Rapid, Reproducible, Quantifiable NMR Metabolomics: Methanol and Methanol: Chloroform Precipitation for Removal of Macromolecules in Serum and Whole Blood

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis

The role of the Toll-like receptor 4 (TLR4) is of recognising intracellular and extracellular pathogens and of activating the immune response. This process can be compromised by single nucleotide polymorphisms (SNPs) which might affect the activity of several TLRs. The aim of this study is of ascertaining whether SNPs in the TLR4 of Bubalus bubalis infected by Brucella abortus, compromise the protein functionality. For this purpose, a computational analysis was performed. Next, computational predictions were confirmed by performing genotyping analysis. Finally, NMR-based metabolomics analysis was performed to identify potential biomarkers for brucellosis. The results indicate two SNPs (c. 672 A > C and c. 902 G > C) as risk factor for brucellosis in Bubalus bubalis, and three metabolites (lactate, 3-hydroxybutyrate and acetate) as biological markers for predicting the risk of developing the disease. These metabolites, together with TLR4 structural modifications in the MD2 interaction domain, are a clear signature of the immune system alteration during diverse Gram-negative bacterial infections. This suggests the possibility to extend this study to other pathogens, including Mycobacterium tuberculosis. In conclusion, this study combines multidisciplinary approaches to evaluate the biological and structural effects of SNPs on protein function.

Kidney function as a key driver of the pharmacokinetic response to high-dose L-carnitine in septic shock

STUDY OBJECTIVE

Levocarnitine (L-carnitine) has shown promise as a metabolic-therapeutic for septic shock, where mortality approaches 40%. However, high-dose (≥ 6 grams) intravenous supplementation results in a broad range of serum concentrations. We sought to describe the population pharmacokinetics (PK) of high-dose L-carnitine, test various estimates of kidney function, and assess the correlation of PK parameters with pre-treatment metabolites in describing drug response for patients with septic shock.

DESIGN

Population PK analysis was done with baseline normalized concentrations using nonlinear mixed effect models in the modeling platform Monolix. Various estimates of kidney function, patient demographics, dose received, and organ dysfunction were tested as population covariates.

DATA SOURCE

We leveraged serum samples and metabolomics data from a phase II trial of L-carnitine in vasopressor-dependent septic shock. Serum was collected at baseline (T0); end-of-infusion (T12); and 24, 48, and 72 h after treatment initiation.

PATIENTS AND INTERVENTION

Patients were adaptively randomized to receive intravenous L-carnitine (6 grams, 12 grams, or 18 grams) or placebo.

MEASUREMENTS AND MAIN RESULTS

The final dataset included 542 serum samples from 130 patients randomized to L-carnitine. A two-compartment model with linear elimination and a fixed volume of distribution (17.1 liters) best described the data and served as a base structural model. Kidney function estimates as a covariate on the elimination rate constant (k) reliably improved model fit. Estimated glomerular filtration rate (eGFR), based on the 2021 Chronic Kidney Disease Epidemiology collaboration (CKD-EPI) equation with creatinine and cystatin C, outperformed creatinine clearance (Cockcroft-Gault) and older CKD-EPI equations that use an adjustment for self-identified race.

CONCLUSIONS

High-dose L-carnitine supplementation is well-described by a two-compartment population PK model in patients with septic shock. Kidney function estimates that leverage cystatin C provided superior model fit. Future investigations into high-dose L-carnitine supplementation should consider baseline metabolic status and dose adjustments based on renal function over a fixed or weight-based dosing paradigm.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and ¹H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups.

DESIGN

We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock.

SETTING

We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients.

PATIENTS

Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001).

CONCLUSIONS

Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

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.

Redox Potential Correlates with Changes in Metabolite Concentrations Attributable to Pathways Active in Oxidative Stress Response in Swine Traumatic Shock

INTRODUCTION

Oxidation-reduction (redox) reactions, and the redox potential (RP) that must be maintained for proper cell function, lie at the heart of physiologic processes in critical illness. Imbalance in RP reflects systemic oxidative stress, and whole blood RP measures have been shown to correlate with oxygen debt level over time in swine traumatic shock. We hypothesize that RP measures reflect changing concentrations of metabolites involved in oxidative stress. To test this hypothesis, we compared blood and urine RP with concentrations of multiple metabolites in a swine traumatic shock model to identify meaningful RP-metabolite relationships.

METHODS

Seven swine were subjected to traumatic shock. Mixed venous (MV) RP, urine RP, and concurrent MV and urine metabolite concentrations were assessed at baseline, max O 2 Debt (80 mL/kg), end resuscitation, and 2 h post-resuscitation. RP was measured at collection via open circuit potential using nanoporous gold electrodes with Ag/AgCl reference and a ParstatMC potentiostat. Metabolite concentrations were measured by quantitative 1 H-NMR spectroscopy. MV and urine RP were compared with time-matched metabolites across all swine. LASSO regression with leave-one-out cross validation was used to determine meaningful RP/metabolite relationships. Metabolites had to maintain magnitude and direction of coefficients across 6 or more swine to be considered as having a meaningful relationship. KEGG IDs of these metabolites were uploaded into Metscape for pathway identification and evaluation for physiologic function.

RESULTS

Meaningful metabolite relationships (and mean coefficients across cross-validation folds) with MV RP included: choline (-6.27), ATP (-4.39), glycine (5.93), ADP (1.84), glucose (15.96), formate (-13.09), pyruvate (6.18), and taurine (-7.18). Relationships with urine RP were: betaine (4.81), urea (4.14), glycine (-2.97), taurine (10.32), 3-hydroxyisobutyrate (-7.67), N-phenylacetylglycine, PAG (-14.52), hippurate (12.89), and formate (-5.89). These meaningful metabolites were found to scavenge extracellular peroxide (pyruvate), inhibit ROS and activate cellular antioxidant defense (taurine), act as indicators of antioxidant mobilization against oxidative stress (glycine + PAG), and reflect renal hydroxyl radical trapping (hippurate), among other activities.

CONCLUSIONS

Real-time RP measures demonstrate significant relationships with metabolites attributable to metabolic pathways involved in systemic responses to oxidative stress, as well as those involved in these processes. These data support RP measures as a feasible, biologically relevant marker of oxidative stress. As a direct measure of redox state, RP may be a useful biomarker and clinical tool in guiding diagnosis and therapy in states of increased oxidative stress and may offer value as a marker for organ injury in these states as well.

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.

Serum Levels of Acylcarnitines and Amino Acids Are Associated with Liberation from Organ Support in Patients with Septic Shock

Sepsis-induced metabolic dysfunction is associated with mortality, but the signatures that differentiate variable clinical outcomes among survivors are unknown. Our aim was to determine the relationship between host metabolism and chronic critical illness (CCI) in patients with septic shock. We analyzed metabolomics data from mechanically ventilated patients with vasopressor-dependent septic shock from the placebo arm of a recently completed clinical trial. Baseline serum metabolites were measured by liquid chromatography-mass spectrometry and ¹H-nuclear magnetic resonance. We conducted a time-to-event analysis censored at 28 days. Specifically, we determined the relationship between metabolites and time to extubation and freedom from vasopressors using a competing risk survival model, with death as a competing risk. We also compared metabolite concentrations between CCI patients, defined as intensive care unit level of care ≥ 14 days, and those with rapid recovery. Elevations in two acylcarnitines and four amino acids were related to the freedom from organ support (subdistributional hazard ratio < 1 and false discovery rate < 0.05). Proline, glycine, glutamine, and methionine were also elevated in patients who developed CCI. Our work highlights the need for further testing of metabolomics to identify patients at risk of CCI and to elucidate potential mechanisms that contribute to its etiology.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (¹H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

Pharmacometabolomics identifies candidate predictor metabolites of an L-carnitine treatment mortality benefit in septic shock

Sepsis‐induced metabolic dysfunction contributes to organ failure and death. L‐carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor‐dependent septic shock demonstrated a non‐significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90‐day mortality benefit from L‐carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L‐carnitine dose, on 90‐day mortality was determined by logistic regression. A grid‐search analysis maximizing the Z‐statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L‐carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan‐Meier estimate. Accounting for L‐carnitine treatment and dose, 11 ¹H‐NMR metabolites and 12 acylcarnitines were independent predictors of 90‐day mortality. Based on the grid‐search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L‐carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L‐carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90‐day sepsis mortality. Our proof‐of‐concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.

Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability

PURPOSE

Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK.

METHODS

This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m² paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (T_c>0.05) and maximum concentration (C_max). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with T_c>0.05 and C_max using Pearson correlation followed by stepwise linear regression.

RESULTS

Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with T_c>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with C_max (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with T_c>0.05, while betaine and lactate were associated with C_max (all p < 0.05).

CONCLUSION

This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

Serum Metabonomic Research of the Anti-Hypertensive Effects of Ogaja on Spontaneously Hypertensive Rats

Our previous studies have shown that Ogaja Acanthopanax sessiliflorus has an important role in decreasing blood pressure, but its biochemical change characteristic has not been clarified completely at the metabolic level. Therefore, in this study, a combination method of nuclear magnetic resonance (NMR) spectroscopy-based metabonomics and multivariate statistical analyses was employed to explore the metabolic changes of serum samples from spontaneously hypertensive rats treated with Ogaja extracts. In the results of multivariate statistical analysis, the spontaneously hypertensive rat (SHR) groups treated with Ogaja were separated from the SHR group. The group of SHR treated with 200 mg/kg Ogaja was clustered with the positive control (captopril) group, and the 400 and 600 mg/kg Ogaja treatment SHR groups were clustered together. Quantified metabolites were statistically analyzed to find the metabolites showing the effects of Ogaja. Succinate and betaine had variable importance in projection (VIP) scores over 2.0. Succinate, which is related to renin release, and betaine, which is related to lowering blood pressure, increased dose-dependently.

Interaction between MyD88, TIRAP and IL1RL1 against Helicobacter pylori infection

The Toll-interleukin 1 receptor superfamily includes the genes interleukin 1 receptor-like 1 (IL1RL1), Toll like receptors (TLRs), myeloid differentiation primary-response 88 (MyD88), and MyD88 adaptor-like (TIRAP). This study describes the interaction between MyD88, TIRAP and IL1RL1 against Helicobacter pylori infection. Cases and controls were genotyped at the polymorphic sites MyD88 rs6853, TIRAP rs8177374 and IL1RL1 rs11123923. The results show that specific combinations of IL1RL1-TIRAP (AA-CT; P: 2,8 × 10^–17) and MyD88-TIRAP-IL1RL1 (AA-CT-AA; P: 1,4 × 10^–8) – but not MyD88 alone—act synergistically against Helicobacter pylori. Nuclear magnetic resonance (NMR) clearly discriminates cases from controls by highlighting significantly different expression levels of several metabolites (tyrosine, tryptophan, phenylalanine, branched-chain amino acids, short chain fatty acids, glucose, sucrose, urea, etc.). NMR also identifies the following dysregulated metabolic pathways associated to Helicobacter pylori infection: phenylalanine and tyrosine metabolism, pterine biosynthesis, starch and sucrose metabolism, and galactose metabolism. Furthermore, NMR discriminates between the cases heterozygous at the IL1RL1 locus from those homozygous at the same locus. Heterozygous patients are characterized by high levels of lactate, and IL1RL1—both associated with anti-inflammatory activity—and low levels of the pro-inflammatory molecules IL-1β, TNF-α, COX-2, and IL-6.

Diagnosis of papillary thyroid carcinoma by 1H NMR spectroscopy-based metabolomic analysis of whole blood

The incidence rate of thyroid carcinoma, especially papillary thyroid carcinoma (PTC), has increased significantly over time. As a primary pathway for metastasis, the lymphatic system is an important prognostic factor for PTC patients. Although the metabolic changes in PTC patients have been investigated in extensive studies, few studies focused on the whole blood metabolic profiling of PTC patients. In this study, we investigated the ¹H NMR-based metabolic profiles of whole blood samples that were obtained from healthy individuals and PTC patients, with or without lymph node metastasis. The estimation of the predictive potential of metabolites was evaluated using multivariate statistical analyses, which revealed that the whole blood carries information that is sufficient for distinguishing between PTC patients and healthy individuals. However, PTC patients were not well classified as positive or negative according to the lymph nodes. We did not find a metabolite that could discriminate the presence of lymph node metastasis. Further studies with larger sample sizes are needed to elucidate significant metabolites to indicate the presence of lymph node metastasis in patients with PTC.

A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite's true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

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.

A Multivariate Metabolomics Method for Estimating Platelet Mitochondrial Oxygen Consumption Rates in Patients with Sepsis

Background: Sepsis-induced alterations in mitochondrial function contribute to organ dysfunction and mortality. Measuring mitochondrial function in vital organs is neither feasible nor practical, highlighting the need for non-invasive approaches. Mitochondrial function may be reflected in the concentrations of metabolites found in platelets and whole blood (WB) samples. We proposed to use these as alternates to indirectly estimate platelet mitochondrial oxygen consumption rate (mOCR) in sepsis patients. Methods: We determined the relationships between platelet mOCR and metabolites in both platelets and WB, as measured by quantitative ¹H-NMR metabolomics. The associations were identified by building multiple linear regression models with stepwise forward-backward variable selection. We considered the models to be significant with an ANOVA test (p-value ≤ 0.05) and a positive predicted-R². Results: The differences in adjusted-R² and ANOVA p-values (platelet adj-R²: 0.836 (0.0003), 0.711 (0.0004) vs. WB adj-R²: 0.428 (0.0079)) from the significant models indicate the platelet models were more associated with platelet mOCR. Conclusions: Our data suggest there are groups of metabolites in WB (leucine, acetylcarnitine) and platelets (creatine, ADP, glucose, taurine) that are associated with platelet mOCR. Thus, WB and platelet metabolites could be used to estimate platelet mOCR.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping

In this study we assess multiple sample preparation methods for UHPLC-MS metabolic phenotyping analysis of human urine and plasma. All methods are discussed in terms of metabolite and lipid coverage and reproducibility.

Serum amino acid concentrations and clinical outcomes in smokers: SPIROMICS metabolomics study

Metabolomics is an emerging science that can inform pathogenic mechanisms behind clinical phenotypes in COPD. We aimed to understand disturbances in the serum metabolome associated with respiratory outcomes in ever-smokers from the SPIROMICS cohort. We measured 27 serum metabolites, mostly amino acids, by ¹H-nuclear magnetic resonance spectroscopy in 157 white ever-smokers with and without COPD. We tested the association between log-transformed metabolite concentrations and one-year incidence of respiratory exacerbations after adjusting for age, sex, current smoking, body mass index, diabetes, inhaled or oral corticosteroid use, study site and clinical predictors of exacerbations, including FEV₁% predicted and history of exacerbations. The mean age of participants was 53.7 years and 58% had COPD. Lower concentrations of serum amino acids were independently associated with 1-year incidence of respiratory exacerbations, including tryptophan (β = −4.1, 95% CI [−7.0; −1.1], p = 0.007) and the branched-chain amino acids (leucine: β = −6.0, 95% CI [−9.5; −2.4], p = 0.001; isoleucine: β = −5.2, 95% CI [−8.6; −1.8], p = 0.003; valine: β = −4.1, 95% CI [−6.9; −1.4], p = 0.003). Tryptophan concentration was inversely associated with the blood neutrophil-to-lymphocyte ratio (p = 0.03) and the BODE index (p = 0.03). Reduced serum amino acid concentrations in ever-smokers with and without COPD are associated with an increased incidence of respiratory exacerbations.

Selective Amino Acid-Only in Vivo NMR: A Powerful Tool To Follow Stress Processes

In vivo NMR of small ¹³C-enriched aquatic organisms is developing as a powerful tool to detect and explain toxic stress at the biochemical level. Amino acids are a very important category of metabolites for stress detection as they are involved in the vast majority of stress response pathways. As such, they are a useful proxy for stress detection in general, which could then be a trigger for more in-depth analysis of the metabolome. ¹H-¹³C heteronuclear single quantum coherence (HSQC) is commonly used to provide additional spectral dispersion in vivo and permit metabolite assignment. While some amino acids can be assigned from HSQC, spectral overlap makes monitoring them in vivo challenging. Here, an experiment typically used to study protein structures is adapted for the selective detection of amino acids inside living Daphnia magna (water fleas). All 20 common amino acids can be selectively detected in both extracts and in vivo. By monitoring bisphenol-A exposure, the in vivo amino acid-only approach identified larger fluxes in a greater number of amino acids when compared to published works using extracts from whole organism homogenates. This suggests that amino acid-only NMR of living organisms may be a very sensitive tool in the detection of stress in vivo and is highly complementary to more traditional metabolomics-based methods. The ability of selective NMR experiments to help researchers to "look inside" living organisms and only detect specific molecules of interest is quite profound and paves the way for the future development of additional targeted experiments for in vivo research and monitoring.