Signature Mapping (SigMa): An efficient approach for processing complex human urine 1H NMR metabolomics data

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.

Comprehensive analysis of alcohol compounds in commercial instant coffee: A validated 1H NMR spectroscopy study within the Islamic paradigm

Coffee, a globally consumed beverage, has raised concerns in Islamic jurisprudence due to the possible presence of alcohol compounds. This research aims to utilise the sensitivity and reliability of 1H NMR spectroscopy in the quantification of alcohol compounds such as ethanol, furfuryl alcohol, and 5-(hydroxymethyl) furfural (HMF) in commercial instant coffee. Analysis of seven products was performed using advanced 1H Nuclear Magnetic Resonance (NMR) spectroscopy together with Statistical Total Correlation Spectroscopy (STOCSY) and Resolution-Enhanced (RED)-STORM. The analysis of the 100 mg sample revealed the absence of ethanol. The amount of furfuryl alcohol and HMF in the selected commercial instant coffee samples was 0.817 μg and 0.0553 μg, respectively. This study demonstrates the utility of 1H NMR spectroscopy in accurate quantification of trace components for various applications.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Twenty Years of 1H NMR Plant Metabolomics: A Way Forward toward Assessment of Plant Metabolites for Constitutive and Inducible Defenses to Biotic Stress

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by ¹H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to ¹H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of ¹H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach

Background and aim

Results from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND).

Methods

Ninety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success.

Results

There were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period.

Conclusion

We identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention

Scope

The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight.

Methods

Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed.

Results

The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate.

Conclusion

The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.

Associations between sheep meat intake frequency and blood plasma levels of metabolites and lipoproteins in healthy Uzbek adults

INTRODUCTION

Uzbekistan is one of the countries with the highest number of diet-related chronic diseases, which is believed to be associated with high animal fat intake. Sheep meat is high in fats (~ 5% in muscle), including saturated and monounsaturated fatty acids, and it contains nearly twice the higher amounts of n-3 polyunsaturated fatty acids and conjugated linoleic acids compared to beef. Nevertheless, sheep meat is considered health promoting by the locals in Uzbekistan and it accounts for around 1/3 of red meat intake in the country.

OBJECTIVES

The aim of this study was to apply a metabolomics approach to investigate if sheep meat intake frequency (SMIF) is associated with alterations in fasting blood plasma metabolites and lipoproteins in healthy Uzbek adults.

METHODS

The study included 263 subjects, 149 females and 114 males. For each subject a food intake questionnaire, including SMIF, was recorded and fasting blood plasma samples were collected for metabolomics. Blood plasma metabolites and lipoprotein concentrations were determined using ¹H NMR spectroscopy.

RESULTS AND CONCLUSION

The results showed that SMIF was confounded by nationality, sex, body mass index (BMI), age, intake frequency of total meat and fish in ascending order (p < 0.01). Multivariate and univariate data analyses showed differences in the levels of plasma metabolites and lipoproteins with respect to SMIF. The effect of SMIF after statistical adjustment by nationality, sex, BMI, age, intake frequency of total meat and fish decreased but remained significant. Pyruvic acid, phenylalanine, ornithine, and acetic acid remained significantly lower in the high SMIF group, whereas choline, asparagine, and dimethylglycine showed an increasing trend. Levels of cholesterol, apolipoprotein A1, as well as low- and high-density lipoprotein subfractions all displayed a decreasing trend with increased SMIF although the difference were not significant after FDR correction.

Molecular profiling of whey permeate reveals new insights into molecular affinities related to industrial unit operations during lactose production

Lactose powder production from whey permeate generates various side-streams. Molecular profiling of these side-streams and lactose powder can help to detect minor compounds affecting lactose crystallization, lactose powder properties and document the composition of the underutilized side-streams. In this study, whey permeate, lactose powder and intermediate streams from trial lactose productions were analyzed using gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy. In total, 110 compounds were identified and 49 were quantified. Linking the molecular profiles to in-process steps revealed differential compositional attenuation by the unit operations. Small molecules (e.g. methanol) and a few larger molecules (e.g. fatty acids) permeated reverse osmosis membrane, while twenty-three compounds (e.g. hydroxypyruvic acid, malonic acid, gluconic acid and ribonic acid) co-crystallized with lactose and ended up in lactose power. These results help to better understand and control lactose powder production and highlights possibilities to develop new food ingredients.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics

Two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) ¹H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D ¹H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D ¹H-¹³C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ_o) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ_o for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D ¹H-¹³C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

A metabolic readout of the urine metabolome of COVID-19 patients

Analysis of urine samples from COVID-19 patients by ¹H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used ¹H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.

Dietary Fish Meal Level and a Package of Choline, β-Glucan, and Nucleotides Modulate Gut Function, Microbiota, and Health in Atlantic Salmon (Salmo salar, L.)

Steatosis and inflammation have been common gut symptoms in Atlantic salmon fed plant rich diets. Choline has recently been identified as essential for salmon in seawater, and β-glucan and nucleotides are frequently used to prevent inflammation. The study is aimed at documenting whether increased fishmeal (FM) levels (8 levels from 0 to 40%) and supplementation (Suppl) with a mixture of choline (3.0 g/kg), β-glucan (0.5 g/kg), and nucleotides (0.5 g/kg) might reduce the symptoms. Salmon (186 g) were fed for 62 days in 16 saltwater tanks before samples were taken from 12 fish per tank for observation of biochemical, molecular, metabolome, and microbiome indicators of function and health. Steatosis but no inflammation was observed. Lipid digestibility increased and steatosis decreased with increasing FM levels and supplementation, seemingly related to choline level. Blood metabolites confirmed this picture. Genes in intestinal tissue affected by FM levels are mainly involved in metabolic and structural functions. Only a few are immune genes. The supplement reduced these FM effects. In gut digesta, increasing FM levels increased microbial richness and diversity, and changed the composition, but only for unsupplemented diets. An average choline requirement of 3.5 g/kg was indicated for Atlantic salmon at the present life stage and under the present condition.

1-2 Drinks Per Day Affect Lipoprotein Composition after 3 Weeks-Results from a Cross-Over Pilot Intervention Trial in Healthy Adults Using Nuclear Magnetic Resonance-Measured Lipoproteins and Apolipoproteins

Alcohol consumption ranging from 1−2 drinks/day associates with a lower risk of coronary heart disease in some studies. The underlying mechanisms are unclear. The Metabolic Imprints of Alcoholic Beverages (MetAl) trial aimed to explore the short-term effects of moderate alcohol consumption on cardiovascular biomarkers. A 2 × 3-week cross-over single-blinded intervention trial investigating the effect of 1−2 drinks/day (~12−24 g) compared with abstention on 1H Nuclear Magnetic Resonance-measured main lipoproteins and subfractions was performed in 26 healthy adults. Volunteers were classified as occasional or habitual drinkers based on their habitual alcohol intakes (<2 or ≥2 drinks/week). Compared with abstention, 1−2 drinks/day increased HDL2a-C (p = 0.004), HDL3-C (p = 0.008), and HDL non-significantly (p = 0.19). Total apoA1 and apoA1 in HDL and its subfractions increased (p < 0.05). Novel findings were a decreased apoB/apoA1 ratio (p = 0.02), and increased HDL2a phospholipid content (p = 0.04). In women alone, the results were similar but attenuated, and LDL-P decreased. Thus, changes in apoA1- and HDL-related biomarkers occur within weeks in moderate drinkers. Compared with abstention, 1−2 drinks/day increased total apoA1 more strongly than HDL-C and increased the cholesterol, apoA1, and phospholipid content of several HDL subfractions. Whether this provides a cardiovascular benefit requires further study. Clinicaltrials.gov: NCT03384147.

Gut Microbiome and Its Cofactors Are Linked to Lipoprotein Distribution Profiles

Increasing evidence indicates that the gut microbiome (GM) plays an important role in dyslipidemia. To date, however, no in-depth characterization of the associations between GM with lipoproteins distributions (LPD) among adult individuals with diverse BMI has been conducted. To determine such associations, we studied blood-plasma LPD, fecal short-chain fatty acids (SCFA) and GM of 262 Danes aged 19–89 years. Stratification of LPD segregated subjects into three clusters displaying recommended levels of lipoproteins and explained by age and body-mass-index. Higher levels of HDL2a and HDL2b were associated with a higher abundance of Ruminococcaceae and Christensenellaceae. Increasing levels of total cholesterol and LDL-1 and LDL-2 were positively associated with Lachnospiraceae and Coriobacteriaceae, and negatively with Bacteroidaceae and Bifidobacteriaceae. Metagenome-sequencing showed a higher abundance of biosynthesis of multiple B-vitamins and SCFA metabolism genes among healthier LPD profiles. Metagenomic-assembled genomes (MAGs) affiliated to Eggerthellaceae and Clostridiales were contributors of these genes and their relative abundance correlated positively with larger HDL subfractions. The study demonstrates that differences in composition and metabolic traits of the GM are associated with variations in LPD among the recruited subjects. These findings provide evidence for GM considerations in future research aiming to shed light on mechanisms of the GM–dyslipidemia axis.

NMR-Based Metabolomics to Decipher the Molecular Mechanisms in the Action of Gut-Modulating Foods

Metabolomics deals with uncovering and characterizing metabolites present in a biological system, and is a leading omics discipline as it provides the nearest link to the biological phenotype. Within food and nutrition, metabolomics applied to fecal samples and bio-fluids has become an important tool to obtain insight into how food and food components may exert gut-modulating effects. This review aims to highlight how nuclear magnetic resonance (NMR)-based metabolomics in food and nutrition science may help us get beyond where we are today in understanding foods’ inherent, or added, biofunctionalities in relation to gut health.

The NMR added value to the green foodomics perspective: Advances by machine learning to the holistic view on food and nutrition

Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modeling a necessity. High-throughput techniques, such as nuclear magnetic resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modeling food-human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients; detect biomarkers of intake and study how they impact the metabolism of different individuals; study the kinetics of compounds in foods or their by-products to detect pathological conditions; and improve the efficiency of in silico models of the metabolic network.

Effect of Supplementing Dairy Goat Diets With Rapeseed Oil or Sunflower Oil on Performance, Milk Composition, Milk Fatty Acid Profile, and in vitro Fermentation Kinetics

The aim of this study was to determine the effect of supplementing dairy goat diets with rapeseed oil and sunflower oil on performance, milk composition, milk fatty acid profile, and in vitro fermentation kinetics. Nine Danish Landrace goats with 42 ± 5 days in milk were allocated to three treatment groups for 42 days. Animals received a basal diet, formulated with 85:15 forage:concentrate ratio, and the basal diet was supplemented with either rapeseed oil or sunflower oil at 4% of dry matter. Goat milk was sampled on days 14, 21, and 42. Milk composition was similar between treatments. From day 14 to day 42, milk yield increased (1.03 vs. 1.34 kg/d), while milk fat (2.72 vs. 1.82 g/d) and total solids (11.2 vs. 9.14 %) were reduced. Compared to control and rapeseed oil, sunflower decreased (P &lt; 0.05) C4:0 (1.56, and 1.67 vs. 1.36 g/100 g) and both oils decreased (P &lt; 0.05) C18:3n3 (0.60 vs. 0.20 and 0.10 g/100g). Rapeseed oil increased (P &lt; 0.05) C18:2 cis9, trans11 compared to control and sunflower oil (0.37 vs. 0.13 and 0.19 g/100 g). Untargeted milk foodomics revealed slightly elevated (P &lt; 0.05) gluconic acid and decreased hippuric acid (P &lt; 0.05) in the milk of oil-fed goats compared to control. In vitro dry matter degradation (63.2 ± 0.02 %) was not affected by dietary treatments, while individual volatile fatty acid proportions, total volatile fatty acids (35.7 ± 2.44 mmol/l), CO2 (18.6 ± 1.15 mol), and CH4 (11.6 ± 1.16 mol) were not affected by dietary treatments. Sunflower oil and rapeseed oil decreased (P &lt; 0.05) total gas production at 24 and 48 h compared with control. Overall, the use of sunflower oil or rapeseed oil at 4% DM inclusion did not compromise animal performance and milk composition.

Saccharomyces cerevisiae does not undergo a quorum sensing-dependent switch of budding pattern

Saccharomyces cerevisiae can alter its morphology to a filamentous form associated with unipolar budding in response to environmental stressors. Induction of filamentous growth is suggested under nitrogen deficiency in response to alcoholic signalling molecules through quorum sensing. To investigate this further, we analysed the budding pattern of S. cerevisiae cells over time under low nitrogen conditions while concurrently measuring cell density and extracellular metabolite concentration. We found that the proportion of cells displaying unipolar budding increased between local cell densities of 4.8 × 10⁶ and 5.3 × 10⁷ cells/ml. This increase in unipolar budding was not reproduced with cells growing at the critical cell density and in conditioned media. Growth under high nitrogen conditions also resulted in increased unipolar budding between local cell densities of 5.2 × 10⁶ and 8.2 × 10⁷ cells/ml, but with differences in metabolite concentration compared to low nitrogen conditions. Neither cell density, metabolite concentration, nor nitrogen deficiency were therefore sufficient to increase unipolar budding. Therefore, by using the budding pattern as an early indicator of filamentous growth, our results suggest that quorum sensing may not control the switch of budding behaviour in S. cerevisiae. Only a high concentration of the putative signalling molecule, 2-phenylethanol, resulted in an increase in unipolar budding. However, this concentration was not physiologically relevant, suggesting toxicity rather than a known quorum sensing mechanism.

Goat Milk Foodomics. Dietary Supplementation of Sunflower Oil and Rapeseed Oil Modify Milk Amino Acid and Organic Acid Profiles in Dairy Goats

The dietary supplementation of vegetable oils is known to improve the dietary energy density as well as milk fatty acid profile; however, the impacts on the milk foodome is largely unknown. This study investigated the effect of two different sources of unsaturated fatty acids, rapeseed oil and sunflower oil, as a feeding supplement on the milk foodome from dairy goats. Nine Danish Landrace goats at 42 ± 5 days in milk were allocated to three treatment groups for 42 days with three animals per group. A control group received a basal diet made of forage and concentrate at an 85:15 ratio. On top of the basal diet, the second and third groups received rapeseed oil or sunflower oil supplements at 4% of dry matter, respectively. Goat milk was sampled on days 14, 21, and 42. The milk foodome was measured using gas chromatography–mass spectrometry and proton nuclear magnetic resonance spectroscopy. The milk levels of 2-hydroxyisovaleric acid, oxaloacetic acid, and taurine were higher in the milk from goats fed with sunflower oil compared to the control group. More glucose-1-phosphate was found in the milk from goats fed with rapeseed oil compared to the control group. Amino acids, valine and tyrosine, and 2-hydroxyisovaleric acid and oxaloacetic acid were higher in the sunflower group compared to the rapeseed group, while the milk from the rapeseed-fed goats had greater levels of ethanol and 2-oxoglutaric acid compared to the sunflower group. Thus, results show that foodomics is suitable for studying how milk chemistry changes as a function of feeding regime.

Urinary and plasma metabolome of farm mink (Neovison vison) after an intervention with raw or cooked poultry offal: a 1H NMR investigation

The introduction of high amounts of cooked poultry offal in mink feed has been associated with health problems in growing mink. Cooking mink feed is a convenient way of reducing microbiological activity, but it may have a negative effect on raw material quality and animal welfare. This study investigates growth and health of mink fed raw or cooked poultry offal and describes urinary and blood plasma metabolic changes related to the feeding. A total of 65 male mink were divided in three feeding groups, two fed cooked offal and one group fed raw offal, and the plasma and urine samples were collected at 3 time points during the growth. Both bio-fluids and feed samples were measured by ¹H NMR spectroscopy and resulted metabolomics data were analysed using univariate and multivariate statistical methods that revealed dominating effect of the mink growth stages and to a less extent the feeding regime. Metabolome differences in relation to low body mass index (BMI) and kidney lesions were observed in plasma. Disease and decrease in BMI was associated with high creatinine and dimethylglycine content in plasma. These molecules were also particularly indicative of the cooked feeds. Moreover, low urinary taurine levels were also associated with disease and low BMI. Individual mink appeared to show negative effects of the cooked feed diet, including impaired growth and gross pathological lesions involving the kidneys. This may be related to the absorption of essential metabolites such as amino acids and fats, necessary for mink growth, that are negatively impacted by the cooking process.

Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using ¹H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the ¹H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q² of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized ¹H NMR spectral recordings.

Human Fecal Metabolome Reflects Differences in Body Mass Index, Physical Fitness, and Blood Lipoproteins in Healthy Older Adults

This study investigated how body mass index (BMI), physical fitness, and blood plasma lipoprotein levels are related to the fecal metabolome in older adults. The fecal metabolome data were acquired using proton nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry on 163 healthy older adults (65-80 years old, 80 females and 83 males). Overweight and obese subjects (BMI ≥ 27) showed higher levels of fecal amino acids (AAs) (valine, alanine, and phenylalanine) compared to normal-weight subjects (BMI ≤ 23.5). Adults classified in the high-fitness group displayed slightly lower concentrations of fecal short-chain fatty acids, propionic acid, and AAs (methionine, leucine, glutamic acid, and threonine) compared to the low-fitness group. Subjects with lower levels of cholesterol in low-density lipoprotein particles (LDLchol, ≤2.6 mmol/L) displayed higher fecal levels of valine, glutamic acid, phenylalanine, and lactic acid, while subjects with a higher level of cholesterol in high-density lipoprotein particles (HDLchol, ≥2.1 mmol/L) showed lower fecal concentration of isovaleric acid. The results from this study suggest that the human fecal metabolome, which primarily represents undigested food waste and metabolites produced by the gut microbiome, carries important information about human health and should be closely integrated to other omics data for a better understanding of the role of the gut microbiome and diet on human health and metabolism.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D

NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using ¹H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.

The plasma metabolome of Atlantic salmon as studied by 1H NMR spectroscopy using standard operating procedures: effect of aquaculture location and growth stage

INTRODUCTION

Metabolomics applications to the aquaculture research are increasing steadily. The use of standardized proton nuclear magnetic resonance (¹H NMR) spectroscopy can provide the aquaculture industry with an unbiased, reproducible, and high-throughput screening tool, which can help to diagnose nutritional and disease-related metabolic disorders in farmed fish.

OBJECTIVE

Standard operating procedures developed for analysing (human) plasma by ¹H NMR were applied to fingerprint the metabolome in plasma samples collected from Atlantic salmon. The aim was to explore the metabolome of salmon plasma in relation to growth stage and sampling site.

METHODS

A total of 72 salmon were collected from three aquaculture sites in Norway (Lat. 65, 67, and 70 °N) and over two sampling events (December 2017 and November 2018). Plasma drawn from each salmon was measured by ¹H NMR and metabolites were quantified using the SigMa software. The NMR data was analysed by principal component analysis (PCA) and ANOVA-simultaneous component analysis (ASCA).

RESULTS

Important metabolic differences were evidenced, with adult salmon having a much higher content of very low-density lipoproteins and cholesterol in their plasma, while smolts displayed significantly higher levels of propylene glycol. Overall, 24% of the metabolite variation was due to the growth stage, whereas 12% of the metabolite variation was related to the aquaculture site and practice (p < 0.001).

CONCLUSION

This study provides a baseline investigation of the plasma metabolome of the Atlantic salmon and demonstrates how ¹H NMR metabolomics can be used in future investigations for comparing aquaculture practices and their influence on the fish metabolome.

New software tools, databases, and resources in metabolomics: updates from 2020

BACKGROUND

Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success.

AIM OF THE REVIEW

In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine ¹H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

Plasma Metabolomics to Evaluate Progression of Necrotising Enterocolitis in Preterm Pigs

Necrotising enterocolitis (NEC) is a microbiome-dependent gut disease in preterm infants in early life. Antibiotic treatment is a common intervention for NEC. How NEC lesions, with or without antibiotics, affect plasma metabolome was explored in this study. Formula-fed preterm pigs were used as a model for human NEC and treated with saline, parenteral or oral antibiotics (n = 15-17) for four days after delivery. Gut tissues were collected for evaluation of NEC-like lesions and plasma for metabolomic analysis by proton nuclear magnetic resonance spectroscopy (¹H-NMR). Metabolites were annotated, quantified and subjected to statistical modelling to delineate the effects of NEC and antibiotic treatment. Presence of severe NEC lesions, not antibiotic treatment, was the main drive for plasma metabolite changes. Relative to other pigs, pigs with severe NEC lesions had higher levels of alanine, histidine and myo-inositol, and lower levels of 3-hydroxybutyric acid and isobutyric acid. Across NEC lesion states (healthy, mild, severe), antibiotics directly affected only a few metabolites (tryptophan, 3-phenyllactic acid). Together and independently, NEC and antibiotic treatment affected circulating metabolites in preterm pigs. Amino acids and plasma metabolites, partly related to the gut microbiome, may be helpful to monitor progression of NEC lesions after proper validation.

A Metabolomic Approach to Beer Characterization

The consumers’ interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of “beeromics” has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. ¹H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

A Multi-Omics Approach Reveals New Signatures in Obese Allergic Asthmatic Children

Background: Asthma is a multifactorial condition where patients with identical clinical diagnoses do not have the same clinical history or respond to treatment. This clinical heterogeneity is reflected in the definition of two main endotypes. We aimed to explore the metabolic and microbiota signatures that characterize the clinical allergic asthma phenotype in obese children. Methods: We used a multi-omics approach combining clinical data, plasma and fecal inflammatory biomarkers, metagenomics, and metabolomics data in a cohort of allergic asthmatic children. Results: We observed that the obese allergic asthmatic phenotype was markedly associated with higher levels of leptin and lower relative proportions of plasma acetate and a member from the Clostridiales order. Moreover, allergic children with a worse asthma outcome showed higher levels of large unstained cells, fecal D lactate and D/L lactate ratio, and with a higher relative proportion of plasma creatinine and an unclassified family member from the RF39 order belonging to the Mollicutes class. Otherwise, children with persistent asthma presented lower levels of plasma citrate and dimethylsulfone. Conclusion: Our integrative approach shows the molecular heterogeneity of the allergic asthma phenotype while highlighting the use of omics technologies to examine the clinical phenotype at a more holistic level.

An NMR Metabolomics Approach to Investigate Factors Affecting the Yoghurt Fermentation Process and Quality

A great number of factors can influence milk fermentation for yoghurt production such as fermentation conditions, starter cultures and milk characteristics. It is important for dairy companies to know the best combinations of these parameters for a controlled fermentation and for the desired qualities of yoghurt. This study investigates the use of a ¹H-NMR metabolomics approach to monitor the changes in milk during fermentation from time 0 to 24 h, taking samples every hour in the first 8 h and then at the end-point at 24 h. Three different starter cultures (L. delbrueckii ssp. bulgaricus, S. thermophilus and their combination) were used and two different heat treatments (99 or 105 °C) were applied to milk. The results clearly show the breakdown of proteins and lactose as well as the concomitant increase in acetate, lactate and citrate during fermentation. Formate is found at different initial concentrations depending on the heat treatment of the milk and its different time trajectory depends on the starter cultures: Lactobacillus cannot produce formate, but needs it for growth, whilst Streptococcus is able to produce formate from pyruvate, therefore promoting the symbiotic relationship between the two strains. On the other hand, Lactobacillus can hydrolyze milk proteins into amino acids, enriching the quality of the final product. In this way, better insight into the protocooperation of lactic acid bacteria strains and information on the impact of a greater heat treatment in the initial matrix were obtained. The global chemical view on the fermentations provided using NMR is key information for yoghurt producers and companies producing starter cultures.

MCR-ALS analysis of 1H NMR spectra by segments to study the zebrafish exposure to acrylamide

Metabolomics is currently an important field within bioanalytical science and NMR has become a key technique for drawing the full metabolic picture. However, the analysis of ¹H NMR spectra of metabolomics samples is often very challenging, as resonances usually overlap in crowded regions, hindering the steps of metabolite profiling and resonance integration. In this context, a pre-processing method for the analysis of 1D ¹H NMR data from metabolomics samples is proposed, consisting of the blind resolution and integration of all resonances of the spectral dataset by multivariate curve resolution-alternating least squares (MCR-ALS). The resulting concentration estimates can then be examined with traditional chemometric methods such as principal component analysis (PCA), ANOVA-simultaneous component analysis (ASCA), and partial least squares-discriminant analysis (PLS-DA). Since MCR-ALS does not require the use of spectral templates, the concentration estimates for all resonances are obtained even before being assigned. Consequently, the metabolomics study can be performed without neglecting any relevant resonance. In this work, the proposed pipeline performance was validated with 1D ¹H NMR spectra from a metabolomics study of zebrafish upon acrylamide (ACR) exposure. Remarkably, this method represents a framework for the high-throughput analysis of NMR metabolomics data that opens the way for truly untargeted NMR metabolomics analyses. Graphical abstract.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet

OBJECTIVES

The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss.

METHODS

The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight.

RESULTS

Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research.

CONCLUSIONS

Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.