Quantitative metabolomics using NMR

A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group

OBJECTIVES

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation.

METHODS

A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed.

RESULTS

Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology.

CONCLUSIONS

Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

Majorbio Cloud 2024: Update single-cell and multiomics workflows

Majorbio Cloud (https://cloud.majorbio.com/) is a one-stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single-omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.

Metabolic Deficits in the Retina of a Familial Dysautonomia Mouse Model

Neurodegenerative retinal diseases such as glaucoma, diabetic retinopathy, Leber's hereditary optic neuropathy (LHON), and dominant optic atrophy (DOA) are marked by progressive death of retinal ganglion cells (RGC). This decline is promoted by structural and functional mitochondrial deficits, including electron transport chain (ETC) impairments, increased oxidative stress, and reduced energy (ATP) production. These cellular mechanisms associated with progressive optic nerve atrophy have been similarly observed in familial dysautonomia (FD) patients, who experience gradual loss of visual acuity due to the degeneration of RGCs, which is thought to be caused by a breakdown of mitochondrial structures, and a disruption in ETC function. Retinal metabolism plays a crucial role in meeting the elevated energetic demands of this tissue, and recent characterizations of FD patients' serum and stool metabolomes have indicated alterations in central metabolic processes and potential systemic deficits of taurine, a small molecule essential for retina and overall eye health. The present study sought to elucidate metabolic alterations that contribute to the progressive degeneration of RGCs observed in FD. Additionally, a critical subpopulation of retinal interneurons, the dopaminergic amacrine cells, mediate the integration and modulation of visual information in a time-dependent manner to RGCs. As these cells have been associated with RGC loss in the neurodegenerative disease Parkinson's, which shares hallmarks with FD, a targeted analysis of the dopaminergic amacrine cells and their product, dopamine, was also undertaken. One dimensional (1D) proton (1H) nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and retinal histology methods were employed to characterize retinae from the retina-specific Elp1 conditional knockout (CKO) FD mouse model (Pax6-Cre; Elp1LoxP/LoxP). Metabolite alterations correlated temporally with progressive RGC degeneration and were associated with reduced mitochondrial function, alterations in ATP production through the Cahill and mini-Krebs cycles, and phospholipid metabolism. Dopaminergic amacrine cell populations were reduced at timepoints P30-P90, and dopamine levels were 25-35% lower in CKO retinae compared to control retinae at P60. Overall, this study has expanded upon our current understanding of retina pathology in FD. This knowledge may apply to other retinal diseases that share hallmark features with FD and may help guide new avenues for novel non-invasive therapeutics to mitigate the progressive optic neuropathy in FD.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy

BACKGROUND

To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible.

RESULTS

We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples.

SIGNIFICANCE

The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

Unveiling novel metabolic alterations in postmenopausal osteoporosis and type 2 diabetes mellitus through NMR-based metabolomics: A pioneering approach for identifying early diagnostic markers

BACKGROUND AND AIMS

Postmenopausal osteoporosis (PMO) and type 2 diabetes mellitus (T2DM) frequently coexist in postmenopausal women. The study aimed to explore metabolic variations linked to these circumstances and their simultaneous presence through proton nuclear magnetic resonance metabolomics (1H NMR).

MATERIALS AND METHODS

Serum samples from 80 postmenopausal women, including 20 PMO individuals, 20 T2DM, 20 T2DM + PMO, and 20 healthy postmenopausal women, were analyzed using 1H NMR spectroscopy.

RESULTS

Our study revealed significant metabolic profile differences among the four groups. Notably, the T2DM + PMO group showed elevated levels of alanine, pyruvate, glutamate, lactate, and aspartate, indicating their involvement in lipid metabolism, energy, and amino acids. Importantly, our multivariate statistical analysis identified a metabolite set that accurately distinguished the groups, suggesting its potential as an early diagnostic marker.

CONCLUSION

The 1H NMR metabolomics approach uncovered metabolic biomarkers intricately linked to postmenopausal osteoporosis (PMO), type 2 diabetes mellitus (T2DM), and their concurrent presence. Among these biomarkers, alanine emerged as a pivotal player, showing its significant role in the metabolic landscape associated with PMO and T2DM. These findings shed light on the pathophysiological mechanisms underlying these conditions and underscore alanine's potential as a diagnostic biomarker.

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Integrated Metabolomics and Proteomics of Symptomatic and Early Presymptomatic States of Colitis

Colitis has a multifactorial pathogenesis with a strong cross-talk among microbiota, hypoxia, and tissue metabolism. Here, we aimed to characterize the molecular signature of the disease in symptomatic and presymptomatic stages of the inflammatory process at the tissue and fecal level. The study is based on two different murine models for colitis, and HR-MAS NMR on "intact" colon tissues and LC-MS/MS on colon tissue extracts were used to derive untargeted metabolomics and proteomics information, respectively. Solution NMR was used to derive metabolomic profiles of the fecal extracts. By combining metabolomic and proteomic analyses of the tissues, we found increased anaerobic glycolysis, accompanied by an altered citric acid cycle and oxidative phosphorylation in inflamed colons; these changes associate with inflammation-induced hypoxia taking place in colon tissues. Different colitis states were also characterized by significantly different metabolomic profiles of fecal extracts, attributable to both the dysbiosis characteristic of colitis as well as the dysregulated tissue metabolism. Strong and distinctive tissue and fecal metabolomic signatures can be detected before the onset of symptoms. Therefore, untargeted metabolomics of tissues and fecal extracts provides a comprehensive picture of the changes accompanying the disease onset already at preclinical stages, highlighting the diagnostic potential of global metabolomics for inflammatory diseases.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida

Pseudomonas putida, a microbial host widely adopted for metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. The periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we adopted a dynamic 13C- and 2H-metabolic flux analysis strategy, termed D-fluxomics. D-fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate-unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.

NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows

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.

Recent advances in understanding brain cancer metabolomics: a review

Regardless of the significant progress made in surgical techniques and adjuvant therapies, brain tumors are a major contributor to cancer-related morbidity and mortality in both pediatric and adult populations. Gliomas represent a significant proportion of cerebral neoplasms, exhibiting diverse levels of malignancy. The etiology and mechanisms of resistance of this malignancy are inadequately comprehended, and the optimization of patient diagnosis and prognosis is a challenge due to the diversity of the disease and the restricted availability of therapeutic options. Metabolomics refers to the comprehensive analysis of endogenous and exogenous small molecules, both in a targeted and untargeted manner, that enables the characterization of an individual's phenotype and offers valuable insights into cellular activity, particularly in the context of cancer biology, including brain tumor biology. Metabolomics has garnered attention in current years due to its potential to facilitate comprehension of the dynamic spatiotemporal regulatory network of enzymes and metabolites that enables cancer cells to adapt to their environment and foster the development of tumors. Metabolic changes are widely acknowledged as a significant characteristic for tracking the advancement of diseases, treatment efficacy, and identifying novel molecular targets for successful medical management. Metabolomics has emerged as an exciting area for personalized medicine and drug discovery, utilizing advanced analytical techniques such as nuclear magnetic resonance spectroscopy (MRS) and mass spectrometry (MS) to achieve high-throughput analysis. This review examines and highlights the latest developments in MRS, MS, and other technologies in studying human brain tumor metabolomics.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

A different approach to the quantification of human seminal plasma metabolites using high-resolution NMR spectroscopy

In this study, a reliable method was established for the absolute quantification of metabolite concentrations in human seminal plasma using ERETIC2, a quantification tool developed by Bruker based on the PULCON principle. The performance of the ERETIC2 was examined using an AVANCE III HD NMR spectrometer (600 MHz) equipped with a triple inverse 1.7 mm TXI probe in terms of some experimental parameters that may affect the accuracy and precision of the quantitative results. Then, the accuracy, precision, and repeatibility of ERETIC2 were determined using L-asparagine solutions at different concentrations. And it was evaluated by comparing it with the classical internal standard (IS) quantification method. The relative standard deviation (RSD) values for ERETIC2 were calculated in the range of 0.55-1.90% and the minimum recovery value was 99.9%, while the RSD values for the IS method were calculated in the range of 0.88-5.83% and recovery value was minimum 91.0%. Besides, the RSD values of the inter-day precisions for the ERETIC2 and IS methods were obtained to be in the range of 1.25 - 3.03% and 0.97 - 3.46%, respectively. Finally, the concentration values of seminal plasma metabolites were determined using different pulse programs with both methods for samples obtained from normozoospermic control and azoospermic patient groups. The results proved that this quantification method developed using NMR spectroscopy is easy to use in complex sample systems such as biological fluids and is a good alternative to the classical internal standard method in terms of accuracy and sensitivity. In addition, the improvement of the spectral resolution and sensitivity with the microcoil probe technology and the possibility of analyzing with minimum sample quantities has contributed positively to the results of this method.

Closing the gap between in vivo and in vitro omics: using QA/QC to strengthen ex vivo NMR metabolomics

Metabolomics aims to achieve a global quantitation of the pool of metabolites within a biological system. Importantly, metabolite concentrations serve as a sensitive marker of both genomic and phenotypic changes in response to both internal and external stimuli. NMR spectroscopy greatly aids in the understanding of both in vitro and in vivo physiological systems and in the identification of diagnostic and therapeutic biomarkers. Accordingly, NMR is widely utilized in metabolomics and fluxomics studies due to its limited requirements for sample preparation and chromatography, its non-destructive and quantitative nature, its utility in the structural elucidation of unknown compounds, and, importantly, its versatility in the analysis of in vitro, in vivo, and ex vivo samples. This review provides an overview of the strengths and limitations of in vitro and in vivo experiments for translational research and discusses how ex vivo studies may overcome these weaknesses to facilitate the extrapolation of in vitro insights to an in vivo system. The application of NMR-based metabolomics to ex vivo samples, tissues, and biofluids can provide essential information that is close to a living system (in vivo) with sensitivity and resolution comparable to those of in vitro studies. The success of this extrapolation process is critically dependent on high-quality and reproducible data. Thus, the incorporation of robust quality assurance and quality control checks into the experimental design and execution of NMR-based metabolomics experiments will ensure the successful extrapolation of ex vivo studies to benefit translational medicine.

Metabolomic Studies in Inborn Errors of Metabolism: Last Years and Future Perspectives

The inborn errors of metabolism (IEMs or Inherited Metabolic Disorders) are a heterogeneous group of diseases caused by a deficit of some specific metabolic pathways. IEMs may present with multiple overlapping symptoms, sometimes difficult delayed diagnosis and postponed therapies. Additionally, many IEMs are not covered in newborn screening and the diagnostic profiling in the metabolic laboratory is indispensable to reach a correct diagnosis. In recent years, Metabolomics helped to obtain a better understanding of pathogenesis and pathophysiology of IEMs, by validating diagnostic biomarkers, discovering new specific metabolic patterns and new IEMs itself. The expansion of Metabolomics in clinical biochemistry and laboratory medicine has brought these approaches in clinical practice as part of newborn screenings, as an exam for differential diagnosis between IEMs, and evaluation of metabolites in follow up as markers of severity or therapies efficacy. Lastly, several research groups are trying to profile metabolomics data in platforms to have a holistic vision of the metabolic, proteomic and genomic pathways of every single patient. In 2018 this team has made a review of literature to understand the value of Metabolomics in IEMs. Our review offers an update on use and perspectives of metabolomics in IEMs, with an overview of the studies available from 2018 to 2022.

Targeting mitochondrial impairment for the treatment of cardiovascular diseases: From hypertension to ischemia-reperfusion injury, searching for new pharmacological targets

Mitochondria and mitochondrial proteins represent a group of promising pharmacological target candidates in the search of new molecular targets and drugs to counteract the onset of hypertension and more in general cardiovascular diseases (CVDs). Indeed, several mitochondrial pathways result impaired in CVDs, showing ATP depletion and ROS production as common traits of cardiac tissue degeneration. Thus, targeting mitochondrial dysfunction in cardiomyocytes can represent a successful strategy to prevent heart failure. In this context, the identification of new pharmacological targets among mitochondrial proteins paves the way for the design of new selective drugs. Thanks to the advances in omics approaches, to a greater availability of mitochondrial crystallized protein structures and to the development of new computational approaches for protein 3D-modelling and drug design, it is now possible to investigate in detail impaired mitochondrial pathways in CVDs. Furthermore, it is possible to design new powerful drugs able to hit the selected pharmacological targets in a highly selective way to rescue mitochondrial dysfunction and prevent cardiac tissue degeneration. The role of mitochondrial dysfunction in the onset of CVDs appears increasingly evident, as reflected by the impairment of proteins involved in lipid peroxidation, mitochondrial dynamics, respiratory chain complexes, and membrane polarization maintenance in CVD patients. Conversely, little is known about proteins responsible for the cross-talk between mitochondria and cytoplasm in cardiomyocytes. Mitochondrial transporters of the SLC25A family, in particular, are responsible for the translocation of nucleotides (e.g., ATP), amino acids (e.g., aspartate, glutamate, ornithine), organic acids (e.g. malate and 2-oxoglutarate), and other cofactors (e.g., inorganic phosphate, NAD⁺, FAD, carnitine, CoA derivatives) between the mitochondrial and cytosolic compartments. Thus, mitochondrial transporters play a key role in the mitochondria-cytosol cross-talk by leading metabolic pathways such as the malate/aspartate shuttle, the carnitine shuttle, the ATP export from mitochondria, and the regulation of permeability transition pore opening. Since all these pathways are crucial for maintaining healthy cardiomyocytes, mitochondrial carriers emerge as an interesting class of new possible pharmacological targets for CVD treatments.

Linking processes to community functions—insights into litter decomposition combining fungal metatranscriptomics and environmental NMR profiling

In forest ecosystems, decomposition is essential for carbon and nutrient cycling and therefore a key process for ecosystem functioning. During the decomposition process, litter chemistry, involved decomposer organisms, and enzymatic activity change interdependently. Chemical composition of the litter is the most complex and dynamic component in the decomposition process and therefore challenging to assess holistically. In this study, we aimed to characterize chemical shifts during decomposition and link them to changes in decomposer fungal activity. We characterized the chemical composition of freshly fallen autumn leaves of European beech (Fagus sylvatica) and the corresponding leaf litter after 1 year of decomposition by proton nuclear magnetic resonance spectroscopy. We further tested the applicability of spiking experiments for qualitative and quantitative characterization of leaves and litter chemistry. The composition and transcriptional activity of fungal communities was assessed by high-throughput Illumina sequencing in the same litter samples. We were able to distinguish freshly fallen leaves from 1-year-old litter based on their chemical composition. Chemical composition of leaves converged among regions with progressing decomposition. Fungal litter communities differed in composition among regions, but they were functionally redundant according to the expression of genes encoding litter degrading enzymes (CAZymes). Fungi of the saprotrophic genera Mycena and Chalara correlated with transcription of litter-degrading CAZymes in 1-year-old litter. Forestry measures influenced the diversity and transcription rate of the detected CAZymes transcripts in litter. Their expression was primarily predicted by composition of the soluble chemical fraction of the litter. Environmental NMR fingerprints thus proved valuable for inferring ecological contexts. We propose and discuss a holistic framework to link fungal activity, enzyme expression, and chemical composition.

Qualitative and Quantitative Mass Spectrometry in Salivary Metabolomics and Proteomics

The metabolomics and proteomics analysis of saliva, an excellent biofluid that is a rich source of biological compounds, allows for the safe and frequent screening of drugs, their metabolites, and molecular biomarkers of various diseases. One of the most frequently used analytical methods in saliva analysis is liquid chromatography coupled with mass spectrometry (LC-MS) and tandem mass spectrometry. The low ionisation efficiency of some compounds and a complex matrix makes their identification by MS difficult. Furthermore, quantitative analysis by LC-MS frequently cannot be performed without isotopically labelled standards, which usually have to be specially synthesised. This review presented reports on qualitative and quantitative approaches in salivary metabolomics and proteomics. The purpose of this manuscript was to present the challenges, advances, and future prospects of mass spectrometry, both in the analysis of salivary metabolites and proteins. The presented review should appeal to those interested in the recent advances and trends in qualitative and quantitative mass spectrometry in salivary metabolomics and proteomics, which may facilitate a diagnostic accuracy, the evaluation of treatment efficacy, the early diagnosis of disease, and a forensic investigation of some unapproved drugs for any medical or dietary administration.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification

The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.

Chemical shift variations in common metabolites

The reliability and robustness of metabolite assignments in 1H NMR is complicated by numerous factors including variations in temperature, pH, buffer choice, ionic strength, and mixture composition that led to peak overlap and spectral crowding. As sample conditions fluctuate, peak drift and line broadening further complicate peak deconvolution and subsequent chemical assignment. We present a collection of 1D 1H NMR spectra of 54 common metabolites at varied pH (6.0 to 8.0 in 0.5 step increments) and temperature (290 K to 308 K) to quantify chemical shift variability to facilitate automated metabolite assignments. Our results illustrate the fundamental challenges with accurately assigning NMR peaks under varied environmental conditions prevalent in complex mixtures. Phosphorylated metabolites showed a larger variation in chemical shifts due to pH, whereas; amino acids showed a higher variation due to temperature. Mixtures of phosphorous compounds showed a consistently poor reliability in achieving an accurate assignment. Phosphorylated cholines, amino acids, and glycerols yielded a 40 % false negative rate for 7 out of 9 mixture conditions. Amino acids had a false negative rate of 57 % at 298 K and pH 8. Our results demonstrate that the automated assignments of complex biofluid mixtures require an expert to intervene to confirm the accuracy of metabolite assignments. Our analysis also indicates the need for reference databases to include spectra under a variety of conditions that includes mixtures and a range of pH and temperature to improve the accuracy and reproducibility of metabolite assignments.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Fine optimization of a dissolution dynamic nuclear polarization experimental setting for 13C NMR of metabolic samples

NMR-based analysis of metabolite mixtures provides crucial information on biological systems but mostly relies on 1D 1 H experiments for maximizing sensitivity. However, strong peak overlap of 1 H spectra often is a limitation for the analysis of inherently complex biological mixtures. Dissolution dynamic nuclear polarization (d-DNP) improves NMR sensitivity by several orders of magnitude, which enables 13 C NMR-based analysis of metabolites at natural abundance. We have recently demonstrated the successful introduction of d-DNP into a full untargeted metabolomics workflow applied to the study of plant metabolism. Here we describe the systematic optimization of d-DNP experimental settings for experiments at natural 13 C abundance and show how the resolution, sensitivity, and ultimately the number of detectable signals improve as a result. We have systematically optimized the parameters involved (in a semi-automated prototype d-DNP system, from sample preparation to signal detection, aiming at providing an optimization guide for potential users of such a system, who may not be experts in instrumental development). The optimization procedure makes it possible to detect previously inaccessible protonated 13 C signals of metabolites at natural abundance with at least 4 times improved line shape and a high repeatability compared to a previously reported d-DNP-enhanced untargeted metabolomic study. This extends the application scope of hyperpolarized 13 C NMR at natural abundance and paves the way to a more general use of DNP-hyperpolarized NMR in metabolomics studies.

Integrated analysis of the salivary microbiome and metabolome in chronic and aggressive periodontitis: A pilot study

This pilot study was designed to identify the salivary microbial community and metabolic characteristics in patients with generalized periodontitis. A total of 36 saliva samples were collected from 13 patients with aggressive periodontitis (AgP), 13 patients with chronic periodontitis (ChP), and 10 subjects with periodontal health (PH). The microbiome was evaluated using 16S rRNA gene high-throughput sequencing, and the metabolome was accessed using gas chromatography-mass spectrometry. The correlation between microbiomes and metabolomics was analyzed by Spearman’s correlation method. Our results revealed that the salivary microbial community and metabolite composition differed significantly between patients with periodontitis and healthy controls. Striking differences were found in the composition of salivary metabolites between AgP and ChP. The genera Treponema, Peptococcus, Catonella, Desulfobulbus, Peptostreptococcaceae_[XI] ([G-2], [G-3] [G-4], [G-6], and [G-9]), Bacteroidetes_[G-5], TM7_[G-5], Dialister, Eikenella, Fretibacterium, and Filifactor were present in higher levels in patients with periodontitis than in the healthy participants. The biochemical pathways that were significantly different between ChP and AgP included pyrimidine metabolism; alanine, aspartate, and glutamate metabolism; beta-alanine metabolism; citrate cycle; and arginine and proline metabolism. The differential metabolites between ChP and AgP groups, such as urea, beta-alanine, 3-aminoisobutyric acid, and thymine, showed the most significant correlations with the genera. These differential microorganisms and metabolites may be used as potential biomarkers to monitor the occurrence and development of periodontitis through the utilization of non-invasive and convenient saliva samples. This study reveals the integration of salivary microbial data and metabolomic data, which provides a foundation to further explore the potential mechanism of periodontitis.

Overexpression of bacteriophage T4 and T7 endolysins differentially regulate the metabolic fingerprint of host Escherichia coli

Bioactive proteins are often overexpressed in different host systems for biotechnological/biomedical applications. Endolysins are natural bactericidal proteins that cleave the bacterial peptidoglycan membrane, and have the potential to be the next-generation enzybiotics. Therefore, the present study aims to elucidate the impact of two endolysins (T4L, T7L) overexpression on metabolic fingerprint of E. coli using NMR spectroscopy. The ¹H NMR-based metabolomics analysis revealed global metabolite profiles of E. coli in response to endolysins. The study has identified nearly 75 metabolites, including organic acids, amino acids, sugars and nucleic acids. RNA Polymerase (RNAP) has been considered as reference protein for marking the specific alterations in metabolic pathways. The data suggested downregulation of central carbon metabolic pathway in both endolysins overexpression, but to a different extent. Also, the endolysin overexpression have highlighted the enhanced metabolic load and stress generation in the host cells, thus leading to the activation of osmoregulatory pathways. The overall changes in metabolic fingerprint of E. coli highlights the enhanced perturbations during the overexpression of T4L as compared to T7L. These untargeted metabolic studies shed light on the regulation of molecular pathways during the heterologous overexpression of these lytic enzymes that are lethal to the host.

Expression of Exogenous GFP-CesA6 in Tobacco Enhances Cell Wall Biosynthesis and Biomass Production

Simple Summary

Cellulose is synthesized at the plasma membrane by an enzymatic complex constituted by different cellulose synthase (CesA) proteins. The overexpression of CesA genes has been assessed for increasing cellulose biosynthesis and plant biomass. In this study, we analyzed transgenic tobacco plants (F₃1 line), stably expressing the Arabidopsis CesA6 fused to GFP, for possible variations in the cellulose biosynthesis. We found that F₃1 plants were bigger than the wild-type (wt), showing significant increases of stem height, root length, and leaf area. They bloomed about 3 weeks earlier and yielded more flowers and seeds than wt. In the F₃1 leaves, the expression of the exogenous GFP-CesA6 prompted the overexpression of all CesAs involved in the synthesis of primary cell wall cellulose and of other proteins responsible for plant cell wall building and remodeling. Instead, secondary cell wall CesAs were not affected. In the F₃1 stem, showing a 3.3-fold increase of the secondary xylem thickness, both primary and secondary CesAs expression was differentially modulated. Significantly, the amounts of cellulose and matrix polysaccharides increased in the transformed seedlings. The results evidence the potentiality to overexpress primary CesAs in tobacco for biomass production increase.

Abstract

Improved cellulose biosynthesis and plant biomass represent important economic targets for several biotechnological applications including bioenergy and biofuel production. The attempts to increase the biosynthesis of cellulose by overexpressing CesAs proteins, components of the cellulose synthase complex, has not always produced consistent results. Analyses of morphological and molecular data and of the chemical composition of cell walls showed that tobacco plants (F₃1 line), stably expressing the Arabidopsis CesA6 fused to GFP, exhibits a “giant” phenotype with no apparent other morphological aberrations. In the F₃1 line, all evaluated growth parameters, such as stem and root length, leaf size, and lignified secondary xylem, were significantly higher than in wt. Furthermore, F₃1 line exhibited increased flower and seed number, and an advance of about 20 days in the anthesis. In the leaves of F₃1 seedlings, the expression of primary CesAs (NtCesA1, NtCesA3, and NtCesA6) was enhanced, as well as of proteins involved in the biosynthesis of non-cellulosic polysaccharides (xyloglucans and galacturonans, NtXyl4, NtGal10), cell wall remodeling (NtExp11 and XTHs), and cell expansion (NtPIP1.1 and NtPIP2.7). While in leaves the expression level of all secondary cell wall CesAs (NtCesA4, NtCesA7, and NtCesA8) did not change significantly, both primary and secondary CesAs were differentially expressed in the stem. The amount of cellulose and matrix polysaccharides significantly increased in the F₃1 seedlings with no differences in pectin and hemicellulose glycosyl composition. Our results highlight the potentiality to overexpress primary CesAs in tobacco plants to enhance cellulose synthesis and biomass production.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Urinary metabolic modulation in human participants residing in Siachen: a 1H NMR metabolomics approach

The main physiological challenge in high altitude environment is hypoxia which affects the aerobic metabolism reducing the energy supply. These changes may further progress toward extreme environment-related diseases. These are further reflected in changes in small molecular weight metabolites and metabolic pathways. In the present study, metabolic changes due to chronic environmental hypoxia were assessed using 1H NMR metabolomics by analysing the urinary metabolic profile of 70 people at sea level and 40 people at Siachen camp (3700 m) for 1 year. Multivariate statistical analysis was carried out, and PLSDA detected 15 metabolites based on VIP score > 1. ROC analysis detected cis-aconitate, Nicotinamide Mononucleotide, Tyrosine, Choline and Creatinine metabolites with a high range of sensitivity and specificity. Pathway analysis revealed 16 pathways impact > 0.05, and phenylalanine tyrosine and tryptophan biosynthesis was the most prominent altered pathway indicating metabolic remodelling to meet the energy requirements. TCA cycle, Glycine serine and Threonine metabolism, Glutathione metabolism and Cysteine alterations were other metabolic pathways affected during long-term high-altitude hypoxia exposure. Present findings will help unlock a new dimension for the potential application of NMR metabolomics to address extreme environment-related health problems, early detection and developing strategies to combat high altitude hypoxia.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Mass Spectrometry-Based Metabolomics of Phytocannabinoids from Non-Cannabis Plant Origins

Phytocannabinoids are isoprenylated resorcinyl polyketides produced mostly in glandular trichomes of Cannabis sativa L. These discoveries led to the identification of cannabinoid receptors, which modulate psychotropic and pharmacological reactions and are found primarily in the human central nervous system. As a result of the biogenetic process, aliphatic ketide phytocannabinoids are exclusively found in the cannabis species and have a limited natural distribution, whereas phenethyl-type phytocannabinoids are present in higher plants, liverworts, and fungi. The development of cannabinomics has uncovered evidence of new sources containing various phytocannabinoid derivatives. Phytocannabinoids have been isolated as artifacts from their carboxylated forms (pre-cannabinoids or acidic cannabinoids) from plant sources. In this review, the overview of the phytocannabinoid biosynthesis is presented. Different non-cannabis plant sources are described either from those belonging to the angiosperm species and bryophytes, together with their metabolomic structures. Lastly, we discuss the legal framework for the ingestion of these biological materials which currently receive the attention as a legal high.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Elevated Circulatory Proline to Glutamine Ratio (PQR) in Endometriosis and Its Potential as a Diagnostic Biomarker

Endometriosis (EM) is a hormone-dependent gynecological disease associated with chronic pelvic pain and altered immuno-inflammatory processes. It shares some cancer-like characteristics such as increased proline biosynthesis and activated glutaminolysis. Both proline and glutamine are interconvertible metabolically, and studies have shown their roles in cancer cell metabolic reprogramming, redox homeostasis, occurrence/development of endometrial carcinoma, and its further progression toward the malignant state. So based on this, we hypothesized that the circulatory proline to glutamine ratio (PQR) would be altered in EM and may serve as an indicative biomarker to improve the clinical diagnosis of EM. In present study, the circulatory-PQR levels were estimated for 39 EM patients and 48 age matched healthy female subjects using 800 MHz NMR spectroscopy. Among 39 EM patients, 15 were in the clinical stages I to II and referred to here as moderate EM (MEM) patients and 24 were in the clinical stages III to IV and referred here as severe EM (SEM) patients. The circulatory-PQR levels were significantly increased in EM patients (0.99 ± 0.13 μM in MEM; 1.39 ± 0.22 μM in SEM) compared to normal control (NC) subjects (0.52 ± 0.05 μM in NC). Further, the circulatory PQR levels exhibit the highest diagnostic potential with area under receiver operating characteristic (AUROC) curve values equal to 0.87 ± 0.04 [95%CI = 0.79-0.96] for MEM and 0.89 ± 0.04 [95% CI = 0.82-0.96] for SEM. These results suggested that circulatory-PQR has significant potential to serve as a noninvasive biomarker for diagnostic/prognostic screening of EM and further underscored the importance of these two nonessential amino acids (proline and glutamine) in cancer metabolism.

A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

In-Cell Quantification of Drugs by Magic-Angle Spinning Dynamic Nuclear Polarization NMR

The determination of intracellular drug concentrations can provide a better understanding of the drug function and efficacy. Ideally, this should be performed nondestructively, with no modification of either the drug or the target, and with the capability to detect low amounts of the molecule of interest, in many cases in the μM to nM range (pmol to fmol per million cells). Unfortunately, it is currently challenging to have an experimental technique that provides direct quantitative measurements of intracellular drug concentrations that simultaneously satisfies these requirements. Here, we show that magic-angle spinning dynamic nuclear polarization (MAS DNP) can be used to fulfill these requirements. We apply a quantitative ¹⁵N MAS DNP approach in combination with ¹⁵N labeling to quantify the intracellular amount of the drug [¹⁵N]CHIR-98014, an activator of the Wingless and Int-1 signaling pathway, determining intracellular drug amounts in the range of tens to hundreds of picomoles per million cells. This is, to our knowledge, the first time that MAS DNP has been used to successfully estimate intracellular drug amounts.

Methane emissions and rumen metabolite concentrations in cattle fed two different silages

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH4) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH4 emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH4 variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH4 emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH4 emissions.

NMR Metabolomics Reveal Urine Markers of Microbiome Diversity and Identify Benzoate Metabolism as a Mediator between High Microbial Alpha Diversity and Metabolic Health

Microbial metabolites measured using NMR may serve as markers for physiological or pathological host–microbe interactions and possibly mediate the beneficial effects of microbiome diversity. Yet, comprehensive analyses of gut microbiome data and the urine NMR metabolome from large general population cohorts are missing. Here, we report the associations between gut microbiota abundances or metrics of alpha diversity, quantified from stool samples using 16S rRNA gene sequencing, with targeted urine NMR metabolites measures from 951 participants of the Study of Health in Pomerania (SHIP). We detected significant genus–metabolite associations for hippurate, succinate, indoxyl sulfate, and formate. Moreover, while replicating the previously reported association between hippurate and measures of alpha diversity, we identified formate and 4-hydroxyphenylacetate as novel markers of gut microbiome alpha diversity. Next, we predicted the urinary concentrations of each metabolite using genus abundances via an elastic net regression methodology. We found profound associations of the microbiome-based hippurate prediction score with markers of liver injury, inflammation, and metabolic health. Moreover, the microbiome-based prediction score for hippurate completely mediated the clinical association pattern of microbial diversity, hinting at a role of benzoate metabolism underlying the positive associations between high alpha diversity and healthy states. In conclusion, large-scale NMR urine metabolomics delivered novel insights into metabolic host–microbiome interactions, identifying pathways of benzoate metabolism as relevant candidates mediating the beneficial health effects of high microbial alpha diversity.

Nuclear magnetic resonance-based metabolomics and cytotoxicity (HT-29 and HCT-116 cell lines) studies insight the potential of less utilized parts of Camellia sinensis (Kangra tea)

Camellia sinensis (tea) is an evergreen plant having bioactive compounds associated with various pharmacological effects, including anti-cancerous activity. These phytochemicals are variedly distributed in plant tissues. A detailed study to understand chemical composition within the economically underutilized tea tissues is required to generate value. Therefore, a comprehensive chemical profiling of underutilized C. sinensis parts [coarse leaves, flowers, fruits (immature);n = 9] was performed by NMR techniques. NMR (1D and 2D) spectroscopy ambiguously identified and quantified fifty-seven metabolites (Coarse leaves: 35, flowers; 42, immature fruits; 45). The statistical analysis showed apparent tissue-specific similarities (26 metabolites) and variations. Further, HPLC-DAD revealed absolute quantification of catechins, caffeine and theanine among the different parts of C. sinensis. Moreover, cytotoxicity studies of tea tissues against colorectal cancer cell lines showed anticancer potentials. This chemical information and anticancer activity of underutilized C. sinensis parts will help to develop value added nutraceutical and cosmeceutical products.

Feather pulp: a novel substrate useful for proton nuclear magnetic resonance spectroscopy metabolomics and biomarker discovery

Noninvasive biomarkers of stress that are predictive of poultry health are needed. Feather pulp is highly vascularized and represents a potential source of biomarkers that has not been extensively explored. We investigated the feasibility and use of feather pulp for novel biomarker discovery using ¹H-Nuclear Magnetic Resonance Spectroscopy (NMR)-based metabolomics. To this end, high quality NMR metabolomic spectra were obtained from chicken feather pulp extracted using either ultrafiltration (UF) or Bligh-Dyer methanol-chloroform (BD) methods. In total, 121 and 160 metabolites were identified using the UF and BD extraction methods, respectively, with 71 of these common to both methods. The metabolome of feather pulp differed in broiler breeders that were 1-, 23-, and 45-wk-of-age. Moreover, feather pulp was more difficult to obtain from older birds, indicating that age must be considered when targeting feather pulp as a source of biomarkers. The metabolomic profile of feather pulp obtained from 12-day-old broilers administered corticosterone differed from control birds, indicating that the metabolome of feather pulp was sensitive to induced physiological stress. A comparative examination of feather pulp and serum in broilers revealed that the feather pulp metabolome differed from that of serum but provided more information. The study findings show that metabolite biomarkers in chicken feather pulp may allow producers to effectively monitor stress, and to objectively develop and evaluate on-farm mitigations, including practices that reduce stress and enhance bird health.

Rapid quantitative 1H-13C two-dimensional NMR with high precision

Two dimensional (2D) 1H-13C heteronuclear single-quantum correlation (HSQC) spectroscopy has recently been proposed for quantitative determination of typical linear low density polyethylene (LLDPE) with high accuracy. It requires highly precise measurement to achieve further reliable quantification. In this context, this paper aims at determining conditions that allow the achievement of high precision. On the basis of the optimized parameters, two time-saving strategies, nonuniform sampling (NUS) and band-selective HSQC are evaluated on model polyolefins in terms of repeatability. Precision better than 0.3% and 5% for ethylene content (E mol%) and 1-hexene content (H mol%) of the model poly(ethylene-co-1-hexene)s are obtained with 50% NUS or band-selective HSQC. Moreover, dramatic precision enhancements can be achieved with the combination of band-selective HSQC and 50% NUS, in which repeatabilities better than 0.15% and 2.5% for E mol% and H mol% are observed. The experiment times are reduced to about 0.5 h. These methods open important perspectives for rapid, precise and accurate quantitative analysis of complex polymers.