A metabonomic approach to early prognostic evaluation of experimental sepsis by (1)H NMR and pattern recognition

Advances in metabolomics in critically ill patients with sepsis and septic shock

Sepsis is associated with high morbidity and mortality rates in hospitalized patients. This condition has a complex pathophysiology and can swiftly progress to the severe form of septic shock, which can lead to organ dysfunction, organ failure, and death. Metabolomics has transformed the clinical and research topography of sepsis, with application to prognosis, diagnosis, and risk assessment. Metabolomics involves detecting and analyzing levels of metabolites in blood (plasma, serum, and/or erythrocytes) and urine; when applied in sepsis, this technology can improve our understanding of the pathogenesis of the disease and aid in better disease management by identifying early biomarkers. For this review article, "metabolomics," "sepsis," and "septic shock" were keywords used to search records in various databases including PubMed and Scopus from their inception until December 2023. This review article summarizes information regarding metabolic profiling performed in sepsis and septic shock and illustrates how metabolomics is advancing the diagnosis and prognosis of patients with sepsis.

Serum Metabolomic Profiles in Critically Ill Patients with Shock on Admission to the Intensive Care Unit

Inflammatory processes are common in intensive care (ICU) patients and can induce multiple changes in metabolism, leading to increased risks of morbidity and mortality. Metabolomics enables these modifications to be studied and identifies a patient’s metabolic profile. The objective is to precise if the use of metabolomics at ICU admission can help in prognostication. This is a prospective ex-vivo study, realized in a university laboratory and a medico-surgical ICU. Metabolic profiles were analyzed by proton nuclear magnetic resonance. Using multivariable analysis, we compared metabolic profiles of volunteers and ICU patients divided into predefined subgroups: sepsis, septic shock, other shock and ICU controls. We also assessed possible correlations between metabolites and mortality. One hundred and eleven patients were included within 24 h of ICU admission, and 19 healthy volunteers. The ICU mortality rate was 15%. Metabolic profiles were different in ICU patients compared to healthy volunteers (p < 0.001). Among the ICU patients, only the subgroup of patients with septic shock had significant differences compared to the ICU control patients in several metabolites: pyruvate, lactate, carnitine, phenylalanine, urea, creatine, creatinine and myo-inositol. However, there was no correlation between these metabolite profiles and mortality. On the first day of ICU admission, we observed changes in some metabolic products in patients with septic shock, suggesting increased anaerobic glycolysis, proteolysis, lipolysis and gluconeogenesis. These changes were not correlated with prognosis.

Identifying potential biomarkers and therapeutic targets for dogs with sepsis using metabolomics and lipidomics analyses

Sepsis is a diagnostic and therapeutic challenge and is associated with morbidity and a high risk of death. Metabolomic and lipidomic profiling in sepsis can identify alterations in metabolism and might provide useful insights into the dysregulated host response to infection, but investigations in dogs are limited. We aimed to use untargeted metabolomics and lipidomics to characterize metabolic pathways in dogs with sepsis to identify therapeutic targets and potential diagnostic and prognostic biomarkers. In this prospective observational cohort study, we examined the plasma metabolomes and lipidomes of 20 healthy control dogs and compared them with those of 21 client-owned dogs with sepsis. Patient data including signalment, physical exam findings, clinicopathologic data and clinical outcome were recorded. Metabolites were identified using an untargeted mass spectrometry approach and pathway analysis identified multiple enriched metabolic pathways including pyruvaldehyde degradation; ketone body metabolism; the glucose-alanine cycle; vitamin-K metabolism; arginine and betaine metabolism; the biosynthesis of various amino acid classes including the aromatic amino acids; branched chain amino acids; and metabolism of glutamine/glutamate and the glycerophospholipid phosphatidylethanolamine. Metabolites were identified with high discriminant abilities between groups which could serve as potential biomarkers of sepsis including 13,14-Dihydro-15-keto Prostaglandin A₂; 12(13)-DiHOME (12,13-dihydroxy-9Z-octadecenoic acid); and 9-HpODE (9-Hydroxyoctadecadienoic acid). Metabolites with higher abundance in samples from nonsurvivors than survivors included 3-(2-hydroxyethyl) indole, indoxyl sulfate and xanthurenic acid. Untargeted lipidomic profiling revealed multiple sphingomyelin species (SM(d34:0)+H; SM(d36:0)+H; SM(d34:0)+HCOO; and SM(d34:1D3)+HCOO); lysophosphatidylcholine molecules (LPC(18:2)+H) and lipophosphoserine molecules (LPS(20:4)+H) that were discriminating for dogs with sepsis. These biomarkers could aid in the diagnosis of dogs with sepsis, provide prognostic information, or act as potential therapeutic targets.

[Metabolomic changes of neonatal sepsis: an exploratory clinical study]

OBJECTIVES

To study the metabolic mechanism of neonatal sepsis at different stages by analyzing the metabolic pathways involving the serum metabolites with significant differences in neonates with sepsis at different time points after admission.

METHODS

A total of 20 neonates with sepsis who were hospitalized in the Department of Neonatology, Hunan Provincial People's Hospital, from January 1, 2019 to January 1, 2020 were enrolled as the sepsis group. Venous blood samples were collected on days 1, 4, and 7 after admission. Ten healthy neonates who underwent physical examination during the same period were enrolled as the control group. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used for the metabonomic analysis of serum samples to investigate the change in metabolomics in neonates with sepsis at different time points.

RESULTS

On day 1 after admission, the differentially expressed serum metabolites between the sepsis and control groups were mainly involved in the biosynthesis of terpenoid skeleton. For the sepsis group, the differentially expressed serum metabolites between days 1 and 4 after admission were mainly involved in pyruvate metabolism, and those between days 4 and 7 after admission were mainly involved in the metabolism of cysteine and methionine. The differentially expressed serum metabolites between days 1 and 7 after admission were mainly involved in ascorbic acid metabolism.

CONCLUSIONS

The metabolic mechanism of serum metabolites varies at different stages in neonates with sepsis and is mainly associated with terpenoid skeleton biosynthesis, pyruvate metabolism, cysteine/methionine metabolism, and ascorbic acid metabolism.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Cow mastitis, with high incidence rate and complex cause of disease, is one of the main diseases that affect the development of dairy industry in the world. Clinical mastitis and subclinical mastitis caused by Staphylococcus aureus, Escherichia coli, Streptococcus, and other pathogens have a huge potential safety hazard to food safety and the rapid development of animal husbandry. The economic loss caused by cow mastitis is billions of dollars every year in the world. In recent years, the omics technology has been widely used in animal husbandry with the continuous breakthrough of sequencing technology and the continuous reduction of sequencing cost. For dairy cow mastitis, the traditional diagnostic technique, such as histopathological screening, somatic cell count, milk pH test, milk conductivity test, enzyme activity test, and infrared thermography, are difficult to fully and comprehensively clarify its pathogenesis due to their own limitations. Metabolomics technology is an important part of system biology, which can simultaneously analyze all low molecular weight metabolites such as amino acids, lipids, carbohydrates under the action of complex factors including internal and external environment and in a specific physiological period accurately and efficiently, and then clarify the related metabolic pathways. Metabolomics, as the most downstream of gene expression, can amplify the small changes of gene and protein expression at the level of metabolites, which can more fully reflect the cell function. The application of metabolomics technology in cow mastitis can analyze the hetero metabolites, identify the related biomarkers, and reveal the physiological and pathological changes of cow mammary gland, so as to provide valuable reference for the prediction, diagnosis, and treatment of mastitis. The research progress of metabolomics technology in cow mastitis in recent years was reviewed, in order to provide guidance for the development of cow health and dairy industry safety in this manuscript.

Title NMR-based metabolic profiling provides diagnostic and prognostic information in critically ill children with suspected infection

Sepsis, defined as life-threatening organ dysfunction caused by infection is difficult to distinguish clinically from infection or post-operative inflammation. We hypothesized that in a heterogeneous group of critically ill children, there would be different metabolic profiles between post-operative inflammation, bacterial and viral infection and infection with or without organ dysfunction. 1D ¹H nuclear magnetic resonance spectra were acquired in plasma samples from critically ill children. We included children with bacterial (n = 25) and viral infection (n = 30) and controls (n = 58) (elective cardiac surgery without infection). Principal component analysis was used for data exploration and partial least squares discriminant analysis models for the differences between groups. Area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Univariate analysis demonstrated differences between controls and bacterial and viral infection. There was excellent discrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination between bacterial and viral (AUC = 0.78). There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and infection with no organ dysfunction. In critically ill children, NMR metabolomics differentiates well between those with a post-operative inflammation but no infection, and those with infection (bacterial and viral), and between sepsis and infection.

Dynamic Alterations of Brain Injury, Functional Recovery, and Metabolites Profile after Cerebral Ischemia/Reperfusion in Rats Contributes to Potential Biomarkers

Cerebral ischemia-reperfusion (I/R) is characterized by initial transient cerebral ischemia followed by reperfusion. Various pathophysiological processes are involved in brain injury and functional recovery during cerebral I/R. There are few studies on dynamic metabolic process after cerebral I/R. The present study was to observe dynamic alteration of brain injury, functional recovery, and metabolites after cerebral I/R in rats and discover potential metabolic markers. The cerebral I/R model was established by middle cerebral artery occlusion (MCAO) for 90 min, following reperfusion in rats. The results of cerebral infarction area, cerebral edema, and behavior test showed that there were dynamic changes in brain injury and functional recovery at different periods after cerebral I/R. Further analysis showed that the brain injury was severe on the first day of cerebral I/R, and there was a significant functional recovery from the 7th day of cerebral I/R, followed by an aggravation trend of brain injury from the days 7 to 28. Furthermore, Matrix-assisted laser desorption ionization mass spectrometry imaging analysis showed that the expression of ATP, glucose, and citric acid on 7th day was the highest during cerebral I/R, which indicated that energy metabolism and oxidative phosphorylation played important roles during cerebral I/R. In addition, the untargeted metabolomic results showed that the level of isocitric acid, the ratio of oxyglutaric acid/glutamic acid, and the level of pyruvic acid associated with the TCA cycle were also the highest on the 7th day during cerebral I/R, which indicated that the transient spontaneous recovery of ischemic brain on the 7th day after ischemia-reperfusion might be related to oxidative phosphorylation and energy metabolism in the brain in this period. In conclusion, the results suggest that some small molecule metabolites participate in the brain injury and functional recovery during cerebral I/R, which is of great significance to the development of therapeutic drugs and diagnostic markers.

Nuclear magnetic resonance-based serum metabolomic analysis reveals different disease evolution profiles between septic shock survivors and non-survivors

BACKGROUND

Septic shock is the most severe phase of sepsis and is associated with high rates of mortality. However, early stage prediction of septic shock outcomes remains difficult. Metabolomic techniques have emerged as a promising tool for improving prognosis.

METHODS

Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models separating the serum metabolomes of survivors from those of non-survivors were established with samples obtained at the intensive care unit (ICU) admission (H0) and 24 h later (H24). For 51 patients with available H0 and H24 samples, multi-level modeling was performed to provide insight into different metabolic evolutions that occurred between H0 and H24 in the surviving and non-surviving patients. Relative quantification and receiver operational characteristic curves (ROC) were applied to estimate the predictability of key discriminatory metabolites for septic shock mortality.

RESULTS

Metabolites that were involved in energy supply and protein breakdown were primarily responsible for differentiating survivors from non-survivors. This was not only seen in the H0 and H24 discriminatory models, but also in the H0-H24 paired models. Reanalysis of extra H0-H24 paired samples in the established multi-level model demonstrated good performance of the model for the classification of samplings. According to the ROC results, nine discriminatory metabolites defined consistently from the unpaired model and the H0-H24 time-trend change (ΔH24-H0) show good prediction of mortality. These results suggest that NMR-based metabolomic analysis is useful for a better overall assessment of septic shock patients.

CONCLUSIONS

Dysregulation of the metabolites identified by this study is associated with poor outcomes for septic shock. Evaluation of these compounds during the first 24 h after ICU admission in the septic shock patient may be helpful for estimating the severity of cases and for predicting outcomes.

TRIAL REGISTRATION

All human serum samples were collected and stored, provided by the "center of biologic resources for liver disease", in Jean Verdier Hospital, Bondy, France (BB-0033-00027).

Maresin1 Alleviates Metabolic Dysfunction in Septic Mice: A 1H NMR-Based Metabolomics Analysis

Maresin1 (MaR1), a new anti-inflammatory and proresolving lipid mediator, has been proven to exert organ-protective effects in septic animal models. However, the potential mechanisms are still not fully elucidated. In this study, we sought to explore the impact of MaR1 on metabolic dysfunction in cecal ligation and puncture- (CLP-) induced septic mice. We found that MaR1 significantly increased the overall survival rate and attenuated lung and liver injuries in septic mice. In addition, MaR1 markedly reduced the levels of proinflammatory cytokines (TNF-α and IL-6) and alleviated mitochondrial damage. Based on a ¹H NMR-based metabolomics analysis, CLP-induced septic mice had increased levels of acetate, pyruvate, and lactate in serum and decreased levels of alanine, aspartate, glutamate, and fumarate in lungs. However, these metabolic disorders, mainly involving energy and amino acid metabolism, can be recovered by MaR1 treatment. Therefore, our results suggest that the protective effects of MaR1 on sepsis could be related to the recovery of metabolic dysfunction and the alleviation of inflammation and mitochondrial damage.

Rapid Cerebral Metabolic Shift during Neonatal Sepsis Is Attenuated by Enteral Colostrum Supplementation in Preterm Pigs

Sepsis, the clinical manifestation of serious infection, may disturb normal brain development, especially in preterm infants with an immature brain. We hypothesized that neonatal sepsis induces systemic metabolic alterations that rapidly affect metabolic signatures in immature brain and cerebrospinal fluid (CSF). Cesarean-delivered preterm pigs systemically received 10⁹ CFU/kg Staphylococcus epidermidis (SE) and were provided total parenteral nutrition (n = 9) or enteral supplementation with bovine colostrum (n = 10) and compared with uninfected pigs receiving parenteral nutrition (n = 7). Plasma, CSF, and brain tissue samples were collected after 24 h and analyzed by ¹H NMR-based metabolomics. Both plasma and CSF metabolomes revealed SE-induced changes in metabolite levels that reflected a modified energy metabolism. Hence, increased plasma lactate, alanine, and succinate levels, as well as CSF lactate levels, were observed during SE infection (all p < 0.05, ANOVA analysis). Myo-inositol, a glucose derivative known for beneficial effects on lung maturation in preterm infants, was also increased in plasma and CSF following SE infection. Enteral colostrum supplementation attenuated the lactate accumulation in blood and CSF. Bloodstream infection in preterm newborns was found to induce a rapid metabolic shift in both plasma and CSF, which was modulated by colostrum feeding.

Metabolomics approach in lung tissue of septic rats and the interventional effects of Xuebijing injection using UHPLC-Q-Orbitrap-HRMS

Sepsis is the dysregulated host response to an infection which leads to life-threatening organ dysfunction. Metabolomic profiling in bio-fluid or tissue is vital for elucidating the pathogenesis of sepsis and evaluating therapeutic effects of medication. In this study, an untargeted metabolomics approach was applied to study the metabolic changes in lung tissue of septic rats induced by cecal ligation and puncture (CLP) and investigate the treatment effects of Xubijing injection (XBJ). Metabolomics analyses were performed on ultra-high performance liquid chromatography-Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap-HRMS) together with multivariate statistical analysis. A total of 26 differential metabolites between CLP and sham-operated group were identified. The altered metabolic pathways included energy metabolism, amino metabolism, lipid metabolism, fatty acid metabolism and hormone metabolism. Among the 26-varied metabolites, 15 were significantly regulated after XBJ treatment. The metabolic pathway network of sepsis was drawn to interpret the pathological feature of lung damage caused by sepsis and the underlying regulating mechanism of XBJ on the molecular levels. Our findings display that LC-MS-based metabolomics is a useful tool for uncovering the underlying molecular mechanism of sepsis, and XBJ may exert therapeutic effect by regulating multiple metabolic pathways.

Identification of novel metabolomic biomarkers in an experimental model of septic acute kidney injury

The aim of this study is the identification of metabolomic biomarkers of sepsis and sepsis-induced acute kidney injury (AKI) in an experimental model. Pigs were anesthetized and monitored to measure mean arterial pressure (MAP), systemic blood flow (Q_T), mean pulmonary arterial pressure, renal artery blood flow (Q_RA), renal cortical blood flow (Q_RC), and urine output (UO). Sepsis was induced at t = 0 min by the administration of live Escherichia coli ( n = 6) or saline ( n = 8). At t = 300 min, animals were killed. Renal tissue, urine, and serum samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy. Principal component analyses were performed on the processed NMR spectra to highlight kidney injury biomarkers. Sepsis was associated with decreased Q_T and MAP and decreased Q_RA, Q_RC, and UO. Creatinine serum concentration and neutrophil gelatinase-associated lipocalin (NGAL) serum and urine concentrations increased. NMR-based metabolomics analysis found metabolic differences between control and septic animals: 1) in kidney tissue, increased lactate and nicotinuric acid and decreased valine, aspartate, glucose, and threonine; 2) in urine, increased isovaleroglycine, aminoadipic acid, N-acetylglutamine, N-acetylaspartate, and ascorbic acid and decreased myoinositol and phenylacetylglycine; and 3) in serum, increased lactate, alanine, pyruvate, and glutamine and decreased valine, glucose, and betaine concentrations. The concentration of several metabolites altered in renal tissue and urine samples from septic animals showed a significant correlation with markers of AKI (i.e., creatinine and NGAL serum concentrations). NMR-based metabolomics is a potentially useful tool for biomarker identification of sepsis-induced AKI.

The potential of metabolic profiling for vaccine development

Recent technological advances have provided deeper insights into the role of small molecules in biological processes. Metabolic profiling has thus entered the arena of -omics studies and rapidly proven its value both as stand-alone and as complement to other more advanced approaches, notably transcriptomics. Here we describe the potential of metabolic profiling for vaccinology embedded in the context of infection and immunity. This discussion is preceded by a description of the relevant technical and analytical tools for biological interpretation of metabolic data. Although not as widely applied as other -omics technologies, we believe that metabolic profiling can make important contributions to the better understanding of mechanisms underlying vaccine-induced responses and their effects on the prevention of infection or disease.

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis

The objective of this review is to explain the science of metabolomics-a science of systems biology that measures and studies endogenous small molecules (metabolites) that are present in a single biological sample-and its application to the diagnosis and treatment of sepsis. In addition, we discuss how discovery through metabolomics can contribute to the development of precision medicine targets for this complex disease state and the potential avenues for those new discoveries to be applied in the clinical environment. A nonsystematic literature review was performed focusing on metabolomics, pharmacometabolomics, and sepsis. Human (adult and pediatric) and animal studies were included. Metabolomics has been investigated in the diagnosis, prognosis, and risk stratification of sepsis, as well as for the identification of drug target opportunities. Metabolomics elucidates a new level of detail when compared with other systems biology sciences, with regard to the metabolites that are most relevant in the pathophysiology of sepsis, as well as highlighting specific biochemical pathways at work in sepsis. Metabolomics also highlights biochemical differences between sepsis survivors and nonsurvivors at a level of detail greater than that demonstrated by genomics, transcriptomics, or proteomics, potentially leading to actionable targets for new therapies. The application of pharmacometabolomics and its integration with other systems pharmacology to sepsis therapeutics could be particularly helpful in differentiating drug responders and nonresponders and furthering knowledge of mechanisms of drug action and response. The accumulated literature on metabolomics suggests it is a viable tool for continued discovery around the pathophysiology, diagnosis and prognosis, and treatment of sepsis in both adults and children, and it provides a greater level of biochemical detail and insight than other systems biology approaches. However, the clinical application of metabolomics in sepsis has not yet been fully realized. Prospective validation studies are needed to translate metabolites from the discovery phase into the clinical utility phase.

Application of LC-MS-based metabolomics method in differentiating septic survivors from non-survivors

Septic shock is the most severe form of sepsis, which is still one of the leading causes of death in the intensive care unit (ICU). Even though early prognosis and diagnosis are known to be indispensable for reaching an optimistic outcome, pathogenic complexities and the lack of specific treatment make it difficult to predict the outcome individually. In the present study, serum samples from surviving and non-surviving septic shock patients were drawn before clinical intervention at admission. Metabolic profiles of all the samples were analyzed by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. One thousand four hundred nineteen peaks in positive mode and 1878 peaks in negative mode were retained with their relative standard deviation (RSD) below 30 %, in which 187 metabolites were initially identified by retention time and database in the light of the exact molecular mass. Differences between samples from the survivors and the non-survivors were investigated using multivariate and univariate analysis. Finally, 43 significantly varied metabolites were found in the comparison between survivors and non-survivors. Concretely, metabolites in the tricarboxylic acid (TCA) cycle, amino acids, and several energy metabolism-related metabolites were up-regulated in the non-survivors, whereas those in the urea cycle and fatty acids were generally down-regulated. Metabolites such as lysine, alanine, and methionine did not present significant changes in the comparison. Six metabolites were further defined as primary discriminators differentiating the survivors from the non-survivors at the early stage of septic shock. Our findings reveal that LC-MS-based metabolomics is a useful tool for studying septic shock. Graphical abstract ᅟ.

Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study

BACKGROUND

Clinicians lack objective tests to help determine the severity of bronchiolitis or to distinguish a viral from bacterial causes of respiratory distress. We hypothesized that children with respiratory syncytial virus (RSV) infection would have a different metabolomic profile compared to those with bacterial infection or healthy controls, and this might also vary with bronchiolitis severity.

METHODS

Clinical information and urine-based metabolomic data were collected from healthy age-matched children (n = 37) and those admitted to hospital with a proven infection (RSV n = 55; Non-RSV viral n = 16; bacterial n = 24). Nuclear magnetic resonance (NMR) measured 86 metabolites per urine sample. Partial least squares discriminant analysis (PLS-DA) was performed to create models of separation.

RESULTS

Using a combination of metabolites, a strong PLS-DA model (R2 = 0.86, Q2 = 0.76) was created differentiating healthy children from those with RSV infection. This model had over 90 % accuracy in classifying blinded infants with similar illness severity. Two other models differentiated length of hospitalization and viral versus bacterial infection.

CONCLUSION

While the sample sizes remain small, this is the first report suggesting that metabolomic analysis of urine samples has the potential to become a diagnostic aid. Future studies with larger sample sizes are required to validate the utility of metabolomics in pediatric patients with respiratory distress.

Use of Metabolomics to Trend Recovery and Therapy After Injury in Critically Ill Trauma Patients

IMPORTANCE

Metabolomics is the broad and parallel study of metabolites within an organism and provides a contemporaneous snapshot of physiologic state. Use of metabolomics in the clinical setting may help achieve precision medicine for those who have experienced trauma, where diagnosis and treatment are tailored to the individual patient.

OBJECTIVE

To examine whether metabolomics can (1) distinguish healthy volunteers from trauma patients and (2) quantify changes in catabolic metabolites over time after injury.

DESIGN, SETTING, AND PARTICIPANTS

Prospective cohort study with enrollment from September 2014 to May 2015 at an urban, level 1 trauma center. Included in the study were 10 patients with severe blunt trauma admitted within 12 hours of injury with systolic blood pressure less than 90 mm Hg or base deficit greater than 6 mEq/L and 5 healthy volunteers. Plasma samples (n = 35) were obtained on days 1, 3, and 7, and they were analyzed using mass spectrometry.

MAIN OUTCOMES AND MEASURES

Principal component analyses, multiple linear regression, and paired t tests were used to select biomarkers of interest. A broad-based metabolite profile comparison between trauma patients and healthy volunteers was performed. Specific biomarkers of interest were oxidative catabolites.

RESULTS

Trauma patients had a median age of 45 years and a median injury severity score of 43 (interquartile range, 34-50). Healthy fasting volunteers had a median age of 33 years. Compared with healthy volunteers, trauma patients showed oxidative stress on day 1: niacinamide concentrations were a mean (interquartile range) of 0.95 (0.30-1.45) relative units for trauma patients vs 1.06 (0.96-1.09) relative units for healthy volunteers (P = .02), biotin concentrations, 0.43 (0.27-0.58) relative units for trauma patients vs 1.21 (0.93-1.56) relative units for healthy volunteers (P = .049); and choline concentrations, 0.17 (0.09-0.22) relative units for trauma patients vs 0.21 (0.18-0.22) relative units for healthy volunteers (P = .004). Trauma patients showed lower nucleotide synthesis on day 1: adenylosuccinate concentrations were 0.08 (0.04-0.12) relative units for trauma patients vs 0.15 (0.14-0.17) relative units for healthy volunteers (P = .02) and cytidine concentrations were 1.44 (0.95-1.73) relative units for trauma patients vs 1.74 (1.62-1.98) relative units for healthy volunteers (P = .05). From trauma day 1 to day 7, trauma patients showed increasing muscle catabolism: serine levels increased from 42.03 (31.20-54.95) µM to 79.37 (50.29-106.37) µM (P = .002), leucine levels increased from 69.21 (48.36-99.89) µM to 114.16 (92.89-143.52) µM (P = .004), isoleucine levels increased from 20.43 (10.92-27.41) µM to 48.72 (36.28-64.84) µM (P < .001), and valine levels increased from 122.56 (95.63-140.61) µM to 190.52 (136.68-226.07) µM (P = .004). There was an incomplete reversal of oxidative stress.

CONCLUSIONS AND RELEVANCE

Metabolomics can function as a serial, comprehensive, and potentially personalized tool to characterize metabolism after injury. A targeted metabolomics approach was associated with ongoing oxidative stress, impaired nucleotide synthesis, and initial suppression of protein metabolism followed by increased nitrogen turnover. This technique may provide new therapeutic and nutrition targets in critically injured patients.

Comparison of sepsis rats induced by caecal ligation puncture or Staphylococcus aureus using a LC-QTOF-MS metabolomics approach

Sepsis is a whole-body inflammatory response to infection with high mortality and is treated in intensive care units (ICUs). In the present study, to identify metabolic biomarkers that can differentiate sepsis models induced by caecal ligation puncture (CLP) or Staphylococcus aureus (S. aureus), small molecular metabolites in the serum were measured by liquid chromatography quadruple time-of-flight mass spectrometry (LC-QTOF-MS) and analysed using the multivariate statistical analysis (MVA) of partial least square-discrimination analysis (PLS-DA) method. The results demonstrated that the body showed obvious metabolic disorders in the sepsis groups compared with the control group. A total of 8 potential biomarkers were identified in the CLP group, and 10 potential biomarkers were identified in the S. aureus group. These potential biomarkers primarily reflected an energy metabolism disorder, inflammatory response, oxidative stress and tissue damage, which occur during sepsis, and these markers might potentially be used to differentiate CLP from Staphylococcus aureus sepsis.

Metabolomics in Newborns

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction.

Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit

Early diagnosis and patient stratification may improve sepsis outcome by a timely start of the proper specific treatment. We aimed to identify metabolomic biomarkers of sepsis in urine by ¹H-NMR spectroscopy to assess the severity and to predict outcomes. Urine samples were collected from 64 patients with severe sepsis or septic shock in the ICU for a ¹H NMR spectra acquisition. A supervised analysis was performed on the processed spectra, and a predictive model for prognosis (30-days mortality/survival) of sepsis was constructed using partial least-squares discriminant analysis (PLS-DA). In addition, we compared the prediction power of metabolomics data respect the Sequential Organ Failure Assessment (SOFA) score. Supervised multivariate analysis afforded a good predictive model to distinguish the patient groups and detect specific metabolic patterns. Negative prognosis patients presented higher values of ethanol, glucose and hippurate, and on the contrary, lower levels of methionine, glutamine, arginine and phenylalanine. These metabolites could be part of a composite biopattern of the human metabolic response to sepsis shock and its mortality in ICU patients. The internal cross-validation showed robustness of the metabolic predictive model obtained and a better predictive ability in comparison with SOFA values. Our results indicate that NMR metabolic profiling might be helpful for determining the metabolomic phenotype of worst-prognosis septic patients in an early stage. A predictive model for the evolution of septic patients using these metabolites was able to classify cases with more sensitivity and specificity than the well-established organ dysfunction score SOFA.

Monitoring neonatal fungal infection with metabolomics

The objective of our study was to evaluate the capability of the metabolomics approach to identify the variations of urine metabolites over time related to the neonatal fungal septic condition. The study population included a clinical case of a preterm neonate with invasive fungal infection and 13 healthy preterm controls. This study showed a unique urine metabolic profile of the patient affected by fungal sepsis compared to urine of controls and it was also possible to evaluate the efficacy of therapy in improving patient health.

Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit

INTRODUCTION

Septic shock is a major life-threatening condition in critically ill patients and it is well known that early recognition of septic shock and expedient initiation of appropriate treatment improves patient outcome. Unfortunately, to date no single compound has shown sufficient sensitivity and specificity to be used as a routine biomarker for early diagnosis and prognosis of septic shock in the intensive care unit (ICU). Therefore, the identification of new diagnostic tools remains a priority for increasing the survival rate of ICU patients. In this study, we have evaluated whether a combined nuclear magnetic resonance spectroscopy-based metabolomics and a multiplex cytokine/chemokine profiling approach could be used for diagnosis and prognostic evaluation of septic shock patients in the ICU.

METHODS

Serum and plasma samples were collected from septic shock patients and ICU controls (ICU patients with the systemic inflammatory response syndrome but not suspected of having an infection). (1)H Nuclear magnetic resonance spectra were analyzed and quantified using the targeted profiling methodology. The analysis of the inflammatory mediators was performed using human cytokine and chemokine assay kits.

RESULTS

By using multivariate statistical analysis we were able to distinguish patient groups and detect specific metabolic and cytokine/chemokine patterns associated with septic shock and its mortality. These metabolites and cytokines/chemokines represent candidate biomarkers of the human response to septic shock and have the potential to improve early diagnosis and prognosis of septic shock.

CONCLUSIONS

Our findings show that integration of quantitative metabolic and inflammatory mediator data can be utilized for the diagnosis and prognosis of septic shock in the ICU.

Discrimination of sepsis stage metabolic profiles with an LC/MS-MS-based metabolomics approach

Background

To identify metabolic biomarkers that can be used to differentiate sepsis from systemic inflammatory response syndrome (SIRS), assess severity and predict outcomes.

Methods

65 patients were involved in this study, including 35 patients with sepsis, 15 patients with SIRS and 15 normal patients. Small metabolites that were present in patient serum samples were measured by liquid chromatography mass spectrometry techniques and analysed using multivariate statistical methods.

Results

The metabolic profiling of normal patients and patients with SIRS or sepsis was markedly different. A significant decrease in the levels of lactitol dehydrate and S-phenyl-d-cysteine and an increase in the levels of S-(3-methylbutanoyl)-dihydrolipoamide-E and N-nonanoyl glycine were observed in patients with sepsis in comparison to patients with SIRS (p<0.05). Patients with severe sepsis and septic shock displayed lower levels of glyceryl-phosphoryl-ethanolamine, Ne, Ne dimethyllysine, phenylacetamide and d-cysteine (p<0.05) in their sera. The profiles of patients with sepsis 48 h before death illustrated an obvious state of metabolic disorder, such that S-(3-methylbutanoyl)-dihydrolipoamide-E, phosphatidylglycerol (22:2 (13Z, 16Z)/0:0), glycerophosphocholine and S-succinyl glutathione were significantly decreased (p<0.05). The receiver operating characteristic curve of the differential expression of these metabolites was also performed.

Conclusions

The body produces significant evidence of metabolic disorder during SIRS or sepsis. Seven metabolites may potentially be used to diagnose sepsis.

Trial registration number

ClinicalTrial.gov identifier NCT01649440.

Metabolomic profiling in cattle experimentally infected with Mycobacterium avium subsp. paratuberculosis

The sensitivity of current diagnostics for Johne's disease, a slow, progressing enteritis in ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP), is too low to reliably detect all infected animals in the subclinical stage. The objective was to identify individual metabolites or metabolite profiles that could be used as biomarkers of early MAP infection in ruminants. In a monthly follow-up for 17 months, calves infected at 2 weeks of age were compared with aged-matched controls. Sera from all animals were analyzed by 1H nuclear magnetic resonance spectrometry. Spectra were acquired, processed, and quantified for analysis. The concentration of many metabolites changed over time in all calves, but some metabolites only changed over time in either infected or non-infected groups and the change in others was impacted by the infection. Hierarchical multivariate statistical analysis achieved best separation between groups between 300 and 400 days after infection. Therefore, a cross-sectional comparison between 1-year-old calves experimentally infected at various ages with either a high- or a low-dose and age-matched non-infected controls was performed. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS DA) yielded distinct separation of non-infected from infected cattle, regardless of dose and time (3, 6, 9 or 12 months) after infection. Receiver Operating Curves demonstrated that constructed models were high quality. Increased isobutyrate in the infected cattle was the most important agreement between the longitudinal and cross-sectional analysis. In general, high- and low-dose cattle responded similarly to infection. Differences in acetone, citrate, glycerol and iso-butyrate concentrations indicated energy shortages and increased fat metabolism in infected cattle, whereas changes in urea and several amino acids (AA), including the branched chain AA, indicated increased protein turnover. In conclusion, metabolomics was a sensitive method for detecting MAP infection much sooner than with current diagnostic methods, with individual metabolites significantly distinguishing infected from non-infected cattle.

Use of 1H-nuclear magnetic resonance to screen a set of biomarkers for monitoring metabolic disturbances in severe burn patients

Introduction

To establish a plasma metabolomics fingerprint spectrum for severe burn patients and to use it to identify a set of biomarkers that could be used for clinical monitoring.

Methods

Twenty-one severe burn patients and three healthy control individuals were enrolled in this study, and the plasma samples from patients and healthy individuals were collected for nuclear magnetic resonance (NMR) measurements. The NMR spectra were analyzed using principal component analysis (PCA) and partial least squares (PLS) in order to establish the metabolomics fingerprint representing the changes in metabolism and to select the major biomarkers.

Results

NMR spectra of the plasma samples showed significant differences between burn patients and healthy individuals. Using metabolomics techniques, we found an Eigen-metabolome that consists of 12 metabolites, which are regulated by 103 enzymes in a global metabolic network. Among these metabolites, α-ketoisovaleric acid, 3-methylhistidine, and β-hydroxybutyric acid were the most important biomarkers that were significantly increased during the early stage of burn injury. These results suggest that the mitochondrial damage and carbohydrate, protein and fatty acid metabolism disturbances occur after burn injury. Our analysis also show that histone deacetylases, which are protein transcription suppressors, were remarkably increased and indicate that protein transcription was inhibited and anabolism was restrained during the early stage of burn injury.

Conclusions

Metabolomics techniques based on NMR can be used to monitor metabolism in severe burn patients. Our study demonstrates that integrated ¹H-NMR metabolome and global metabolic network analysis is useful for visualizing complex metabolic disturbances after severe burn injury and may provide a new quantitative injury severity evaluation for future clinical use.

Trial registration

Chinese Clinical Trial Registry ChiCTR-OCC-12002145. Registered 25 April 2012.

Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock

OBJECTIVES

To determine whether a nuclear magnetic resonance-based metabolomics approach can be useful for the early diagnosis and prognosis of septic shock in ICUs.

DESIGN

Laboratory-based study.

SETTING

University research laboratory.

SUBJECTS

Serum samples from septic shock patients and ICU controls (ICU patients with systemic inflammatory response syndrome but not suspected of having an infection) were collected within 24 hours of admittance to the ICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

H nuclear magnetic resonance spectra of septic shock and ICU control samples were analyzed and quantified using a targeted profiling approach. By applying multivariate statistical analysis (e.g., orthogonal partial least squares discriminant analysis), we were able to distinguish the patient groups and detect specific metabolic patterns. Some of the metabolites were found to have a significant impact on the separation between septic shock and control samples. These metabolites could be interpreted in terms of a biological human response to septic shock and they might serve as a biomarker pattern for septic shock in ICUs. Additionally, nuclear magnetic resonance-based metabolomics was evaluated in order to detect metabolic variation between septic shock survivors and nonsurvivors and to predict patient outcome. The area under the receiver operating characteristic curve indicated an excellent predictive ability for the constructed orthogonal partial least squares discriminant analysis models (septic shock vs ICU controls: area under the receiver operating characteristic curve = 0.98; nonsurvivors vs survivors: area under the receiver operating characteristic curve = 1).

CONCLUSIONS

Our results indicate that nuclear magnetic resonance-based metabolic profiling could be used for diagnosis and mortality prediction of septic shock in the ICU.

A Metabolomic Approach to the Pathogenesis of Ventilator-induced Lung Injury

BACKGROUND

Global metabolic profiling using quantitative nuclear magnetic resonance spectroscopy (MRS) and mass spectrometry (MS) is useful for biomarker discovery. The objective of this study was to discover biomarkers of acute lung injury induced by mechanical ventilation (ventilator-induced lung injury [VILI]), by using MRS and MS.

METHODS

Male Sprague-Dawley rats were subjected to two ventilatory strategies for 2.5 h: tidal volume 9 ml/kg, positive end-expiratory pressure 5 cm H2O (control, n = 14); and tidal volume 25 ml/kg and positive end-expiratory pressure 0 cm H2O (VILI, n = 10). Lung tissue, bronchoalveolar lavage fluid, and serum spectra were obtained by high-resolution magic angle spinning and H-MRS. Serum spectra were acquired by high-performance liquid chromatography coupled to quadupole-time of flight MS. Principal component and partial least squares analyses were performed.

RESULTS

Metabolic profiling discriminated characteristics between control and VILI animals. As compared with the controls, animals with VILI showed by MRS higher concentrations of lactate and lower concentration of glucose and glycine in lung tissue, accompanied by increased levels of glucose, lactate, acetate, 3-hydroxybutyrate, and creatine in bronchoalveolar lavage fluid. In serum, increased levels of phosphatidylcholine, oleamide, sphinganine, hexadecenal and lysine, and decreased levels of lyso-phosphatidylcholine and sphingosine were identified by MS.

CONCLUSIONS

This pilot study suggests that VILI is characterized by a particular metabolic profile that can be identified by MRS and MS. The metabolic profile, though preliminary and pending confirmation in larger data sets, suggests alterations in energy and membrane lipids.SUPPLEMENTAL DIGITAL CONTENT IS AVAILABLE IN THE TEXT.

Serum metabolomics identifies citrulline as a predictor of adverse outcomes in an equine model of gut-derived sepsis

Acute laminitis is an inflammatory disease of the equine foot that often occurs secondarily to sepsis or systemic inflammation associated with gastrointestinal disease. It has been suggested that laminitis is similar to multiple organ dysfunction syndrome in humans, although in horses the weight-bearing laminar epithelium of the foot appears to be the tissue most sensitive to insult and the first "organ" to fail. Metabolomics performed on serum samples collected before (Con) and after (Lmn) experimental induction of gastrointestinal-associated sepsis in six horses detected 1,177 metabolites of both mammalian and bacterial origin in equine serum. Network and correlation analyses suggested a dysregulation of fatty acid metabolism in the Lmn group, as well as an accumulation of organic acids such as lactate. Furthermore, concentrations of the amino acid citrulline were decreased in Lmn samples from all study animals, suggesting that citrulline might be useful as a biomarker to identify critically ill animals that are at risk of developing laminitis. We therefore established normal ranges of plasma citrulline concentrations in a separate group of horses (n = 36) and tested the ability of citrulline to predict adverse outcomes (laminitis or death) in critically ill horses (n = 23). Plasma citrulline was significantly lower in critically ill horses that went on to experience adverse outcomes (n = 6). Further study is required to accurately determine a diagnostic cutoff, but the present data are suggestive of the predictive value of citrulline as a biomarker for laminar failure in equine sepsis.

New diagnostic possibilities in systemic neonatal infections: metabolomics

Systemic neonatal infection is a serious complication in preterm and term infants and is defined as a complex clinical syndrome caused by bacteria, fungi and virus. Sepsis remains among the leading causes of death in both developed and underdeveloped countries above all in the neonatal period. Earlier diagnosis may offer the ability to initiate treatment to prevent adverse outcomes. There have been many studies on various diagnostic haematological markers like acute phase reactants, C-reactive protein, procalcitonin, interleukins and presepsin. However, there is still no single test that satisfies the criteria as being the ideal marker for the early diagnosis of neonatal sepsis. In this regard, metabolomic analysis seems to be a promising method for determining metabolic variations correlated with systemic neonatal infections.

Urinary (1)H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis

The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3-6 days and late onset after 4-7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance ((1)H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05).

[Nuclear magnetic resonance based metabolic phenotyping for patient evaluations in operating rooms and intensive care units]

Metabolic phenotyping consists in the identification of subtle and coordinated metabolic variations associated with various pathophysiological stimuli. Different analytical methods, such as nuclear magnetic resonance, allow the simultaneous quantification of a large number of metabolites. Statistical analyses of these spectra thus lead to the discrimination between samples and the identification of a metabolic phenotype corresponding to the effect under study. This approach allows the extraction of candidate biomarkers and the recovery of perturbed metabolic networks, driving to the generation of biochemical hypotheses (pathophysiological mechanisms, diagnostic tests, therapeutic targets…). Metabolic phenotyping could be useful in anaesthesiology and intensive care medicine for the evaluation, monitoring or diagnosis of life-threatening situations, to optimise patient managements. This review introduces the physical and statistical fundamentals of NMR-based metabolic phenotyping, describes the work already achieved by this approach in anaesthesiology and intensive care medicine. Finally, potential areas of interest are discussed for the perioperative and intensive management of patients, from newborns to adults.

Application of (1)h NMR spectroscopy-based metabolomics to sera of tuberculosis patients

Nuclear magnetic resonance (NMR) spectroscopy is an ideal platform for the metabolic analysis of biofluids due to its high reproducibility, nondestructiveness, nonselectivity in metabolite detection, and the ability to simultaneously quantify multiple classes of metabolites. Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. In this study, we combined multivariate pattern recognition (PR) analytical techniques with (1)H NMR spectroscopy to explore the metabolic profile of sera from TB patients. A total of 77 serum samples obtained from patients with TB (n = 38) and healthy controls (n = 39) were investigated. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was capable of distinguishing TB patients from controls and establishing a TB-specific metabolite profile. A total of 17 metabolites differed significantly in concentration between the two groups. Serum samples from TB patients were characterized by increased concentrations of 1-methylhistidine, acetoacetate, acetone, glutamate, glutamine, isoleucine, lactate, lysine, nicotinate, phenylalanine, pyruvate, and tyrosine, accompanied by reduced concentrations of alanine, formate, glycine, glycerolphosphocholine, and low-density lipoproteins relative to control subjects. Our study reveals the metabolic profile of sera from TB patients and indicates that NMR-based methods can distinguish TB patients from healthy controls. NMR-based metabolomics has the potential to be developed into a novel clinical tool for TB diagnosis or therapeutic monitoring and could contribute to an improved understanding of disease mechanisms.

Metabolomics as a novel approach for early diagnosis of pediatric septic shock and its mortality

RATIONALE

Septic shock is a significant cause of morbidity and mortality in the pediatric population. Early recognition of septic shock and appropriate treatment increase survival rate; thus, developing new diagnostic tools may improve patients' outcomes.

OBJECTIVES

To determine whether a metabolomics approach could be useful in the diagnosis and prognosis of septic shock in pediatric intensive care unit (PICUs).

METHODS

Serum samples were collected from 60 patients with septic shock, 40 PICU patients with systemic inflammatory response syndrome (not suspected of having an infection), and 40 healthy children. Proton nuclear magnetic resonance spectroscopy spectra were analyzed and quantified using targeted profiling methodology.

MEASUREMENTS AND MAIN RESULTS

Multivariate statistical analysis was applied to detect specific patterns in metabolic profiles and to highlight differences between patient samples. Supervised analysis afforded good predictive models and managed to separate patient populations. Some of the metabolite concentrations identified in serum samples changed markedly, indicating their influence on the separation between patient groups. These metabolites represent a composite biopattern of the pediatric metabolic response to septic shock and might be considered as the basis for a biomarker panel for the diagnosis of septic shock and its mortality in PICU.

CONCLUSIONS

Our results indicate that nuclear magnetic resonance metabolite profiling might serve as a promising approach for the diagnosis and prediction of mortality in septic shock in a pediatric population and that quantitative metabolomics methods can be applied in the clinical evaluations of pediatric septic shock.

Chronic alcohol ingestion increases mortality and organ injury in a murine model of septic peritonitis

Background

Patients admitted to the intensive care unit with alcohol use disorders have increased morbidity and mortality. The purpose of this study was to determine how chronic alcohol ingestion alters the host response to sepsis in mice.

Methods

Mice were randomized to receive either alcohol or water for 12 weeks and then subjected to cecal ligation and puncture. Mice were sacrificed 24 hours post-operatively or followed seven days for survival.

Results

Septic alcohol-fed mice had a significantly higher mortality than septic water-fed mice (74% vs. 41%, p = 0.01). This was associated with worsened gut integrity in alcohol-fed mice with elevated intestinal epithelial apoptosis, decreased crypt proliferation and shortened villus length. Further, alcohol-fed mice had higher intestinal permeability with decreased ZO-1 and occludin protein expression in the intestinal tight junction. The frequency of splenic and bone marrow CD4+ T cells was similar between groups; however, splenic CD4+ T cells in septic alcohol-fed mice had a marked increase in both TNF and IFN-γ production following ex vivo stimulation. Neither the frequency nor function of CD8+ T cells differed between alcohol-fed and water-fed septic mice. NK cells were decreased in both the spleen and bone marrow of alcohol-fed septic mice. Pulmonary myeloperoxidase levels and BAL levels of G-CSF and TFG-β were higher in alcohol-fed mice. Pancreatic metabolomics demonstrated increased acetate, adenosine, xanthine, acetoacetate, 3-hydroxybutyrate and betaine in alcohol-fed mice and decreased cytidine, uracil, fumarate, creatine phosphate, creatine, and choline. Serum and peritoneal cytokines were generally similar between alcohol-fed and water-fed mice, and there were no differences in bacteremia, lung wet to dry weight, or pulmonary, liver or splenic histology.

Conclusions

When subjected to the same septic insult, mice with chronic alcohol ingestion have increased mortality. Alterations in intestinal integrity, the host immune response, and pancreatic metabolomics may help explain this differential response.

Implementation of molecular phenotyping approaches in the personalized surgical patient journey

OBJECTIVE

The present review describes commonly employed metabolic profiling platforms and discusses the current and likely future application of these technologies in surgery.

BACKGROUND

The metabolic adaptations that occur in response to surgical illness and trauma are incompletely understood. Evaluating these will be critical to the development of personalized surgical health solutions. Metabonomics is an advancing field in systems biology, which provides a means of interrogating these metabolic shifts.

METHODS

Recent literature regarding metabolic profiling technologies and their applications in surgical practice are discussed. Future strategies are proposed for the incorporation of these and next-generation technologies in the evaluation of all steps in the patient surgical pathway.

RESULTS

Metabolite-based profiling has provided valuable insights into the metabolic irregularities that occur in cancer development and progression across a variety of cancer subclasses including colorectal, breast, prostate, and lung cancers. In addition, metabolic modeling has shown considerable promise in other surgical conditions including trauma and sepsis and in the assessment of pharmacotherapeutic efficacy.

DISCUSSION

Metabonomics offers a posttranscriptional view of system activity providing functional information downstream of the genome and proteome. Information at this level will provide the surgeon with a novel means of evaluating major socioeconomic problems such as cancer and sepsis. In addition, the rapid nature of emerging next generation profiling platforms provides a viable means of "real-time" perioperative metabolic assessment and optimization.

Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers

Nuclear magnetic resonance spectroscopy (called NMR for ex vivo techniques and MRS for in vivo techniques) has become a useful analytical and diagnostic tool in biomedicine. In the past two decades, an MR-based spectroscopic approach for translational and clinical research has emerged that allows for biochemical characterization of the tissue of interest either ex vivo (NMR-based metabolomics) or in vivo (localized MRS-single voxel or multivoxel-spectroscopic imaging). The greatest advantages of MRS techniques are their ability to detect multiple tissue-specific metabolites in a single experiment, their quantitative nature and translational component (in vitro/ex vivo-discovered metabolic biomarkers can be translated into noninvasive spectroscopic imaging protocols). Disadvantages of MRS include low sensitivity and spectral resolution and, in case of NMR-metabolomics, metabolite degradation and incomplete recovery in processed samples. In vivo MRS has worse spectral resolution than ex vivo high-resolution NMR due to the inherently wider lines of metabolites in vivo and the difficulty of using traditional line-narrowing methods (e.g., sample spinning). It also suffers from poor time-resolution, therefore offering fewer metabolic biomarkers to be followed in vivo. In the present review article, we provide considerations for establishing reliable protocols (both in vivo and ex vivo) for metabolite detection, recovery and quantification from in vivo and ex vivo MR spectra.

A metabolomic approach for diagnosis of experimental sepsis

BACKGROUND

The search for reliable diagnostic biomarkers of sepsis remains necessary. Assessment of global metabolic profiling using quantitative nuclear magnetic resonance (NMR)-based metabolomics offers an attractive modern methodology for fast and comprehensive determination of multiple circulating metabolites and for defining the metabolic phenotype of sepsis.

OBJECTIVE

To develop a novel NMR-based metabolomic approach for diagnostic evaluation of sepsis.

METHODS

Male Sprague-Dawley rats (weight 325-375 g) underwent cecal ligation and puncture (n = 14, septic group) or sham procedure (n = 14, control group) and 24 h later were euthanized. Lung tissue, bronchoalveolar lavage (BAL) fluid, and serum samples were obtained for (1)H NMR and high-resolution magic-angle spinning analysis. Unsupervised principal components analysis was performed on the processed spectra, and a predictive model for diagnosis of sepsis was constructed using partial least-squares discriminant analysis.

RESULTS

NMR-based metabolic profiling discriminated characteristics between control and septic rats. Characteristic metabolites changed markedly in septic rats as compared with control rats: alanine, creatine, phosphoethanolamine, and myoinositol concentrations increased in lung tissue; creatine increased and myoinositol decreased in BAL fluid; and alanine, creatine, phosphoethanolamine, and acetoacetate increased whereas formate decreased in serum. A predictive model for diagnosis of sepsis using these metabolites classified cases with sensitivity and specificity of 100%.

CONCLUSIONS

NMR metabolomic analysis is a potentially useful technique for diagnosis of sepsis. The concentrations of metabolites involved in energy metabolism and in the inflammatory response change in this model of sepsis.

1H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease characterized by multi-system involvement, diverse clinical presentation, and alterations in circulating metabolites. In this study, a 1H NMR spectroscopy-based metabolomics approach was applied to establish a human SLE serum metabolic profile. Serum samples were obtained from patients with SLE ( n = 64), patients with rheumatoid arthritis (RA) ( n = 30) and healthy controls ( n = 35). The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing SLE or RA patients from healthy subjects. The OPLS-DA model was able to predict diagnosis of SLE with a sensitivity rate of 60.9% and a specificity rate of 97.1%. For diagnosing RA, the model has much higher sensitivity (96.7%) and specificity (91.4%). The SLE serum samples were characterized by reduced concentrations of valine, tyrosine, phenylalanine, lysine, isoleucine, histidine, glutamine, alanine, citrate, creatinine, creatine, pyruvate, high-density lipoprotein, cholesterol, glycerol, formate and increased concentrations of N-acetyl glycoprotein, very low-density lipoprotein and low-density lipoprotein in comparison with the control population. Theresults not only indicated that serum NMR-based metabolomic methods had sufficient sensitivity and specificity to distinguish SLE and RA from healthy controls, but also have the potential to be developed into a clinically useful diagnostic tool, and could also contribute to a further understanding of disease mechanisms.

The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a "snapshot" reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis.

Alterations in urine, serum and brain metabolomic profiles exhibit sexual dimorphism during malaria disease progression

Background

Metabolic changes in the host in response to Plasmodium infection play a crucial role in the pathogenesis of malaria. Alterations in metabolism of male and female mice infected with Plasmodium berghei ANKA are reported here.

Methods

¹H NMR spectra of urine, sera and brain extracts of these mice were analysed over disease progression using Principle Component Analysis and Orthogonal Partial Least Square Discriminant Analysis.

Results

Analyses of overall changes in urinary profiles during disease progression demonstrate that females show a significant early post-infection shift in metabolism as compared to males. In contrast, serum profiles of female mice remain unaltered in the early infection stages; whereas that of the male mice changed. Brain metabolite profiles do not show global changes in the early stages of infection in either sex. By the late stages urine, serum and brain profiles of both sexes are severely affected. Analyses of individual metabolites show significant increase in lactate, alanine and lysine, kynurenic acid and quinolinic acid in sera of both males and females at this stage. Early changes in female urine are marked by an increase of ureidopropionate, lowering of carnitine and transient enhancement of asparagine and dimethylglycine. Several metabolites when analysed individually in sera and brain reveal significant changes in their levels in the early phase of infection mainly in female mice. Asparagine and dimethylglycine levels decrease and quinolinic acid increases early in sera of infected females. In brain extracts of females, an early rise in levels is also observed for lactate, alanine and glycerol, kynurenic acid, ureidopropionate and 2-hydroxy-2-methylbutyrate.

Conclusions

These results suggest that P. berghei infection leads to impairment of glycolysis, lipid metabolism, metabolism of tryptophan and degradation of uracil. Characterization of early changes along these pathways may be crucial for prognosis and better disease management. Additionally, the distinct sexual dimorphism exhibited in these responses has a bearing on the understanding of the pathophysiology of malaria.

SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system

BACKGROUND

SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves.

RESULTS

SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS (HRMAS), processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin). In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used.

CONCLUSIONS

SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools.

Serum miR-146a and miR-223 as potential new biomarkers for sepsis

OBJECTIVE

Current biomarkers cannot completely distinguish sepsis from systemic inflammatory response syndrome (SIRS) caused by other non-infectious diseases. Circulating microRNAs (miRNAs) are promising biomarkers for several diseases, but their correlation with sepsis is not totally clarified.

METHODS

Seven miRNAs related to inflammation or infection were included in the present study. Serum miRNA expression was investigated in 50 patients diagnosed with sepsis, 30 patients with SIRS and 20 healthy controls to evaluate the diagnostic and prognostic value. Expression levels of serum miRNAs were determined by quantitative PCR using the Qiagen miScript system. Serum CRP and IL-6 levels were determined by enzyme linked immunosorbent assay.

RESULTS

Serum miR-146a and miR-223 were significantly reduced in septic patients compared with SIRS patients and healthy controls. The areas under the receiver operating characteristic curve of miR-146a, miR-223 and IL-6 were 0.858, 0.804 and 0.785, respectively.

CONCLUSION

Serum miR-146a and miR-223 might serve as new biomarkers for sepsis with high specificity and sensitivity. (ClinicalTrials.gov number, NCT00862290.).