Discovery of anti-Mycobacterium tuberculosis desertomycins from Streptomyces flavofungini TRM90047 based on genome mining and HSQC-TOCSY.

Tuberculosis caused by Mycobacterium tuberculosis (M. tb) is a major public health problem with high morbidity and mortality worldwide. In our previous study, we found that a fermentation product of Streptomyces flavofungini TRM90047 exhibited anti-M. tb activity and decreased the expression level of several genes, including rpsL, Rplc and ClpC1. Guided by heteronuclear single quantum correlation-total correlation spectroscopy (HSQC-TOCSY) fingerprints and genome mining, we isolated two new 44-membered macrolides, desertomycin 44-1 (1) and desertomycin 44-2 (2), together with known desertomycin A (3) from S. flavofungini TRM90047. Three desertomycins showed anti-M. tb activity. The EC50 values of desertomycin A, desertomycin 44-1 and desertomycin 44-2 were 25 µg/mL, 25 µg/mL and 50 µg/mL, respectively. Molecular docking analyses revealed that the isolated desertomycins bound well to the RPSL, RPLC and CLPC1 proteins. In the present study, we describe the discovery of new anti-M. tb compounds guided by genome mining, HSQC-TOCSY and anti-M. tb bioassays.

NMR sugar-profile in genuine grape must.

The wine market has always faced the problem of fraud, including the addition of exogenous sugar solutions to grape musts to increase the final alcohol content. Since in some countries the practice of chaptalization is prohibited (except by adding concentrated must) it is necessary to broaden the analytical techniques that allow the identification of this type of fraud. The aim of this study was to define an NMR-based sugar profile of genuine grape must to set concentration limits for each sugar as parameters of authenticity. Glucose, fructose, together with eleven minor sugars were quantified in 82 genuine Italian grape musts, developing an analytical procedure based on highly selective chemical shift filters followed by TOCSY. Alongside the characteristic myo- and scyllo-inositol, significant contents of mannose, galactose, and trehalose were also found. Otherwise, maltose, rhamnose, arabinose, sucrose and lactose are present in lower concentrations and show great concentration variability.

Multinuclear 1H/13C/15N chemical shift assignment of therapeutic octreotide acetate performed at natural abundance.

Octreotide acetate, the active pharmaceutical ingredient in the long-acting release (LAR) drug product Sandostatin®, is a cyclic octapeptide that mimics the naturally occurring somatostatin peptide hormone. Modern NMR can be a robust analytical method to identify and quantify octreotide molecules. Previous 1H chemical shift assignments were mostly performed in organic solvents, and no assignments for heteronuclear 13C, 15N, and aromatic 1H nuclei are available. Here, using state-of-the-art 1D and 2D homo- and heteronuclear NMR experiments, octreotide was fully assigned, including water exchangeable amide protons, in aqueous buffer except for 13CO and 15NH of F1, 15NH of C2, and 15NζHζ of K5 that were not observed because of water exchange or conformational exchange. The solution NMR spectra were then directly compared with 1D 1H/13C/15N solid-state NMR (SSNMR) spectra showing the potential applicability of 13C/15N SSNMR for octreotide drug product characterization.

Single-Scan Ultraselective NMR Experiments with Preserved Sensitivity.

Selective NMR experiments provide rapid access to important structural information, and are essential to tackle the analysis of large molecules and complex mixtures. Single-scan ultraselective experiments are particularly useful, as they can rapidly select signals that overlap with other signals. Here, we describe a novel type of single-scan ultraselective NMR experiments that is robust against the effects of translational molecular diffusion, and thus make it possible to improve significantly the sensitivity of the experiment. This will largely broaden the applicability of this powerful class of experiments.

Multipronged Approach to Profiling Metabolites in Beta vulgaris L. Dried Pulp Extracts Using Chromatography, NMR and Other Spectroscopy Methods.

Beetroot (Beta vulgaris L.) is known for being a rich source of phytochemicals, minerals and vitamins. This study aims to show how the combination of extraction/chromatography/mass spectrometry and NMR offers an efficient way to profile metabolites in the extracts of beetroot. Such combination may lead to the identification of more nutritional or medicinal compounds in natural products, and it is essential for our ongoing investigation to study the selective adsorption/desorption of these metabolites' on/off nanoparticles. The aqueous and organic extracts underwent analyses using UV-vis spectroscopy; GC-MS; LC-MS; 1H, 13C, 31P, TOCSY, HSQC, and selective TOCSY NMR experiments. Polar Extract: The two forms of betalain pigment were identified by UV-vis and LC MS. Fourteen amino acids, sucrose, and other compounds, among which is riboflavin, were identified by LC-MS. Two-dimensional TOCSY showed the spin coupling correlations corresponding to some of these compounds. The HSQC spectrum showed 1H/13C spin correlation in sucrose, confirming its high abundance in beetroot. Organic Extract: GC-MS data enabled the identification of several compounds including six fatty acid methyl esters (FAME) with higher than, on average, 90% similarity score. Selective TOCSY NMR data showed the spin coupling pattern corresponding to oleic, linoleic, and linolenic fatty acids. 31P NMR spectra indicate that phospholipids exist in both the organic and aqueous phase.

Short-chain fatty acids reprogram metabolic profiles with the induction of reactive oxygen species production in human colorectal adenocarcinoma cells.

Short-chain fatty acids (SCFAs) exhibit anticancer activity in cellular and animal models of colon cancer. Acetate, propionate, and butyrate are the three major SCFAs produced from dietary fiber by gut microbiota fermentation and have beneficial effects on human health. Most previous studies on the antitumor mechanisms of SCFAs have focused on specific metabolites or genes involved in antitumor pathways, such as reactive oxygen species (ROS) biosynthesis. In this study, we performed a systematic and unbiased analysis of the effects of acetate, propionate, and butyrate on ROS levels and metabolic and transcriptomic signatures at physiological concentrations in human colorectal adenocarcinoma cells. We observed significantly elevated levels of ROS in the treated cells. Furthermore, significantly regulated signatures were involved in overlapping pathways at metabolic and transcriptomic levels, including ROS response and metabolism, fatty acid transport and metabolism, glucose response and metabolism, mitochondrial transport and respiratory chain complex, one-carbon metabolism, amino acid transport and metabolism, and glutaminolysis, which are directly or indirectly linked to ROS production. Additionally, metabolic and transcriptomic regulation occurred in a SCFAs types-dependent manner, with an increasing degree from acetate to propionate and then to butyrate. This study provides a comprehensive analysis of how SCFAs induce ROS production and modulate metabolic and transcriptomic levels in colon cancer cells, which is vital for understanding the mechanisms of the effects of SCFAs on antitumor activity in colon cancer.

Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing 1H-1H TOCSY NMR.

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the cellular pathways of development, and enhances our understanding of best practices for in vitro differentiation to guide cellular therapies. In this work, the dynamic evolution of adipose-tissue-derived human Mesenchymal stem cells (AT-derived hMSCs) after fourteen days of cultivation, adipocyte and osteocyte differentiation, was inspected based on 1H-1H TOCSY using machine learning. Multi-class classification in addition to the novelty detection of metabolites was established based on a control hMSC sample after four days' cultivation and we successively detected the changes of metabolites in differentiated MSCs following a set of 1H-1H TOCSY experiments. The classifiers Kernel Null Foley-Sammon Transform and Kernel Density Estimation achieved a total classification error between 0% and 3.6% and false positive and false negative rates of 0%. This approach was successfully able to automatically reveal metabolic changes that accompanied MSC cellular evolution starting from their undifferentiated status to their prolonged cultivation and differentiation into adipocytes and osteocytes using machine learning supporting the research in the field of metabolic pathways of stem cell differentiation.

Utilizing the 1H-15N NMR Methods for the Characterization of Isomeric Human Milk Oligosaccharides.

Human milk oligosaccharides (HMOs) are structurally complex unconjugated glycans that are the third largest solid fraction in human milk after lactose and lipids. HMOs are in the forefront of research since they have been proven to possess beneficial health effects, especially on breast-fed neonates. Although HMO research is a trending topic nowadays, readily available analytical methods suitable for the routine investigation of HMOs are still incomplete. NMR spectroscopy provides detailed structural information that can be used to indicate subtle structural differences, particularly for isomeric carbohydrates. Herein, we propose an NMR-based method to identify the major isomeric HMOs containing GlcNAc and/or Neu5Ac building blocks utilizing their amide functionality. Experimental conditions were optimized (H2O:D2O 9:1 v/v solvent at pH 3.0) to obtain 1H-15N HSQC and 1H-15N HSQC-TOCSY NMR spectra of the aforementioned building blocks in HMOs. Four isomeric HMO pairs, LNT/LNnT, 3'SL/6'SL, LNFP II/LNFP III, and LSTa/LSTb, were investigated, and complete NMR resonance assignments were provided. In addition, 1H and 15N NMR resonances were found to be indicative of various linkages, thereby facilitating the distinction of isomeric tri-, tetra-, and pentasaccharide HMOs. The rapid growth of HMO products (from infant formulas and dietary supplements to cosmetics) undoubtedly requires expanding the range of applicable analytical methods. Thus, our work provides a 15N NMR-based method to advance this challenging field of carbohydrate analysis.

Activation of Vegetable Oils by Reaction with Maleic Anhydride as a Renewable Source in Chemical Processes: New Experimental and Computational NMR Evidence.

Vegetable oils are bio-based and sustainable starting materials that can be used to develop chemicals for industrial processes. In this study, the functionalization of three vegetable oils (grape, hemp, and linseed) with maleic anhydride was carried out either by conventional heating or microwave activation to obtain products that, after further reactions, can enhance the water dispersion of oils for industrial applications. To identify the most abundant derivatives formed, trans-3-octene, methyl oleate, and ethyl linoleate were reacted as reference systems. A detailed NMR study, supported by computational evidence, allowed for the identification of the species formed in the reaction of trans-3-octene with maleic anhydride. The signals in the 1H NMR spectra of the alkenyl succinic anhydride (ASA) moieties bound to the organic chains were clearly identified. The reactions achieved by conventional heating were carried out for 5 h at 200 °C, resulting in similar or lower amounts of ASA units/g of oil with respect to the reactions performed by microwave activation, which, however, induced a higher viscosity of the samples.

Structural Basis of Artemisinin Binding Sites in Serum Albumin with the Combined Use of NMR and Docking Calculations.

Artemisinin is known to bind to the main plasma protein carrier serum albumin (SA); however, there are no atomic level structural data regarding its binding mode with serum albumin. Herein, we employed a combined strategy of saturation transfer difference (STD), transfer nuclear Overhauser effect spectroscopy (TR-NOESY), STD-total correlation spectroscopy (STD-TOCSY), and Interligand Noes for PHArmacophore Mapping (INPHARMA) NMR methods and molecular docking calculations to investigate the structural basis of the interaction of artemisinin with human and bovine serum albumin (HSA/BSA). A significant number of inter-ligand NOEs between artemisinin and the drugs warfarin and ibuprofen as well as docking calculations were interpreted in terms of competitive binding modes of artemisinin in the warfarin (FA7) and ibuprofen (FA4) binding sites. STD NMR experiments demonstrate that artemisinin is the main analyte for the interaction of the A. annua extract with BSA. The combined strategy of NMR and docking calculations of the present work could be of general interest in the identification of the molecular basis of the interactions of natural products with their receptors even within a complex crude extract.

Four-in-one: HSQC, HSQC-TOCSY (or H2BC), TOCSY, and enhanced HMBC spectra integrated into a single NO Relaxation Delay (NORD) NMR experiment.

The NMR pulse sequence design strategy of NORD (NO Relaxation Delay) is extended to design of two new three-module experiments, NORD {HMBC}-{HSQC-TOCSY}-{TOCSY} and NORD {HMBC}-{2BOB}-{TOCSY}, each delivering four spectra - HMBC, HSQC, TOCSY, and either HSQC-TOCSY or H2BC. Compared to individual recording of these spectra particularly the sensitivity of the least sensitive module, HMBC, is enhanced by designing the homonuclear TOCSY module to allow buildup of magnetization pertinent to HMBC during its execution. Effectively, the sensitivity of the heteronuclear modules is boosted at the expense of the inherently much higher TOCSY sensitivity, thus resulting in a significant saving in spectrometer time.

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

Conformational ensemble of the TNF-derived peptide solnatide in solution.

Tumor necrosis factor (TNF) is a homotrimer that has two spatially distinct binding regions, three lectin-like domains (LLD) at the TIP of the protein and three basolaterally located receptor-binding sites, the latter of which are responsible for the inflammatory and cell death-inducing properties of the cytokine. Solnatide (a.k.a. TIP peptide, AP301) is a 17-mer cyclic peptide that mimics the LLD of human TNF which activates the amiloride-sensitive epithelial sodium channel (ENaC) and, as such, recapitulates the capacity of TNF to enhance alveolar fluid clearance, as demonstrated in numerous preclinical studies. TNF and solnatide interact with glycoproteins and these interactions are necessary for their trypanolytic and ENaC-activating activities. In view of the crucial role of ENaC in lung liquid clearance, solnatide is currently being evaluated as a novel therapeutic agent to treat pulmonary edema in patients with moderate-to-severe acute respiratory distress syndrome (ARDS), as well as severe COVID-19 patients with ARDS. To facilitate the description of the functional properties of solnatide in detail, as well as to further target-docking studies, we have analyzed its folding properties by NMR. In solution, solnatide populates a set of conformations characterized by a small hydrophobic core and two electrostatically charged poles. Using the structural information determined here and also that available for the ENaC protein, we propose a model to describe solnatide interaction with the C-terminal domain of the ENaCα subunit. This model may serve to guide future experiments to validate specific interactions with ENaCα and the design of new solnatide analogs with unexplored functionalities.

NMR Assignment of Methyl Groups in Immobilized Proteins Using Multiple-Bond 13C Homonuclear Transfers, Proton Detection, and Very Fast MAS.

In nuclear magnetic resonance spectroscopy of proteins, methyl protons play a particular role as extremely sensitive reporters on dynamics, allosteric effects, and protein-protein interactions, accessible even in high-molecular-weight systems approaching 1 MDa. The notorious issue of their chemical shift assignment is addressed here by a joint use of solid-state 1H-detected methods at very fast (nearly 100 kHz) magic-angle spinning, partial deuteration, and high-magnetic fields. The suitability of a series of RF schemes is evaluated for the efficient coherence transfer across entire 13C side chains of methyl-containing residues, which is key for establishing connection between methyl and backbone 1H resonances. The performance of ten methods for recoupling of either isotropic 13C-13C scalar or anisotropic dipolar interactions (five variants of TOBSY, FLOPSY, DIPSI, WALTZ, RFDR, and DREAM) is evaluated experimentally at two state-of-the-art magic-angle spinning (55 and 94.5 kHz) and static magnetic field conditions (18.8 and 23.5 T). Model isotopically labeled compounds (alanine and Met-Leu-Phe tripeptide) and ILV-methyl and amide-selectively protonated, and otherwise deuterated chicken α-spectrin SH3 protein are used as convenient reference systems. Spin dynamics simulations in SIMPSON are performed to determine optimal parameters of these RF schemes, up to recently experimentally attained spinning frequencies (200 kHz) and B 0 field strengths (28.2 T). The concept of linearization of 13C side chain by appropriate isotope labeling is revisited and showed to significantly increase sensitivity of methyl-to-backbone correlations. A resolution enhancement provided by 4D spectroscopy with non-uniform (sparse) sampling is demonstrated to remove ambiguities in simultaneous resonance assignment of methyl proton and carbon chemical shifts.

HORRENDOUS NMR: Establishing correlations in solution-state NMR by reinstating non-secular J-coupling terms.

Homonuclear isotropic mixing modules allow J-coupled spins to exchange magnetization even when separated by chemical shift offsets that exceed their couplings. This is exploited in TOtal Correlation SpectroscopY (TOCSY) experiments and its variants, which facilitate these homonuclear polarization exchanges by applying broadband RF pulses. These then establish an effective Hamiltonian in which chemical shift offsets are erased, while J-coupling terms -including flip-flop components- remain active. The polarization that these non-secular terms will transfer among systems of chemically inequivalent sites over the course of a mixing period, are widely used modules in 1D and in multidimensional liquid-state NMR. Homonuclear correlation experiments are also common in solids NMR, particularly among X = 13C or 15N nuclei. Solids NMR experiments are often challenged by high-power RF demands which have led to a family of homonuclear solid-state correlation experiments that avoid pulsing on the nuclei of interest, and focus instead on the 1Hs that are bonded to them. These solid experiments usually reintroduce/strengthen 1H-X dipolar couplings; these, in conjunction with assistance from rotational resonance effects, bring back the truncated X-X dipolar interactions and facilitate the generation of cross peaks. The present study explores whether a similar goal can be achieved for solution-state counterparts, based on the reintroduction of truncated flip-flop terms in the J-coupling Hamiltonian via the pulsing on other, heteronuclear species. A proposal to achieve this is derived, and the resulting HOmonucleaR Recoupling by hEteroNuclear DecOUplingS (HORRENDOUS) approach to provide correlations between like nuclei without pulsing on them, is demonstrated.

Nuclear Magnetic Resonance Methods in Structural Characterization of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are sulfated glycans of complex structure and multiple biological actions. They are composed of disaccharide repeating units of alternating uronic acid/galactose and hexosamine. Sulfation patterns are an additional structural variation of these polymers. Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful analytical techniques employed in structural analysis of GAGs. 1D and 2D NMR spectra, both homonuclear 1H and heteronuclear 1H-13C, are the commonest NMR methods used. This chapter describes the overall experimental methods and materials necessary for adequate preparation of GAG samples for NMR investigations aimed to unveil the main structural characteristics of these biomacromolecules. The NMR methods discussed here cover all three isotopes (1H, 13C, and 15N) that can be exploited in structural analysis of GAGs. These NMR methods are described from an overall standpoint, to be applied to any GAG family, extracted from either natural or synthetic sources and destined to either basic research or pharmaceutical applications.

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR).

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from 1H-1H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the 1H-1H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.

Glycosylation States on Intact Proteins Determined by NMR Spectroscopy.

Protein glycosylation is important in many organisms for proper protein folding, signaling, cell adhesion, protein-protein interactions, and immune responses. Thus, effectively determining the extent of glycosylation in glycoprotein therapeutics is crucial. Up to now, characterizing protein glycosylation has been carried out mostly by liquid chromatography mass spectrometry (LC-MS), which requires careful sample processing, e.g., glycan removal or protein digestion and glycopeptide enrichment. Herein, we introduce an NMR-based method to better characterize intact glycoproteins in natural abundance. This non-destructive method relies on exploiting differences in nuclear relaxation to suppress the NMR signals of the protein while maintaining glycan signals. Using RNase B Man5 and RNase B Man9, we establish reference spectra that can be used to determine the different glycoforms present in heterogeneously glycosylated commercial RNase B.

Increasing sensitivity and versatility in NMR supersequences with new HSQC-based modules.

The sensitivity-enhanced HSQC, as well as HSQC-TOCSY, experiments have been modified for incorporation into NOAH (NMR by Ordered Acquisition using 1H detection) supersequences, adding diversity for 13C and 15N modules. Importantly, these heteronuclear modules have been specifically tailored to preserve the magnetisation required for subsequent acquisition of other heteronuclear or homonuclear modules in a supersequence. In addition, we present protocols for optimally combining HSQC and HSQC-TOCSY elements within the same supersequences, yielding high-quality 2D spectra suitable for structure characterisation but with greatly reduced experiment durations. We further demonstrate that these time savings can translate to increased detection sensitivity per unit time.

Prioritizing natural product compounds using 1D-TOCSY NMR spectroscopy.

Natural product (NP) secondary metabolites are designed evolutionarily to have biological effects in other organisms for defense and the mediation of ecological interactions. Their structural complexity and diversity complement biological systems, allowing them to display unique bioactivities. Although more than half of all pharmaceuticals stem from NPs, pharmaceutical companies have reduced NP-based drug discovery programs due to various time and cost-consuming pitfalls; the re-isolation of already known, bioactive compounds being one of the most common. Dereplication methods minimize cost and speed up the discovery of new, bioactive leads by quickly identifying known small molecules. Liquid chromatography coupled mass spectrometry (LC-MS) is the most widely utilized dereplication technique because of its sensitivity and the open-source availability of MS libraries. However, single-ionization techniques are not able to detect all metabolites in a biological sample. Even more concerning, bioactive isomers cannot be differentiated by their mass alone. In response to these issues, complementary dereplication tools are needed to assist MS. Total correlation spectroscopy (TOCSY) is an NMR experiment that illustrates the connection between all coupled protons in a spin system. Most molecules contain several spin systems, and together, these networks form a unique fingerprint that can be utilized to quickly differentiate and dereplicate known compounds, even those with identical masses. In addition, these fingerprints can be used to identify possible new compounds in a crude NP-extract that are structurally related to known small molecules. From a sample of the U.S. endemic lichen Niebla homalea, five non-cytotoxic, new triterpenoids and three known triterpenoids were isolated in our laboratory. As our goal is to discover both new and cytotoxic compounds, we developed a one-dimensional TOCSY-based dereplication method to quickly identify these non-bioactive triterpenoids. After prioritizing triterpenoid-free fractions that showed antiproliferative activity in various cancer cell lines, the new compound 11 was isolated from another Niebla species.