Lipid mediators of inflammation as novel plasma biomarkers to identify patients with bacteremia

Discrimination of serum metabolomics profiles in infants with sepsis, based on liquid chromatography-mass spectrometer

Sepsis is one of the most important problems to be addressed in pediatrics, characterized by insidious onset, rapid progression, and high rates of severe infection and even mortality. Biomarkers with high sensitivity and robustness are urgently required for the early diagnosis of infant sepsis. Serum metabolomic approaches based on liquid chromatography-mass spectrometry were used to analyze the samples from 30 infants with sepsis at an early stage and 30 infants with noninfectious diseases. Multivariate statistical analysis was used to screen for differential metabolites and ROC curves were generated to find potential biomarkers. Six metabolites, including phosphatidic acid (PA (8:0/14:0)), phosphatidyl ethanolamine (PE (16:0/18:2(9Z,12Z))), cytidine 5'-diphosphocholine (CDP-CHO), sphingomyelin (SM (d18:0/16:1(9Z))), prolylhydroxyproline and phosphorylcholine (P-CHO), were identified between the two groups. ROC curve analysis showed that prolylhydroxyproline (AUC = 0.832) had potential diagnostic values for infant sepsis. The AUC value was 0.859 (CI: 0.764, 0.954) in the combined model. Prolylhydroxyproline were found to be correlated with CRP and PCT levels, while PE and CDP-CHO associated with PCT levels. Pathway analysis indicated that glycerophospholipid metabolism, aminoacyl-tRNA biosynthesis and necroptosis pathways played important roles in infant sepsis. Network analysis showed that the differential metabolites were linked to ERK/ MAPK, NF-κB, AMPK, mTOR, and other classical inflammatory and metabolic signaling pathways. This study identified serum metabolite profiles and three metabolites as potential biomarkers in infants with sepsis. The findings will help improve the early diagnosis of sepsis in infants.

A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics

Purpose

There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.

Patients and Methods

42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.

Results

The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.

Conclusion

A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.

Systemic inflammatory markers in patients with polyneuropathies

Introduction

In patients with peripheral neuropathies (PNP), neuropathic pain is present in 50% of the cases, independent of the etiology. The pathophysiology of pain is poorly understood, and inflammatory processes have been found to be involved in neuro-degeneration, -regeneration and pain. While previous studies have found a local upregulation of inflammatory mediators in patients with PNP, there is a high variability described in the cytokines present systemically in sera and cerebrospinal fluid (CSF). We hypothesized that the development of PNP and neuropathic pain is associated with enhanced systemic inflammation.

Methods

To test our hypothesis, we performed a comprehensive analysis of the protein, lipid and gene expression of different pro- and anti-inflammatory markers in blood and CSF from patients with PNP and controls.

Results

While we found differences between PNP and controls in specific cytokines or lipids, such as CCL2 or oleoylcarnitine, PNP patients and controls did not present major differences in systemic inflammatory markers in general. IL-10 and CCL2 levels were related to measures of axonal damage and neuropathic pain. Lastly, we describe a strong interaction between inflammation and neurodegeneration at the nerve roots in a specific subgroup of PNP patients with blood-CSF barrier dysfunction.

Conclusion

In patients with PNP systemic inflammatory, markers in blood or CSF do not differ from controls in general, but specific cytokines or lipids do. Our findings further highlight the importance of CSF analysis in patients with peripheral neuropathies.

Brain lipidomics: From functional landscape to clinical significance

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with the etiology and progression of neurodegeneration and other neurological pathologies. Hence, brain lipids are emerging as important potential targets for the early diagnosis and prognosis of neurological diseases. This review aims to highlight the significance and usefulness of lipidomics in diagnosing and treating brain diseases. We explored lipid alterations associated with brain diseases, paying attention to organ-specific characteristics and the functions of brain lipids. As the recent advances in brain lipidomics would have been impossible without advances in analytical techniques, we provide up-to-date information on mass spectrometric approaches and integrative analysis with other omic approaches. Last, we present the potential applications of lipidomics combined with artificial intelligence techniques and interdisciplinary collaborative research for treating brain diseases with clinical heterogeneities.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

Lipid profiles and differential lipids in serum related to severity of community-acquired pneumonia: A pilot study

This study aimed to characterize the lipidomic responses to community-acquired pneumonia (CAP) and provide new insight into the underlying mechanisms of pathogenesis and potential avenues for diagnostic and therapeutic treatments. This study was performed from January 2017 to October 2018. Lipidomic profiles were generated using ultra high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) platform. Spearman's rank correlation test and multiple linear regression analysis were applied to explore the correlation between changes in the relative abundance of lipids and clinical parameters. Kaplan-Meier methods were used to build 30-day survival curves. From the UHPLC-MS/MS results, a total of 509 and 195 lipid species were detected in the positive and negative ionization mode respectively. Positive ionization covered six lipid classes (glycerol-phospholipids, glycerolipids, sphingolipids, sterol-lipids, prenol-lipids, and fatty acid), whilst negative ionization covered three (glycerol-phospholipids, sphingolipids, fatty acid). Four lipids were selected as targets: PC (16:0_18:1), PC (18:2_20:4), PC (36:4), and PC (38:6). The relative increase of the areas under the curves for all four lipids were superior to the pneumonia severity index and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥65 years old) for discriminating severe CAP from CAP. Decreasing relative levels of PC (18:2_20:4), PC (38:6), and PC (36:4) were negatively related to fraction of inspiration O2; Changes in the relative abundance of PC (16:0_18:1) and PC (18:2_20:4) had significantly linear relationship with procalcitonin. Patients with an elevated level of PC (16:0_18:1) had significantly longer duration of hospital stays. As the relative abundance of PC (18:2_20:4), PC (36:4), and PC (38:6) decreased, the length of hospitalization days and 30-day mortality rate increased significantly (all log-rank p<0.05). Therefore, using the UHPLC-MS/MS platform's serum lipidomic approach can help reveal changes in lipid abundance during CAP and establish lipid profiles related to disease severity.

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection. We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis. Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital. Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients. The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission. Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94 ± 0.054 and 0.80 ± 0.085, respectively. Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure. The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.

Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)

Introduction

Pneumonia is the most common cause of mortality from infectious diseases, the second leading cause of nosocomial infection, and the leading cause of mortality among hospitalized adults. To improve clinical management, metabolomics has been increasingly applied to find specific metabolic biopatterns (profiling) for the diagnosis and prognosis of various infectious diseases, including pneumonia.

Methods

One hundred fifty bacterial community-acquired pneumonia (CAP) patients whose plasma samples were drawn within the first 24 h of hospital admission were enrolled in this study and separated into two age- and sex-matched cohorts: non-survivors (died ≤ 90 days) and survivors (survived > 90 days). Three analytical tools, ¹H-NMR spectroscopy, GC-MS, and targeted DI-MS/MS, were used to prognosticate non-survivors from survivors by means of metabolic profiles.

Results

We show that quantitative lipid profiling using DI-MS/MS can predict the 90-day mortality and in-hospital mortality among patients with bacterial CAP compared to ¹H-NMR- and GC-MS-based metabolomics. This study showed that the decreased lysophosphatidylcholines and increased acylcarnitines are significantly associated with increased mortality in bacterial CAP. Additionally, we found that decreased lysophosphatidylcholines and phosphatidylcholines (> 36 carbons) and increased acylcarnitines may be used to predict the prognosis of in-hospital mortality for bacterial CAP as well as the need for ICU admission and severity of bacterial CAP.

Discussion

This study demonstrates that lipid-based plasma metabolites can be used for the prognosis of 90-day mortality among patients with bacterial CAP. Moreover, lipid profiling can be utilized to identify patients with bacterial CAP who are at the highest risk of dying in hospital and who need ICU admission as well as the severity assessment of CAP.

New insights into pediatric community-acquired pneumonia gained from untargeted metabolomics: A preliminary study

BACKGROUND

Available diagnostics often fail to distinguish viral from bacterial causes of pediatric community-acquired pneumonia (pCAP). Metabolomics, which aims at characterizing diseases based on their metabolic signatures, has been applied to expand pathophysiological understanding of many diseases. In this exploratory study, we used the untargeted metabolomic analysis to shed new light on the etiology of pCAP.

METHODS

Liquid chromatography coupled with mass spectrometry was used to quantify the metabolite content of urine samples collected from children hospitalized for CAP of pneumococcal or viral etiology, ascertained using a conservative algorithm combining microbiological and biochemical data.

RESULTS

Fifty-nine children with CAP were enrolled over 16 months. Pneumococcal and viral cases were distinguished by means of a multivariate model based on 93 metabolites, 20 of which were identified and considered as putative biomarkers. Among these, six metabolites belonged to the adrenal steroid synthesis and degradation pathway.

CONCLUSIONS

This preliminary study suggests that viral and pneumococcal pneumonia differently affect the systemic metabolome, with a stronger disruption of the adrenal steroid pathway in pneumococcal pneumonia. This finding may lead to the discovery of novel diagnostic biomarkers and bring us closer to personalized therapy for pCAP.

l-Proline protects mice challenged by Klebsiella pneumoniae bacteremia

OBJECTIVE

K. pneumoniae, a common pathogen that frequently causes bacteremia in clinic, is unresponsive to most of known antibiotics, thus cumulatively exacerbating empirical therapy failures. Effective strategies to control Klebsiella pneumoniae bacteremia are in high demand. One possibility is to mobilize host defense mechanisms against bacterial pathogens.

METHODS

We employed GC/MS-based metabolomics to identify the changes of metabolism in mice challenged by K. pneumoniae (ATCC 43816) bacteremia.

RESULTS

Compared with the mice that compromised from K. pneumoniae bacteremia, mice that survived from infection displayed the varied metabolomic profile. The differential analysis of metabolome showed that Ethanedioic acid, d-Glucose, l-Glutamine, Myo-inositol, and l-Proline were more likely associated with the host surviving a K. pneumoniae bacteremia. Further pathway enrichment analysis proposed that arginine and proline metabolism involved in outcome of K. pneumoniae bacteremia. The follow-up data showed that exogenous l-Proline but not d-Proline could decline the loads of Klebsiella pneumonia in infected blood and tissues (lung, liver and spleen) and increase the mouse survival.

CONCLUSION

Our study provides an exercisable strategy of identifying metabolic biomarkers from surviving host and highlights the possibility of utilizing the metabolic biomarker as a therapy for K. pneumoniae bacteremia.

Plasma acylcarnitines and progression of carotid artery atherosclerosis in HIV infection

OBJECTIVE

To evaluate plasma acylcarnitine profiles and their relationships with progression of carotid artery atherosclerosis among individuals with and without HIV infection.

DESIGN

Prospective cohort studies of 499 HIV-positive and 206 HIV-negative individuals from the Women's Interagency HIV Study and the Multicenter AIDS Cohort Study.

METHODS

Twenty-four acylcarnitine species were measured in plasma samples of participants at baseline. Carotid artery plaque was assessed using repeated B-mode carotid artery ultrasound imaging in 2004-2013. We examined the associations of individual and aggregate short-chain (C2-C7), medium-chain (C8-C14) and long-chain acylcarnitines (C16-C26) with incident carotid artery plaque over 7 years.

RESULTS

Among 24 acylcarnitine species, C8-carnitines and C20 : 4-carnitines showed significantly lower levels comparing HIV-positive to HIV-negative individuals (false discovery rate adjusted P < 0.05); and C20-carnitines and C26-carnitines showed significantly higher levels in HIV positive using antiretroviral therapy than those without antiretroviral therapy (false discovery rate adjusted P < 0.05). In the univariate analyses, higher aggregated short-chain and long-chain acylcarnitine scores were associated with increased risk of carotid artery plaque [risk ratios (RRs) = 1.22 (95% confidence interval 1.02-1.45) and 1.20 (1.02-1.41) per SD increment, respectively]. The association for the short-chain acylcarnitine score remained significant [RR = 1.23 (1.05-1.44)] after multivariate adjustment (including traditional cardiovascular disease risk factors). This association was more evident in HIV-positive individuals without persistent viral suppression [RR = 1.37 (1.11-1.69)] compared with those with persistent viral suppression during follow-up [RR = 1.03 (0.76-1.40)] or HIV-negative individuals [RR = 1.02 (0.69-1.52)].

CONCLUSION

In two HIV cohorts, plasma levels of most acylcarnitines were not significantly different between HIV-positive and HIV-negative individuals. However, higher levels of aggregated short-chain acylcarnitines were associated with progression of carotid artery atherosclerosis.

Talaromyces marneffei Mp1 Protein, a Novel Virulence Factor, Carries Two Arachidonic Acid-Binding Domains To Suppress Inflammatory Responses in Hosts

Talaromyces marneffei infection causes talaromycosis (previously known as penicilliosis), a very important opportunistic systematic mycosis in immunocompromised patients. Different virulence mechanisms in T. marneffei have been proposed and investigated. In the sera of patients with talaromycosis, Mp1 protein (Mp1p), a secretory galactomannoprotein antigen with two tandem ligand-binding domains (Mp1p-LBD1 and Mp1p-LBD2), was found to be abundant. Mp1p-LBD2 was reported to possess a hydrophobic cavity to bind copurified palmitic acid (PLM). It was hypothesized that capturing of lipids from human hosts by expressing a large quantity of Mp1p is a virulence mechanism of T. marneffei It was shown that expression of Mp1p enhanced the intracellular survival of T. marneffei by suppressing proinflammatory responses. Mechanistic study of Mp1p-LBD2 suggested that arachidonic acid (AA), a precursor of paracrine signaling molecules for regulation of inflammatory responses, is the major physiological target of Mp1p-LBD2. In this study, we use crystallographic and biochemical techniques to further demonstrate that Mp1p-LBD1, the previously unsolved first lipid binding domain of Mp1p, is also a strong AA-binding domain in Mp1p. These studies on Mp1p-LBD1 support the idea that the highly expressed Mp1p is an effective AA-capturing protein. Each Mp1p can bind up to 4 AA molecules. The crystal structure of Mp1p-LBD1-LBD2 has also been solved, showing that both LBDs are likely to function independently with a flexible linker between them. T. marneffei and potentially other pathogens highly expressing and secreting proteins similar to Mp1p can severely disturb host signaling cascades during proinflammatory responses by reducing the availabilities of important paracrine signaling molecules.

Plasma mitochondrial DNA and metabolomic alterations in severe critical illness

Background

Cell-free plasma mitochondrial DNA (mtDNA) levels are associated with endothelial dysfunction and differential outcomes in critical illness. A substantial alteration in metabolic homeostasis is commonly observed in severe critical illness. We hypothesized that metabolic profiles significantly differ between critically ill patients relative to their level of plasma mtDNA.

Methods

We performed a metabolomic study with biorepository plasma samples collected from 73 adults with systemic inflammatory response syndrome or sepsis at a single academic medical center. Patients were treated in a 20-bed medical ICU between 2008 and 2010. To identify key metabolites and metabolic pathways related to plasma NADH dehydrogenase 1 (ND1) mtDNA levels in critical illness, we first generated metabolomic data using gas and liquid chromatography-mass spectroscopy. We performed fold change analysis and volcano plot visualization based on false discovery rate-adjusted p values to evaluate the distribution of individual metabolite concentrations relative to ND1 mtDNA levels. We followed this by performing orthogonal partial least squares discriminant analysis to identify individual metabolites that discriminated ND1 mtDNA groups. We then interrogated the entire metabolomic profile using pathway overrepresentation analysis to identify groups of metabolite pathways that were different relative to ND1 mtDNA levels.

Results

Metabolomic profiles significantly differed in critically ill patients with ND1 mtDNA levels ≥ 3200 copies/μl plasma relative to those with an ND1 mtDNA level < 3200 copies/μl plasma. Several analytical strategies showed that patients with ND1 mtDNA levels ≥ 3200 copies/μl plasma had significant decreases in glycerophosphocholines and increases in short-chain acylcarnitines.

Conclusions

Differential metabolic profiles during critical illness are associated with cell-free plasma ND1 mtDNA levels that are indicative of cell damage. Elevated plasma ND1 mtDNA levels are associated with decreases in glycerophosphocholines and increases in short-chain acylcarnitines that reflect phospholipid metabolism dysregulation and decreased mitochondrial function, respectively.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2275-7) contains supplementary material, which is available to authorized users.

Biomarker Phenotype for Early Diagnosis and Triage of Sepsis to the Pediatric Intensive Care Unit

Early diagnosis and triage of sepsis improves outcomes. We aimed to identify biomarkers that may advance diagnosis and triage of pediatric sepsis. Serum and plasma samples were collected from young children (1–23 months old) with sepsis on presentation to the Pediatric Intensive Care Unit (PICU-sepsis, n = 46) or Pediatric Emergency Department (PED-sepsis, n = 58) and PED-non-sepsis patients (n = 19). Multivariate analysis was applied to distinguish between patient groups. Results were compared to our results for older children (2–17 years old). Common metabolites and protein-mediators were validated as potential biomarkers for a sepsis-triage model to differentiate PICU-sepsis from PED-sepsis in children age 1 month-17 years. Metabolomics in young children clearly separated the PICU-sepsis and PED-sepsis cohorts: sensitivity 0.71, specificity 0.93, and AUROC = 0.90 ± 0.03. Adding protein-mediators to the model did not improve performance. The seven metabolites common to the young and older children were used to create the sepsis-triage model. Validation of the sepsis-triage model resulted in sensitivity: 0.83 ± 0.02, specificity: 0.88 ± 0.05 and AUROC 0.93 ± 0.02. The metabolic-based biomarkers predicted which sepsis patients required care in a PICU versus those that could be safely cared for outside of a PICU. This has potential to inform appropriate triage of pediatric sepsis, particularly in EDs with less experience evaluating children.

Clinical Mass Spectrometry in the Bioinformatics Era: A Hitchhiker's Guide

Mass spectrometry (MS) is a sensitive, specific and versatile analytical technique in the clinical laboratory that has recently undergone rapid development. From initial use in metabolic profiling, it has matured into applications including clinical toxicology assays, target hormone and metabolite quantitation, and more recently, rapid microbial identification and antimicrobial resistance detection by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). In this mini-review, we first succinctly outline the basics of clinical mass spectrometry. Examples of hard ionization (electron ionization) and soft ionization (electrospray ionization, MALDI) are presented to demonstrate their clinical applications. Next, a conceptual discourse on mass selection and determination is presented: quadrupole mass filter, time-of-flight mass spectrometer and the Orbitrap; and MS/MS (tandem-in-space, tandem-in-time and data acquisition), illustrated with clinical examples. Current applications in (1) bacterial and fungal identification, antimicrobial susceptibility testing and phylogenetic classification, (2) general unknown urine toxicology screening and expanded new-born metabolic screening and (3) clinical metabolic profiling by gas chromatography are outlined. Finally, major limitations of MS-based techniques, including the technical challenges of matrix effect and isobaric interference; and novel challenges in the post-genomic era, such as protein molecular variants, are critically discussed from the perspective of service laboratories. Computer technology and structural biology have played important roles in the maturation of this field. MS-based techniques have the potential to replace current analytical techniques, and existing expertise and instrument will undergo rapid evolution. Significant automation and adaptation to regulatory requirements are underway. Mass spectrometry is unleashing its potentials in clinical laboratories.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.

Metabolomics in Sepsis and Its Impact on Public Health

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

The role of metabolomic markers for patients with infectious diseases: implications for risk stratification and therapeutic modulation

INTRODUCTION

Metabolomics is a rapidly growing area of research. Metabolomic markers can provide information about the interaction of different organ systems, and thereby improve the understanding of physio-pathological processes, disease risk, prognosis and therapy responsiveness in a variety of diseases. Areas covered: In this narrative review of recent clinical studies investigating metabolomic markers in adult patients presenting with acute infectious disease, we mainly focused on patients with sepsis and lower respiratory tract infections. Currently, there is a growing body of literature showing that single metabolites from distinct metabolic pathways, as well as more complex metabolomic signatures are associated with disease severity and outcome in patients with systemic infections. These pathways include, among others, metabolomic markers of oxidative stress, steroid hormone and amino acid pathways, and nutritional markers. Expert commentary: Metabolic profiling has great potential to optimize patient management, to provide new targets for individual therapy and thereby improve survival of patients. At this stage, research mainly focused on the identification of new predictive signatures and less on metabolic determinants to predict treatment response. The transition from observational studies to implementation of novel markers into clinical practice is the next crucial step to prove the usefulness of metabolomic markers in patient care.

Pyruvate enhancement of cardiac performance: Cellular mechanisms and clinical application

Cardiac contractile function is adenosine-5'-triphosphate (ATP)-intensive, and the myocardium's high demand for oxygen and energy substrates leaves it acutely vulnerable to interruptions in its blood supply. The myriad cardioprotective properties of the natural intermediary metabolite pyruvate make it a potentially powerful intervention against the complex injury cascade ignited by myocardial ischemia-reperfusion. A readily oxidized metabolic substrate, pyruvate augments myocardial free energy of ATP hydrolysis to a greater extent than the physiological fuels glucose, lactate and fatty acids, particularly when it is provided at supra-physiological plasma concentrations. Pyruvate also exerts antioxidant effects by detoxifying reactive oxygen and nitrogen intermediates, and by increasing nicotinamide adenine dinucleotide phosphate reduced form (NADPH) production to maintain glutathione redox state. These enhancements of free energy and antioxidant defenses combine to augment sarcoplasmic reticular Ca²⁺ release and re-uptake central to cardiac mechanical performance and to restore β-adrenergic signaling of ischemically stunned myocardium. By minimizing Ca²⁺ mismanagement and oxidative stress, pyruvate suppresses inflammation in post-ischemic myocardium. Thus, pyruvate administration stabilized cardiac performance, augmented free energy of ATP hydrolysis and glutathione redox systems, and/or quelled inflammation in a porcine model of cardiopulmonary bypass, a canine model of cardiac arrest-resuscitation, and a caprine model of hypovolemia and hindlimb ischemia-reperfusion. Pyruvate's myriad benefits in preclinical models provide the mechanistic framework for its clinical application as metabolic support for myocardium at risk. Phase one trials have demonstrated pyruvate's safety and efficacy for intravenous resuscitation for septic shock, intracoronary infusion for heart failure and as a component of cardioplegia for cardiopulmonary bypass. The favorable outcomes of these trials, which argue for expanded, phase three investigations of pyruvate therapy, mirror findings in isolated, perfused hearts, underscoring the pivotal role of preclinical research in identifying clinical interventions for cardiovascular diseases. Impact statement This article reviews pyruvate's cardioprotective properties as an energy-yielding metabolic fuel, antioxidant and anti-inflammatory agent in mammalian myocardium. Preclinical research has shown these properties make pyruvate a powerful intervention to curb the complex injury cascade ignited by ischemia and reperfusion. In ischemically stunned isolated hearts and in large mammal models of cardiopulmonary bypass, cardiac arrest-resuscitation and hypovolemia, intracoronary pyruvate supports recovery of myocardial contractile function, intracellular Ca²⁺ homeostasis and free energy of ATP hydrolysis, and its antioxidant actions restore β-adrenergic signaling and suppress inflammation. The first clinical trials of pyruvate for cardiopulmonary bypass, fluid resuscitation and intracoronary intervention for congestive heart failure have been reported. Receiver operating characteristic analyses show remarkable concordance between pyruvate's beneficial functional and metabolic effects in isolated, perfused hearts and in patients recovering from cardiopulmonary bypass in which they received pyruvate- vs. L-lactate-fortified cardioplegia. This research exemplifies the translation of mechanism-oriented preclinical studies to clinical application and outcomes.

Mass spectrometry imaging identifies palmitoylcarnitine as an immunological mediator during Salmonella Typhimurium infection

Salmonella Typhimurium causes a self-limiting gastroenteritis that may lead to systemic disease. Bacteria invade the small intestine, crossing the intestinal epithelium from where they are transported to the mesenteric lymph nodes (MLNs) within migrating immune cells. MLNs are an important site at which the innate and adaptive immune responses converge but their architecture and function is severely disrupted during S. Typhimurium infection. To further understand host-pathogen interactions at this site, we used mass spectrometry imaging (MSI) to analyse MLN tissue from a murine model of S. Typhimurium infection. A molecule, identified as palmitoylcarnitine (PalC), was of particular interest due to its high abundance at loci of S. Typhimurium infection and MLN disruption. High levels of PalC localised to sites within the MLNs where B and T cells were absent and where the perimeter of CD169⁺ sub capsular sinus macrophages was disrupted. MLN cells cultured ex vivo and treated with PalC had reduced CD4⁺CD25⁺ T cells and an increased number of B220⁺CD19⁺ B cells. The reduction in CD4⁺CD25⁺ T cells was likely due to apoptosis driven by increased caspase-3/7 activity. These data indicate that PalC significantly alters the host response in the MLNs, acting as a decisive factor in infection outcome.

Talaromyces marneffei Mp1p Is a Virulence Factor that Binds and Sequesters a Key Proinflammatory Lipid to Dampen Host Innate Immune Response

Talaromyces (Penicillium) marneffei is one of the leading causes of systemic mycosis in immunosuppressed or AIDS patients in Southeast Asia. How this intracellular pathogen evades the host immune defense remains unclear. We provide evidence that T. marneffei depletes levels of a key proinflammatory lipid mediator arachidonic acid (AA) to evade the host innate immune defense. Mechanistically, an abundant secretory mannoprotein Mp1p, shown previously to be a virulence factor, does so by binding AA with high affinity via a long hydrophobic central cavity found in the LBD2 domain. This sequesters a critical proinflammatory signaling lipid, and we see evidence that AA, AA's downstream metabolites, and the cytokines interleukin-6 and tumor necrosis factor α are downregulated in T. marneffei-infected J774 macrophages. Given that Mp1p-LBD2 homologs are identified in other fungal pathogens, we expect that this novel class of fatty-acid-binding proteins sequestering key proinflammatory lipid mediators represents a general virulence mechanism of pathogenic fungi.

A Lipidomics Approach in the Characterization of Zika-Infected Mosquito Cells: Potential Targets for Breaking the Transmission Cycle

Recent outbreaks of Zika virus in Oceania and Latin America, accompanied by unexpected clinical complications, made this infection a global public health concern. This virus has tropism to neural tissue, leading to microcephaly in newborns in a significant proportion of infected mothers. The clinical relevance of this infection, the difficulty to perform accurate diagnosis and the small amount of data in literature indicate the necessity of studies on Zika infection in order to characterize new biomarkers of this infection and to establish new targets for viral control in vertebrates and invertebrate vectors. Thus, this study aims at establishing a lipidomics profile of infected mosquito cells compared to a control group to define potential targets for viral control in mosquitoes. Thirteen lipids were elected as specific markers for Zika virus infection (Brazilian strain), which were identified as putatively linked to the intracellular mechanism of viral replication and/or cell recognition. Our findings bring biochemical information that may translate into useful targets for breaking the transmission cycle.

Overlap in serum metabolic profiles between non-related diseases: Implications for LC-MS metabolomics biomarker discovery

Untargeted metabolic profiling has generated large activity in the field of clinical biomarker discovery. Yet, no clinically approved metabolite biomarkers have emerged with failure in validation phases often being a reason. To investigate why, we have applied untargeted metabolic profiling in a retrospective cohort of serum samples representing non-related diseases. Age and gender matched samples from patients diagnosed with pneumonia, congestive heart failure, lymphoma and healthy controls were subject to comprehensive metabolic profiling using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). The metabolic profile of each diagnosis was compared to the healthy control group and significant metabolites were filtered out using t-test with FDR correction. Metabolites found to be significant between each disease and healthy controls were compared and analyzed for overlap. Results show that despite differences in etiology and clinical disease presentation, the fraction of metabolites with an overlap between two or more diseases was 61%. A majority of these metabolites can be associated with immune responses thus representing non-disease specific events. We show that metabolic serum profiles from patients representing non-related diseases display very similar metabolic differences when compared to healthy controls. Many of the metabolites discovered as disease specific in this study have further been associated with other diseases in the literature. Based on our findings we suggest non-related disease controls in metabolomics biomarker discovery studies to increase the chances of a successful validation and future clinical applications.

Lipid metabolites as potential diagnostic and prognostic biomarkers for acute community acquired pneumonia

Early diagnosis of acute community-acquired pneumonia (CAP) is important in patient triage and treatment decisions. To identify biomarkers that distinguish patients with CAP from non-CAP controls, we conducted an untargeted global metabolome analysis for plasma samples from 142 patients with CAP (CAP cases) and 97 without CAP (non-CAP controls). Thirteen lipid metabolites could discriminate between CAP cases and non-CAP controls with area-under-the-receiver-operating-characteristic curve of >0.8 (P ≤ 10⁻⁹). The levels of glycosphingolipids, sphingomyelins, lysophosphatidylcholines and L-palmitoylcarnitine were higher, while the levels of lysophosphatidylethanolamines were lower in the CAP cases than those in non-CAP controls. All 13 metabolites could distinguish CAP cases from the non-infection, extrapulmonary infection and non-CAP respiratory tract infection subgroups. The levels of trihexosylceramide (d18:1/16:0) were higher, while the levels of lysophosphatidylethanolamines were lower, in the fatal than those of non-fatal CAP cases. Our findings suggest that lipid metabolites are potential diagnostic and prognostic biomarkers for CAP.

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Thirteen lipid metabolites could discriminate CAP cases from non-CAP controls.

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The levels of 2 lipid metabolites differ between fatal and non-fatal CAP cases.

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Lipid metabolites are potential diagnostic and prognostic biomarkers for CAP.

Mass spectrometry for the detection of bioterrorism agents: from environmental to clinical applications

In the current context of international conflicts and localized terrorist actions, there is unfortunately a permanent threat of attacks with unconventional warfare agents. Among these, biological agents such as toxins, microorganisms, and viruses deserve particular attention owing to their ease of production and dissemination. Mass spectrometry (MS)-based techniques for the detection and quantification of biological agents have a decisive role to play for countermeasures in a scenario of biological attacks. The application of MS to every field of both organic and macromolecular species has in recent years been revolutionized by the development of soft ionization techniques (MALDI and ESI), and by the continuous development of MS technologies (high resolution, accurate mass HR/AM instruments, novel analyzers, hybrid configurations). New possibilities have emerged for exquisite specific and sensitive detection of biological warfare agents. MS-based strategies for clinical application can now address a wide range of analytical questions mainly including issues related to the complexity of biological samples and their available volume. Multiplexed toxin detection, discovery of new markers through omics approaches, and identification of untargeted microbiological or of novel molecular targets are examples of applications. In this paper, we will present these technological advances along with the novel perspectives offered by omics approaches to clinical detection and follow-up.

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.