for data pre-processing are well defined, but not yet for 2D experiments (for instance COSY in this paper)., Objective: By considering the added value of a second dimension, the objective is to propose two workflows dedicated to 2D NMR data handling and preparation (the Global Peak List and Vectorization approaches) and to compare them (with respect to each other and with 1D standards). This will allow to detect which methodology is the best in terms of amount of metabolomic content and to explore the advantages of the selected workflow in distinguishing among treatment groups and identifying relevant biomarkers. Therefore, this paper explores both the necessity of novel 2D pre-processing workflows, the evaluation of their quality and the evaluation of their performance in the subsequent determination of accurate (2D) biomarkers., Methods: To select the more informative data source, MIC (Metabolomic Informative Content) indexes are used, based on clustering and inertia measures of quality. Then, to highlight biomarkers or critical spectral zones, the PLS-DA model is used, along with more advanced sparse algorithms (sPLS and L-sOPLS)., Results: Results are discussed according to two different experimental designs (one which is unsupervised and based on human urine samples, and the other which is controlled and based on spiked serum media). MIC indexes are shown, leading to the choice of the more relevant workflow to use thereafter. Finally, biomarkers are provided for each case and the predictive power of each candidate model is assessed with cross-validated measures of RMSEP., Conclusion: In conclusion, it is shown that no solution can be universally the best in every case, but that 2D experiments allow to clearly find relevant cross peak biomarkers even with a poor initial separability between groups. The MIC measures linked with the candidate workflows (2D GPL, 2D vectorization, 1D, and with specific parameters) lead to visualize which data set must be used as a priority to more easily find biomarkers. The diversity of data sources, mainly 1D versus 2D, may often lead to complementary or confirmatory results.

Authentication of natural products is of major relevance in the context of manufactured drugs or herbal supplements since such active products generate a lucrative market. The analytical method to identify and quantify valuable natural products is critical for quality control and product assignment of herbal supplements. In this framework, we propose to apply a recently developed quantitative 2D NMR approach called Q QUIPU (Quick QUantItative Perfected and pUre shifted) in combination with 1D 1 H NMR capable to access the concentration of three major alkaloids, berberine, β-hydrastine and canadine, in the root extract of goldenseal (Hydrastis canadensis), one of the 20 most popular herbal supplements used worldwide. We highlight the complementarity of 1D and 2D quantitative NMR to accurately assess the amount of alkaloids with different range of concentrations and stability within extracts. In particular, unstable natural products having non-overlapped signals like berberine could only be quantified by sensitive and fast 1D 1 H, while overlapped signals of β-hydrastine and low intense ones of canadine could only be quantified with the recent 2D Q QUIPU HSQC. Results obtained from this combined approach have led to a good accuracy (<10%) as compared with coupled UHPLC-MS/UV techniques. This quantitative NMR approach paves the way to numerous applications where the accurate quantification of targeted compounds in complex mixtures is required, for instance in agricultural, food and pharmaceuticals products., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)

Nuclear magnetic resonance (NMR) spectroscopy exhibits a great potential for the quantitative analysis of complex biological samples such as those encountered in metabolomics. To overcome the ubiquitous problem of overlapping peaks in 1D NMR spectra of complex mixtures, acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. The experiment duration can be made compatible with high-throughput studies on large sample collections by relying on fast acquisition methods. Here, we describe the general metabolomics workflow to acquire fast quantitative 2D NMR data with a focus on targeted or untargeted analyses.

Background: It is questionable whether enrollment on clinical trials offers any survival advantage at the population level over standard-of-care treatment. The objectives of this study were to describe the impact of trial enrollment on event-free survival and overall survival in pediatric acute myeloid leukemia (AML) using the Cancer in Young People in Canada (CYP-C) database., Methods: Children were included if they had had AML newly diagnosed between ages birth and 14 years from 2001 to 2012. CYP-C is a national pediatric cancer population-based database that includes all cases of pediatric cancer diagnosed and treated at 1 of the 17 tertiary pediatric oncology centers in Canada. Univariate and Cox proportional hazards models were used to evaluate the impact of initial trial enrollment on survival., Results: In total, 397 eligible children with AML were included in the analysis, of whom 94 (23.7%) were enrolled on a clinical trial at initial diagnosis. The most common reason for non-enrollment was that no trial was available. The event-free survival rate at 5 years was 57.8% ± 5.2% for those enrolled versus 54.8% ± 2.9% for those not enrolled (P = .75). The overall survival rate at 5 years was 70.1% ± 4.9% for those enrolled versus 66.3% ± 2.8% for those not enrolled (P = .58). Enrollment on a trial was not associated with improved event-free or overall survival in multiple regression analyses., Conclusions: Enrollment on a clinical trial was not associated with improved survival for children with AML in a population-based cohort. Rationale for trial enrollment should not include the likelihood of benefit compared with non-enrollment., (© 2018 American Cancer Society.)

Dynamic control of cell polarity is of critical importance for many aspects of cellular development and motility. In Myxococcus xanthus, MglA, a G protein, and MglB, its cognate GTPase-activating protein, establish a polarity axis that defines the direction of movement of the cell and that can be rapidly inverted by the Frz chemosensory system. Although vital for collective cell behaviours, how Frz triggers this switch has remained unknown. Here, we use genetics, imaging and mathematical modelling to show that Frz controls polarity reversals via a gated relaxation oscillator. FrzX, which we identify as a target of the Frz kinase, provides the gating and thus acts as the trigger for reversals. Slow relocalization of the polarity protein RomR then creates a refractory period during which another switch cannot be triggered. A secondary Frz output, FrzZ, decreases this delay, allowing rapid reversals when required. Thus, this architecture results in a highly tuneable switch that allows a wide range of reversal frequencies.

Introduction: Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability., Objectives: The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization., Method: The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes-including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes-are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs., Results: Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework., Conclusion: This work demonstrates the potential of fast multidimensional 1 H NMR-suited with an appropriate sample preparation-for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.

Background: The objectives of this study were to describe the impact of trial enrollment at diagnosis on event-free and overall survival in paediatric acute lymphoblastic leukaemic (ALL) using a population-based approach., Methods: We conducted a retrospective cohort study that included children newly diagnosed with ALL between 1 and 14 years of age. The data source was the Cancer in Young People in Canada (CYP-C) national paediatric cancer population-based database. We conducted univariate and multiple Cox proportional hazards models., Results: There were 2569 children with ALL; 1408 (54.8%) were enrolled on a clinical trial at initial diagnosis. Event-free survival at 5 years was 89.8%±0.9 vs 84.1%±1.2. (P<0.0001) for those enrolled and not enrolled on a clinical trial, respectively. Overall survival at 5 years was higher for those enrolled (94.1%±0.7) vs not enrolled (90.5%±1.0; P=0.001). In a model that adjusted for demographic, leukaemic and socioeconomic factors, enrollment on trials was significantly associated with better event-free survival (hazard ratio (HR) 0.67, 95% confidence interval (CI) 0.47-0.95; P=0.023), but not overall survival (HR 0.69, 95% CI 0.44-1.08; P=0.102)., Conclusions: Event-free survival was significantly better in children with ALL enrolled on a clinical trial. Future research should identify barriers to clinical trial enrollment for children with ALL.

The targeted analysis of metabolites in complex mixtures is a challenging issue. NMR is one of the major tools in this field, but there is a strong need for more sensitive, better-resolved, and faster quantitative methods. In this framework, we introduce the concept of FAst, QUantitative, hIghly Resolved and sEnsitivity enhanced (FAQUIRE) NMR to push forward the limits of metabolite NMR analysis. 2D 1 H, 13 C 2D quantitative maps are promising alternatives for enhancing the spectral resolution but are highly time-consuming because of (i) the intrinsic nature of 2D, (ii) the longer recycling times required for quantitative conditions, and (iii) the higher number of scans needed to reduce the level of detection/quantification to access low concentrated metabolites. To reach this aim, speeding up the recently developed QUantItative Perfected and pUre shifted HSQC (QUIPU HSQC) is an interesting attempt to develop the FAQUIRE concept. Thanks to the combination of spectral aliasing, nonuniform sampling, and variable repetition time, the acquisition time of 2D quantitative maps is reduced by a factor 6 to 9, while conserving a high spectral resolution thanks to a pure shift approach. The analytical potential of the new Quick QUIPU HSQC (Q QUIPU HSQC) is evaluated on a model metabolite sample, and its potential is shown on breast-cell extracts embedding metabolites at millimolar to submillimolar concentrations.

The FrzCD chemoreceptor from the gliding bacterium Myxococcus xanthus forms cytoplasmic clusters that occupy a large central region of the cell body also occupied by the nucleoid. In this work, we show that FrzCD directly binds to the nucleoid with its N-terminal positively charged tail and recruits active signaling complexes at this location. The FrzCD binding to the nucleoid occur in a DNA-sequence independent manner and leads to the formation of multiple distributed clusters that explore constrained areas. This organization might be required for cooperative interactions between clustered receptors as observed in membrane-bound chemosensory arrays.

Background: Primary objective was to describe the proportion of children newly diagnosed with cancer enrolled on a therapeutic clinical trial. Secondary objectives were to describe reasons for non-enrollment and factors associated with enrollment on trials., Methods: In this retrospective cohort study, we included children newly diagnosed with cancer between 0 and 14 years of age and diagnosed from 2001 to 2012. We used data from the Cancer in Young People in Canada (CYP-C) national pediatric cancer population-based database. CYP-C captures all cases of pediatric cancer (0-14 years) diagnosed and treated at one of the 17 tertiary pediatric oncology centers in Canada. Non-enrollment was evaluated using univariate and multiple logistic regression analysis., Results: There were 9204 children with cancer included, of whom 2533 (27.5%) were enrolled on a clinical trial. The most common reasons cited for non-enrollment were lack of an available trial (52.2%) and physician choice (11.2%). In multiple regression, Asian and Arab/west Asian race were associated with lower enrollment (P = 0.006 and P = 0.032 respectively). All cancer diagnoses were more likely to be enrolled compared to astrocytoma and children with acute lymphoblastic leukemia had an almost 18-fold increased odds of enrollment compared to astrocytoma (P < 0.0001). Greater distance from the tertiary care center was independently associated with non-enrollment (P < 0.0001)., Conclusions: In Canada, 27.5% of children with cancer are enrolled onto therapeutic clinical trials and lack of an available trial is the most common reason contributing to non-enrollment. Future research should better understand reasons for lack of trial availability and physician preferences to not offer trials.

Multi-dimensional NMR is an appealing approach for dealing with the challenging complexity of biological samples in metabolomics. This article describes how spectroscopists have recently challenged their imagination in order to make 2D NMR a powerful tool for quantitative metabolomics, based on innovative pulse sequences combined with meticulous analytical chemistry approaches. Clever time-saving strategies have also been explored to make 2D NMR a high-throughput tool for metabolomics, relying on alternative data acquisition schemes such as ultrafast NMR. Currently, much work is aimed at drastically boosting the NMR sensitivity thanks to hyperpolarisation techniques, which have been used in combination with fast acquisition methods and could greatly expand the application potential of NMR metabolomics., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Breast cancer is the most common cancer in women worldwide. Despite the information provided by anatomopathological assessment and molecular markers (such as receptor expression ER, PR, HER2), breast cancer therapies and prognostics depend on the metabolic properties of tumor cells. However, metabolomics have not provided a robust and congruent biomarker yet, likely because individual metabolite contents are insufficient to encapsulate all of the alterations in metabolic fluxes. Here, we took advantage of natural 13 C and 15 N isotope abundance to show there are isotopic differences between healthy and cancer biopsy tissues or between healthy and malignant cultured cell lines. Isotope mass balance further suggests that these differences are mostly related to lipid metabolism, anaplerosis and urea cycle, three pathways known to be impacted in malignant cells. Our results demonstrate that the isotope signature is a good descriptor of metabolism since it integrates modifications in C partitioning and N excretion altogether. Our present study is thus a starting point to possible clinical applications such as patient screening and biopsy characterization in every cancer that is associated with metabolic changes.

The objective of this study was to develop a technique for carrying out repeated biopsies of the mammary gland of lactating dairy cows that provides enough material to monitor enzyme activities and gene expression in mammary secretory tissue. A total of 16 Holstein cows were subjected to 4 mammary biopsies each at 3-week intervals for a total of 64 biopsies. A 0.75-cm incision was made through the skin and subcutaneous tissue of the mammary gland and a trocar and cannula were inserted using a circular motion. The trocar was withdrawn and a syringe was plugged into the base of the cannula to create a vacuum for sampling mammary tissue. To reduce bleeding, hand pressure was put on the surgery site after biopsy and skin closure and ice was applied for at least 2 h after the biopsy using a cow bra. The entire procedure took an average of 25 min. Two attempts were usually enough to obtain 800 mg of tissue. Visual examination of milk samples 10 d after the biopsy indicated no trace of blood, except in samples from 2 cows. All wounds healed without infection and subcutaneous hematomas resorbed within 7 d. There was no incidence of mastitis throughout the lactation. This technique provides a new tool for biopsy of the mammary gland repeated at short intervals with the main effect being a decrease in milk production. Although secondary complications leading to illness or death are always a risk with any procedure, this biopsy technique was carried out without complications to the health of animals and with no incidence of mastitis during the lactation.

Functional overexpression of polytopic membrane proteins, particularly when in a foreign host, is often a challenging task. Factors that negatively affect such processes are poorly understood. Using the mammalian membrane protein vitamin K epoxide reductase (VKORc1) as a reporter, we describe a genetic selection approach allowing the isolation of Escherichia coli mutants capable of functionally expressing this blood-coagulation enzyme. The isolated mutants map to components of membrane protein assembly and quality control proteins YidC and HslV. We show that changes in the VKORc1 sequence and in the YidC hydrophilic groove along with the inactivation of HslV promote VKORc1 activity and dramatically increase its expression level. We hypothesize that such changes correct for mismatches in the membrane topogenic signals between E. coli and eukaryotic cells guiding proper membrane integration. Furthermore, the obtained mutants allow the study of VKORc1 reaction mechanisms, inhibition by warfarin, and the high-throughput screening for potential anticoagulants.

The natural xanthines caffeine, theobromine, and theophylline are of major commercial importance as flavor constituents in coffee, cocoa, tea, and a number of other beverages. However, their exploitation for authenticity, a requirement in these commodities that have a large origin-based price-range, by the standard method of isotope ratio monitoring by mass spectrometry (irm-MS) is limited. We have now developed a methodology that overcomes this deficit that exploits the power of isotopic quantitative (13)C nuclear magnetic resonance (NMR) spectrometry combined with chemical modification of the xanthines to enable the determination of positional intramolecular (13)C/(12)C ratios (δ(13)Ci) with high precision. However, only caffeine is amenable to analysis: theobromine and theophylline present substantial difficulties due to their poor solubility. However, their N-methylation to caffeine makes spectral acquisition feasible. The method is confirmed as robust, with good repeatability of the δ(13)Ci values in caffeine appropriate for isotope fractionation measurements at natural abundance. It is shown that there is negligible isotope fractionation during the chemical N-methylation procedure. Thus, the method preserves the original positional δ(13)Ci values. The method has been applied to measure the position-specific variation of the (13)C/(12)C distribution in caffeine. Not only is a clear difference between caffeine isolated from different sources observed, but theobromine from cocoa is found to show a (13)C pattern distinct from that of caffeine.

Quantitative 2D NMR is a powerful analytical tool which is widely used to determine the concentration of small molecules in complex samples. Due to the site-specific response of the 2D NMR signal, the determination of absolute concentrations requires the use of a calibration or standard addition approach, where the analyte acts as its own reference. Standard addition methods, where the targeted sample is gradually spiked with known amounts of the targeted analyte, are particularly well-suited for quantitative 2D NMR of small molecules. This paper explores the potential of such quantitative 2D NMR approaches for the quantitative analysis of a high molecular weight polysaccharide. The results highlight that the standard addition method leads to a strong under-estimation of the target concentration, whatever the 2D NMR pulse sequence. Diffusion measurements show that a change in the macromolecular organization of the studied polysaccharide is the most probable hypothesis to explain the non-linear evolution of the 2D NMR signal with concentration. In spite of this non-linearity--the detailed explanation of which is out of the scope of this paper--we demonstrate that accurate quantitative results can still be obtained provided that an external calibration is performed with a wide range of concentrations surrounding the target value. This study opens the way to a number of studies where 2D NMR is needed for the quantitative analysis of macromolecules., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Quantitative analysis by nuclear magnetic resonance (NMR) requires highly precise measurements to achieve reliable quantification. It is particularly true in (13)C site-specific natural isotope fractionation studied by nuclear magnetic resonance, where the range of values of (13)C isotopic deviations at natural abundance is highly restricted. Consequently, an NMR method capable of measuring δ(13)C ‰ values with a very high precision (a few per mil) is indispensable. This high degree of precision has already been achieved by one-dimensional (13)C acquisitions; however, this approach is limited by peak overlaps which reduce the precision of the isotope content determination, even for certain small molecules. It is therefore necessary to extend this promising methodology to a higher dimensionality. In this context, this paper aims at determining conditions that allow the achievement of two-dimensional (2D) (1)H-(13)C heteronuclear experiments with a precision of a few per mil in a reasonable time. Our results demonstrate that a high precision (repeatability of 2 per mil) can be reached with the (1)H-(13)C HSQC (Heteronuclear Single Quantum Correlation) experiment, thus satisfying the conditions needed to perform (13)C isotope analysis by 2D NMR. We also consider the impact of several approaches which have been proposed to reduce the duration of heteronuclear 2D experiments. Two of these common time-saving strategies, spectral aliasing and linear prediction, are fully compatible with the high-precision requirements of isotopic NMR, while a third one, nonuniform sampling, leads to dramatic precision losses. In conclusion, this study demonstrates the feasibility of very precise 2D NMR measurements and opens a number of application perspectives.

Two-dimensional nuclear magnetic resonance (2D NMR) forms a powerful tool for the quantitative analysis of complex mixtures such as samples of metabolic relevance. However, its use for quantitative purposes is far from being trivial, not only because of the associated experiment time, but also due to its subsequent high sensitivity to hardware instabilities affecting its precision. In this paper, an alternative approach is considered to measure absolute metabolite concentrations in complex mixtures with a high precision in a reasonable time. It is based on a "multi-scan single shot" (M3S) strategy, which is derived from the ultrafast 2D NMR methodology. First, the analytical performance of this methodology is compared to the one of conventional 2D NMR. 2D correlation spectroscopy (COSY) spectra are obtained in 10 min on model metabolic mixtures, with a precision in the 1-4% range (versus 5-18% for the conventional approach). The M3S approach also shows a better linearity than its conventional counterpart. It ensures that accurate quantitative results can be obtained provided that a calibration procedure is carried out. The M3S COSY approach is then applied to measure the absolute metabolite concentration in three breast cancer cell line extracts, relying on a standard addition protocol. M3S COSY spectra of such extracts are recorded in 20 min and give access to the absolute concentration of 14 major metabolites, showing significant differences between cell lines.

Metabolomic studies by NMR spectroscopy are increasingly employed for a variety of biomedical applications. A very standardized 1D proton NMR protocol is generally employed for data acquisition, associated with multivariate statistical tests. Even if targeted approaches have been proposed to quantify metabolites from such experiments, quantification is often made difficult by the high degree of overlap characterizing (1) H NMR spectra of biological samples. Two-dimensional spectroscopy presents a high potential for accurately measuring concentrations in complex samples, as it offers a much higher discrimination between metabolite resonances. We have recently proposed an original approach relying on the (1) H 2D INADEQUATE pulse sequence, optimized for fast quantitative analysis of complex metabolic mixtures. Here, the first application of the quantitative (1) H 2D INADEQUATE experiment to a real metabonomic study is presented. Absolute metabolite concentrations are determined for different breast cancer cell line extracts, by a standard addition procedure. The protocol is characterized by high analytical performances (accuracy better than 1%, excellent linearity), even if it is affected by relatively long acquisition durations (15 min to 1 h per spectrum). It is applied to three different cell lines, expressing different hormonal and tyrosine kinase receptors. The absolute concentrations of 15 metabolites are determined, revealing significant differences between cell lines. The metabolite concentrations measured are in good agreement with previous studies regarding metabolic profile changes of breast cancer. While providing a high degree of discrimination, this methodology offers a powerful tool for the determination of relevant biomarkers., (Copyright © 2012 John Wiley & Sons, Ltd.)

A large series of similar non-covalent complexes were probed using ion mobility spectrometry, molecular mechanics/molecular dynamics (MM/MD), electrospray-tandem mass spectrometry (ESI-MS/MS) and RRKM theory in order to determine the effects of charge state and charge location upon the conformation, the 0 K activation energy (E(0)) and the entropy of activation (ΔS(‡)) of the dissociation of these complexes. The non-covalent complexes consisted of poly(methylmethacrylate) oligomers and singly and doubly charged diaminoalkanes of varying length. This allowed for control of the charge separation within the complexes, as well as the size of the complex. A destabilizing effect was observed in complexes containing protons in close proximity, and/or short oligomers. Interestingly, a multiple charge stabilizing effect was observed when charge sites were sufficiently separated and/or when the polymer moiety of the complex was large. ΔS(‡) values of doubly charged complexes showed a greater increase with increasing polymer size in comparison to singly charged complexes. This entropic observation is explained by structure, where IMS and MM/MD determined that the charge location was the determining factor of the overall conformation of these complexes and multiple charging resulted in more rigid arrangements. Dissociation of a tightly bound complex is more entropically favorable than a loosely bound complex. Also presented is a MM/MD refinement regime derived from IMS measurements.

Metabolomic analysis of mammalian cells can be applied across multiple fields including medicine and toxicology. It requires the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Particular attention must be paid to the efficiency and reliability of the extraction procedure. Even though a number of recent studies have dealt with optimizing a particular protocol for specific matrices and analytical techniques, there is no universal method to allow the detection of the entire cellular metabolome. Here, we present a strategy for choosing extraction procedures from adherent mammalian cells for the global NMR analysis of the metabolome. After the quenching of cells, intracellular metabolites are extracted from the cells using one of the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol, methanol/water, and methanol/chloroform/water. The hydrophilic metabolite profiles are analysed using (1)H nuclear magnetic resonance (NMR) spectroscopy. We propose an original geometric representation of metabolites reflecting the efficiency of extraction methods. In the case of NMR-based analysis of mammalian cells, this methodology demonstrates that a higher portion of intracellular metabolites are extracted by using methanol or methanol/chloroform/water. The preferred method is evaluated in terms of biological variability for studying metabolic changes caused by the phenotype of four different human breast cancer cell lines, showing that the selected extraction procedure is a promising tool for metabolomic and metabonomic studies of mammalian cells. The strategy proposed in this paper to compare extraction procedures is applicable to NMR-based metabolomic studies of various systems., (© Springer-Verlag 2011)

The gas-phase binding of small molecules to the Amyloid β-40 peptide generated by electrospray ionization has been explored with collision-induced dissociation mass spectrometry and kinetic rate theory. This study discusses a simple procedure used to theoretically model the experimental breakdown diagrams for the Aβ-40 peptide complexed with a series of aminosulfonate small molecules, namely homotaurine, 3-cyclohexylamino-2-hydroxy-1-propanesulfonic acid (CAPSO), 3-(1,3,4,9-tetrahydro-2H-β-carbolin-2-yl)propane-1-sulfonic acid, 3-(1,3,4,9-tetrahydro-2H-β-carbolin-2-yl)butane-1-sulfonic acid, and 3-(cyclohexylamino)propane-1-sulfonic acid. An alternative procedure employing an extrapolation procedure for k(E) is also discussed.

Quantitative analysis of metabolic mixtures by (1)H 1D NMR offers a limited potential for precise quantification of biomarkers, due to strong overlap between the peaks. Two-dimensional spectroscopy is a powerful tool to unambiguously and simultaneously measure a larger number of metabolite contributions. However, it is still rarely used for quantification, first because quantitative analysis by 2D NMR requires a calibration procedure due to the multi-impulsional nature of 2D NMR experiments, and above all because of the prohibitive experiment duration that is necessary to obtain such a calibration curve. In this work, we develop and evaluate a 2D (1)H INADEQUATE protocol for a fast determination of metabolite concentrations in complex mixtures. The 2D pulse sequence is carefully optimized and evaluated in terms of precision and linearity. Quantitative (1)H INADEQUATE 2D spectra of metabolic mixtures are obtained in 7 min with a repeatability better than 2% for metabolite concentrations as small as 100 μM and an excellent linearity. The method described in this work allows a fast and precise quantification of metabolic mixtures, and it forms a promising tool for metabonomic studies., (Copyright © 2010 Elsevier B.V. All rights reserved.)

This paper describes an efficient and reproducible screening method for identifying low molecular weight compounds that bind to amyloid beta peptides (Abeta) peptides using electrospray ionization mass spectrometry (ESI-MS). Low molecular weight compounds capable of interacting with soluble Abeta may be able to modulate/inhibit the Abeta aggregation process and serve as potential disease-modifying agents for AD. The present approach was used to rank the binding affinity of a library of compounds to Abeta1-40 peptide. The results obtained show that low molecular weight compounds bind similarly to Abeta1-42, Abeta1-40, as well as Abeta1-28 peptides and they underline the critical role of Abeta peptide charge motif in binding at physiological pH. Finally, some elements of structure-activity relationship (SAR) involved in the binding affinity of homotaurine to soluble Abeta peptides are discussed., (2010. Published by Elsevier Inc.)

Monte Carlo (MC) methods play an important role in simulations of protein folding. These methods rely on a random sampling of moves on a potential energy surface. To improve the efficiency of the sampling, we propose a new selection of trial moves based on an empirical distribution of three-residue (triplet) conformations. This selection is compared to random combinations of the preferred conformations of the three amino acids, and it is shown that the new trial moves lead to finding structures closer to the native conformation., (Copyright 2004 Wiley Periodicals, Inc.)