Pneumococcal pneumonia: potential for diagnosis through a urinary metabolic profile

Metabolomics of Respiratory Diseases

Metabolomics is an expanding field of systems biology that is gaining significant attention in respiratory research. As a unique approach to understanding and diagnosing diseases, metabolomics provides a snapshot of all metabolites present in biological samples such as exhaled breath condensate, bronchoalveolar lavage, plasma, serum, urine, and other specimens that may be obtained from patients with respiratory diseases. In this article, we review the rapidly expanding field of metabolomics in its application to respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and acute lung injury, along with its more severe form, adult respiratory disease syndrome. We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized occupational and environmental materials. With the latest advances in our understanding of the microbiome, we discuss microbiome-derived metabolites that arise from the gut and lung in asthma and COPD that have mechanistic implications for these diseases. Recent literature has suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe diseases which may be fatal for many patients each year.

Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future

Clinical microbiology has possessed a marvellous past, an important present and a bright future. Western medicine modernization started with the discovery of bacterial pathogens, and from then, clinical bacteriology became a cornerstone of diagnostics. Today, clinical microbiology uses standard techniques including Gram stain morphology, in vitro culture, antigen and antibody assays, and molecular biology both to establish a diagnosis and monitor the progression of microbial infections. Clinical microbiology has played a critical role in pathogen detection and characterization for emerging infectious diseases as evidenced by the ongoing COVID-19 pandemic. Revolutionary changes are on the way in clinical microbiology with the application of “-omic” techniques, including transcriptomics and metabolomics, and optimization of clinical practice configurations to improve outcomes of patients with infectious diseases.

Identification of Potential Urinary Metabolite Biomarkers of Pseudomonas aeruginosa Ventilator-Associated Pneumonia

Introduction:

Ventilator-associated pneumonia (VAP) caused by Pseudomonas aeruginosa is a major cause of morbidity and mortality in hospital intensive care units (ICU). Rapid identification of P. aeruginosa-derived markers in easily accessible patients’ samples can enable an early detection of P. aeruginosa VAP (VAP-PA), thereby stewarding antibiotic use and improving clinical outcomes.

Methods:

Metabolites were analysed using liquid chromatography-mass spectrometry (LC-MS) in prospectively collected urine samples from mechanically ventilated patients admitted to the Antwerp University Hospital ICU. Patients were followed from the start of mechanical ventilation (n = 100 patients) till the time of clinical diagnosis of VAP (n = 13). Patients (n = 8) in whom diagnosis of VAP was further confirmed by culturing respiratory samples and urine samples were studied for semi-quantitative metabolomics.

Results:

We first show that multivariate analyses highly discriminated VAP-PA from VAP–non-PA as well as from the pre-infection groups (R² = .97 and .98, respectively). A further univariate analysis identified 58 metabolites that were significantly elevated or uniquely present in VAP-PA compared to the VAP–non-PA and pre-infection groups (P < .05). These comprised both a known metabolite of histidine as well as a novel nicotine metabolite. Most interestingly, we identified 3 metabolites that were not only highly upregulated for, but were also highly specific to, VAP-PA, as these metabolites were completely absent in all pre-infection timepoints and in VAP–non-PA group.

Conclusions:

Considerable differences exist between urine metabolites in VAP-PA compared to VAP due to other bacterial aetiologies as well to non-VAP (pre-infection) timepoints. The unique urinary metabolic biomarkers we describe here, if further validated, could serve as highly specific diagnostic biomarkers of VAP-PA.

Infection Biomarkers Based on Metabolomics

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

Diagnosis of neonatal sepsis: the past, present and future

Sepsis remains a significant cause of neonatal mortality and morbidity, especially in low- and middle-income countries. Neonatal sepsis presents with nonspecific signs and symptoms that necessitate tests to confirm the diagnosis. Early and accurate diagnosis of infection will improve clinical outcomes and decrease the overuse of antibiotics. Current diagnostic methods rely on conventional culture methods, which is time-consuming, and may delay critical therapeutic decisions. Nonculture-based techniques including molecular methods and mass spectrometry may overcome some of the limitations seen with culture-based techniques. Biomarkers including hematological indices, cell adhesion molecules, interleukins, and acute-phase reactants have been used for the diagnosis of neonatal sepsis. In this review, we examine past and current microbiological techniques, hematological indices, and inflammatory biomarkers that may aid sepsis diagnosis. The search for an ideal biomarker that has adequate diagnostic accuracy early in sepsis is still ongoing. We discuss promising strategies for the future that are being developed and tested that may help us diagnose sepsis early and improve clinical outcomes. IMPACT: Reviews the clinical relevance of currently available diagnostic tests for sepsis. Summarizes the diagnostic accuracy of novel biomarkers for neonatal sepsis. Outlines future strategies including the use of omics technology, personalized medicine, and point of care tests.

Metabolomic profiling of microbial disease etiology in community-acquired pneumonia

Diagnosis of microbial disease etiology in community-acquired pneumonia (CAP) remains challenging. We undertook a large-scale metabolomics study of serum samples in hospitalized CAP patients to determine if host-response associated metabolites can enable diagnosis of microbial etiology, with a specific focus on discrimination between the major CAP pathogen groups S. pneumoniae, atypical bacteria, and respiratory viruses. Targeted metabolomic profiling of serum samples was performed for three groups of hospitalized CAP patients with confirmed microbial etiologies: S. pneumoniae (n = 48), atypical bacteria (n = 47), or viral infections (n = 30). A wide range of 347 metabolites was targeted, including amines, acylcarnitines, organic acids, and lipids. Single discriminating metabolites were selected using Student’s T-test and their predictive performance was analyzed using logistic regression. Elastic net regression models were employed to discover metabolite signatures with predictive value for discrimination between pathogen groups. Metabolites to discriminate S. pneumoniae or viral pathogens from the other groups showed poor predictive capability, whereas discrimination of atypical pathogens from the other groups was found to be possible. Classification of atypical pathogens using elastic net regression models was associated with a predictive performance of 61% sensitivity, 86% specificity, and an AUC of 0.81. Targeted profiling of the host metabolic response revealed metabolites that can support diagnosis of microbial etiology in CAP patients with atypical bacterial pathogens compared to patients with S. pneumoniae or viral infections.

Discovery and predictive modeling of urine microbiome, metabolite and cytokine biomarkers in hospitalized patients with community acquired pneumonia

Pneumonia is the leading cause of infectious related death costing 12 billion dollars annually in the United States alone. Despite improvements in clinical care, total mortality remains around 4%, with inpatient mortality reaching 5–10%. For unknown reasons, mortality risk remains high even after hospital discharge and there is a need to identify those patients most at risk. Also of importance, clinical symptoms alone do not distinguish viral from bacterial infection which may delay appropriate treatment and may contribute to short-term and long-term mortality. Biomarkers have the potential to provide point of care diagnosis, identify high-risk patients, and increase our understanding of the biology of disease. However, there have been mixed results on the diagnostic performance of many of the analytes tested to date. Urine represents a largely untapped source for biomarker discovery and is highly accessible. To test this hypothesis, we collected urine from hospitalized patients with community-acquired pneumonia (CAP) and performed a comprehensive screen for urinary tract microbiota signatures, metabolite, and cytokine profiles. CAP patients were diagnosed with influenza or bacterial (Streptococcus pneumoniae and Staphylococcus aureus) etiologies and compared with healthy volunteers. Microbiome signatures showed marked shifts in taxonomic levels in patients with bacterial etiology versus influenza and CAP versus normal. Predictive modeling of 291 microbial and metabolite values achieved a + 90% accuracy with LASSO in predicting specific pneumonia etiology. This study demonstrates that urine from patients hospitalized with pneumonia may serve as a reliable and accessible sample to evaluate biomarkers that may diagnose etiology and predict clinical outcomes.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

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.

In-hospital metabolite changes in infective endocarditis-a longitudinal 1H NMR-based study

Treatment of infective endocarditis (IE) is a 4-6-week provided course of intravenously administered antibiotics. The aim of this study was to investigate how serum metabolites as measured by proton nuclear magnetic resonance (¹H NMR) spectroscopy are changing over time during the active phase of IE, and to see whether these metabolite changes might be used to monitor recovery in these patients. Patients hospitalized with first-time IE at Herlev Hospital, Denmark, from September 2015 to June 2017 were included. Longitudinal blood sampling was performed and serum was analyzed using ¹H NMR. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was used to separate sample groups and analyze differences in metabolite profiles. Thirteen patients were included in the study (77% men, median age 62 years (IQR 53-77)). All patients were cured during the hospitalization without any relapse during 6 months of follow-up. We analyzed 61 serum samples (median 5 samples, range 2-8 per person) drawn in the treatment period after IE diagnosis. The main changes during the in-hospital period were decreased levels of glucose, mannose, leucine, isoleucine, phenylalanine, tyrosine, and signals from polyols and N-acetylated protein. The metabolomic changes could in contrast to the routinely used parameters CRP and leucocyte levels distinguish between the early and late stages of disease treatment. We present the first longitudinal study of ¹H NMR metabolomics in patients with infective endocarditis. The metabolomic changes show a promising strength compared to routinely used clinical parameters.

Metabolomic-Based Methods in Diagnosis and Monitoring Infection Progression

A robust biomarker screening and validation is crucial for overcoming the current limits in the clinical management of infectious diseases. In this chapter, a general workflow for metabolomics is summarized. Subsequently, an overview of the major contributions of this omics science to the field of biomarkers of infectious diseases is discussed. Different approaches using a variety of analytical platforms can be distinguished to unveil the key metabolites for the diagnosis, prognosis, response to treatment and susceptibility for infectious diseases. To allow the implementation of such biomarkers into the clinics, the performance of large-scale studies employing solid validation criteria becomes essential. Focusing on the etiological agents and after an extensive review of the field, we present a comprehensive revision of the main metabolic biomarkers of viral, bacterial, fungal, and parasitic diseases. Finally, we discussed several articles which show the strongest validation criteria. Following these research avenues, precious clinical resources will be revealed, allowing for reduced misdiagnosis, more efficient therapies, and affordable costs, ultimately leading to a better patient management.

Biomarkers for Acute Respiratory Distress syndrome and prospects for personalised medicine

Acute lung injury (ALI) affects over 10% of patients hospitalised in critical care, with acute respiratory distress syndrome (ARDS) being the most severe form of ALI and having a mortality rate in the region of 40%. There has been slow but incremental progress in identification of biomarkers that contribute to the pathophysiology of ARDS, have utility in diagnosis and monitoring, and that are potential therapeutic targets (Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, Thompson T, Ware LB, Matthay MA, Lancet Respir Med 2014, 2:611–-620). However, a major issue is that ARDS is such a heterogeneous, multi-factorial, end-stage condition that the strategies for “lumping and splitting” are critical (Prescott HC, Calfee CS, Thompson BT, Angus DC, Liu VX, Am J Respir Crit Care Med 2016, 194:147–-155). Nevertheless, sequencing of the human genome, the availability of improved methods for analysis of transcription to mRNA (gene expression), and development of sensitive immunoassays has allowed the application of network biology to ARDS, with these biomarkers offering potential for personalised or precision medicine (Sweeney TE, Khatri P, Toward precision medicine Crit Care Med; 2017 45:934-939).

Biomarker panels have potential applications in molecular phenotyping for identifying patients at risk of developing ARDS, diagnosis of ARDS, risk stratification and monitoring. Two subphenotypes of ARDS have been identified on the basis of blood biomarkers: hypo-inflammatory and hyper-inflammatory. The hyper-inflammatory subphenotype is associated with shock, metabolic acidosis and worst clinical outcomes. Biomarkers of particular interest have included interleukins (IL-6 and IL-8), interferon gamma (IFN-γ), surfactant proteins (SPD and SPB), von Willebrand factor antigen, angiopoietin 1/2 and plasminogen activator inhibitor-1 (PAI-1). In terms of gene expression (mRNA) in blood there have been found to be increases in neutrophil-related genes in sepsis-induced and influenza-induced ARDS, but whole blood expression does not give a robust diagnostic test for ARDS.

Despite improvements in management of ARDS on the critical care unit, this complex disease continues to be a major life-threatening event. Clinical trials of β₂-agonists, statins, surfactants and keratinocyte growth factor (KGF) have been disappointing. In addition, monoclonal antibodies (anti-TNF) and TNFR fusion protein have also been unconvincing. However, there have been major advances in methods of mechanical ventilation, a neuromuscular blocker (cisatracurium besilate) has shown some benefit, and stem cell therapy is being developed. In the future, by understanding the role of biomarkers in the pathophysiology of ARDS and lung injury, it is hoped that this will provide rational therapeutic targets and ultimately improve clinical care (Seymour CW, Gomez H, Chang CH, Clermont G, Kellum JA, Kennedy J, Yende S, Angus DC, Crit Care 2017, 21:257).

The impact of exercise training on the lipid peroxidation metabolomic profile and respiratory infection risk in older adults

Aging is associated with oxidative stress that may increase susceptibility to respiratory infections (RIs). We aimed to assess the impact of exercise training on the risk of RIs in older adults and on a targeted metabolomic profile of stress oxidative lipid peroxidation-related metabolites.

METHODS

In an 8-month clinical trial, 38 participants over 60 years of age were allocated to an exercise group (EG), in which participants underwent 90-min training sessions three times/week(n = 20), or a control group (CG), in which participants maintained daily physical activities(n = 18). Daily respiratory symptoms and RIs number and severity were collected. Serum by-products were assessed by comprehensive two-dimensional gas chromatography coupled to mass spectrometry with time of flight analyzer. Serum metabolomic profiling comprised 76 metabolites (alcohols, aldehydes, alkanes, and ketones). Principal components analysis and ANOVA-simultaneous component analysis were used to evaluate the metabolomic profile change.

RESULTS

The odds ratio of RIs for the EG was 2.0 CI 95% [0.2;25]. The incidence of RIs was 47% [23;70] in the EG vs. 44%[12;77] in the CG. The metabolomic profiling showed that alkanes and aldehydes classes differed between the EG and the CG before and after intervention. A calibration model showed a relation between the metabolites from four main classes (ketones, alcohols, alkanes and aldehydes) and the prediction of the number of RIs.

CONCLUSION

Moderate exercise training, in older adults, compared with no exercise in controls, did not show a difference in the risk of RIs. A pattern of lipid peroxidation was associated with the number of RIs.

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.

The procalcitonin/albumin ratio as an early diagnostic predictor in discriminating urosepsis from patients with febrile urinary tract infection

Discrimination between urosepsis and febrile urinary tract infections is important in therapeutic decision-making to indicate suitable treatments to avoid sepsis-related organ failure. Accurate diagnosis is time-consuming and susceptible to false-positive results. Moreover, patient responses to urosepsis are complex and varied. Therefore, this study aimed to develop a new, early diagnostic predictor that could discriminate between patients with urosepsis and those with febrile urinary tract infections using a combination of initial procalcitonin and albumin levels.We conducted a retrospective study involving 140 patients with febrile urinary tract infections from January 2013 to December 2017. Univariate and multivariate logistic analyses were performed to identify the independent risk factors for differentiating urosepsis from febrile urinary tract infection. A receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the procalcitonin/albumin ratio.Patients in the urosepsis group had higher procalcitonin/albumin ratios compared to those in the febrile urinary tract infection group [2.254 (0.978, 6.299) vs 0.021 (0.004, 0.095); P < .001]. Based on multivariate logistic analysis, the procalcitonin/albumin ratio [adjusted odds ratio (OR) 1.029, 95% confidence interval (CI) 1.013-1.045, P < .001] was an independent predictor of urosepsis, which allowed for differentiation from patients with febrile urinary tract infections. The area under the ROC curve (AUC) for the procalcitonin/albumin ratio was 0.937 (95% CI, 0.894-0.980); P < .001. The sensitivity and specificity of the procalcitonin/albumin ratio cut-off values (>0.44) were 84.62% and 96.00%, respectively. Moreover, in the subset of 65 patients with urosepsis, the procalcitonin/albumin ratio in the uroseptic shock group was higher than in the group of patients without uroseptic shock [5.46 (1.43, 6.58) vs 1.24 (0.63, 4.38); P = .009].Our study demonstrates that the procalcitonin/albumin ratio is an early diagnostic predictor that can discriminate between urosepsis and febrile urinary tract infection. Additionally, in patients with urosepsis, those with higher procalcitonin/albumin ratios were more prone to uroseptic shock. Our findings suggest that the procalcitonin/albumin ratio is a rapid and relatively low-cost biomarker that can be used in clinical practice.

Evolution of ARDS biomarkers: Will metabolomics be the answer?

To date, there is no clinically agreed-upon diagnostic test for acute respiratory distress syndrome (ARDS): the condition is still diagnosed on the basis of a constellation of clinical findings, laboratory tests, and radiological images. Development of ARDS biomarkers has been in a state of continuous flux during the past four decades. To address ARDS heterogeneity, several studies have recently focused on subphenotyping the disease on the basis of observable clinical characteristics and associated blood biomarkers. However, the strong correlation between identified biomarkers and ARDS subphenotypes has yet to establish etiology; hence, there is a need for the adoption of other methodologies for studying ARDS. In this review, we will shed light on ARDS metabolomics research in the literature and discuss advances and major obstacles encountered in ARDS metabolomics research. Generally, the ARDS metabolomics studies focused on identification of differentiating metabolites for diagnosing ARDS, but they were performed to different standards in terms of sample size, selection of control cohort, type of specimens collected, and measuring technique utilized. Virtually none of these studies have been properly validated to identify true metabolomics biomarkers of ARDS. Though in their infancy, metabolomics studies exhibit promise to unfold the biological processes underlying ARDS and, in our opinion, have great potential for pushing forward our present understanding of ARDS.

Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity

Background

This study aimed to determine whether community-acquired pneumonia (CAP) had a metabolic profile and whether this profile can be used for disease severity assessment.

Methods

A total of 175 individuals including 119 CAP patients and 56 controls were enrolled and divided into two cohorts. Serum samples from a discovery cohort (n = 102, including 38 non-severe CAP, 30 severe CAP, and 34 age and sex-matched controls) were determined by untargeted ultra-high-performance liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based metabolomics. Selected differential metabolites between CAP patients versus controls, and between the severe CAP group versus non-severe CAP group, were confirmed by targeted mass spectrometry assays in a validation cohort (n = 73, including 32 non-severe CAP, 19 severe CAP and 22 controls). Pearson’s correlation analysis was performed to assess relationships between the identified metabolites and clinical severity of CAP. The area under the curve (AUC), sensitivity and specificity of the metabolites for predicting the severity of CAP were also investigated.

Results

The metabolic signature was markedly different between CAP patients and controls. Fifteen metabolites were found to be significantly dysregulated in CAP patients, which were mainly mapped to the metabolic pathways of sphingolipid, arginine, pyruvate and inositol phosphate. The alternation trends of five metabolites among the three groups including sphinganine, p-Cresol sulfate, dehydroepiandrosterone sulfate (DHEA-S), lactate and l-arginine in the validation cohort were consistent with those in the discovery cohort. Significantly lower concentrations of sphinganine, p-Cresol sulfate and DHEA-S were observed in CAP patients than in controls (p < 0.05). Serum lactate and sphinganine levels were positively correlated with confusion, urea level, respiratory rate, blood pressure, and age > 65 years (CURB-65), pneumonia severity index (PSI) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, while DHEA-S inversely correlated with the three scoring systems. Combining lactate, sphinganine and DHEA-S as a metabolite panel for discriminating severe CAP from non-severe CAP exhibited a better AUC of 0.911 (95% confidence interval 0.825–0.998) than CURB-65, PSI and APACHE II scores.

Conclusions

This study demonstrates that serum metabolomics approaches based on the LC-MS/MS platform can be applied as a tool to reveal metabolic changes during CAP and establish a metabolite signature related to disease severity.

Trial registration

ClinicalTrials.gov, NCT03093220. Registered retrospectively on 28 March 2017.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2049-2) contains supplementary material, which is available to authorized users.

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.

Diagnostics for neonatal sepsis: current approaches and future directions

Progress has been made in the reduction of morbidity and mortality from neonatal sepsis. However, diagnosis continues to rely primarily on conventional microbiologic techniques, which can be inaccurate. The objective of this review is to provide the clinician with an overview of the current information available on diagnosing this condition. We review currently available diagnostic approaches for documenting neonatal sepsis and also describe novel approaches for diagnosing infection in neonates who are under development and investigation. Substantial progress has been made with molecular approaches and further development of non-culture-based methods offer promise. The potential ability to incorporate antimicrobial resistance gene testing in addition to pathogen identification may provide a venue to incorporate a predominantly molecular platform into a larger program of neonatal care.

Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia

Background

Metabolomics is a tool that has been used for the diagnosis and prognosis of specific diseases. The purpose of this study was to examine if metabolomics could be used as a potential diagnostic and prognostic tool for H1N1 pneumonia. Our hypothesis was that metabolomics can potentially be used early for the diagnosis and prognosis of H1N1 influenza pneumonia.

Methods

¹H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry were used to profile the metabolome in 42 patients with H1N1 pneumonia, 31 ventilated control subjects in the intensive care unit (ICU), and 30 culture-positive plasma samples from patients with bacterial community-acquired pneumonia drawn within the first 24 h of hospital admission for diagnosis and prognosis of disease.

Results

We found that plasma-based metabolomics from samples taken within 24 h of hospital admission can be used to discriminate H1N1 pneumonia from bacterial pneumonia and nonsurvivors from survivors of H1N1 pneumonia. Moreover, metabolomics is a highly sensitive and specific tool for the 90-day prognosis of mortality in H1N1 pneumonia.

Conclusions

This study demonstrates that H1N1 pneumonia can create a quite different plasma metabolic profile from bacterial culture-positive pneumonia and ventilated control subjects in the ICU on the basis of plasma samples taken within 24 h of hospital/ICU admission, early in the course of disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1672-7) contains supplementary material, which is available to authorized users.

Metabolic Profiling in Patients with Pneumonia on Intensive Care

Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care.

In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D ¹H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R²Y = 0.91, Q²Y = 0.28, p = 0.02) and those with VAP from those without (R²Y = 0.94, Q²Y = 0.27, p = 0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins.

Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics.

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Metabolic profiling of serum from ventilated patients shows potential to aid in the diagnosis of pneumonia and ventilator associated pneumonia.

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Lipid species were particularly important in the discrimination of pneumonia and ventilator associated pneumonia from those with brain injuries.

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Metabolites including glycoproteins, amino acids and phospholipids also aided in discrimination.

Pneumonia is a common problem in patients requiring breathing support in Intensive Care Units (ICU). When pneumonia develops patients take longer to get back to full health and in some cases may die. Current diagnostic tests are not always helpful.

We measured an array of molecules, metabolites, in the blood of patients in ICU to compare those with pneumonia to those with brain injuries. We found that substances including lipids, amino acids and glycoproteins showed some ability to differentiate patients with pneumonia from other critically unwell patients. This technique, used with clinical data, may help improve the diagnosis of pneumonia.

Tolerability and safety of the intake of bovine milk oligosaccharides extracted from cheese whey in healthy human adults

Mechanistic research suggests a unique evolutionary relationship between complex milk oligosaccharides and cognate bifidobacteria enriched in breast-fed infants. Bovine milk oligosaccharides (BMO) were recently identified as structurally and functionally similar to human milk oligosaccharides. The present single-blind three-way crossover study is the first to determine the safety and tolerability of BMO consumption by healthy human participants (n 12) and its effects on faecal microbiota and microbial metabolism. Participants consumed each supplement (placebo-control; low- and high-BMO doses) for eleven consecutive days, followed by a 2-week washout period prior to initiating the next supplement arm. Low and high BMO doses were consumed as 25 and 35 % of each individual's daily fibre intake, respectively. Safety and tolerability were measured using standardised questionnaires on gut and stomach discomfort and stool consistency. Faecal extracts were profiled for bacterial populations by next-generation sequencing (NGS) and bifidobacteria presence was confirmed using quantitative PCR. Urine was analysed for changes in microbial metabolism using nuclear magnetic resonance spectroscopy (1H-NMR). Consumption of both the low and high BMO doses was well tolerated and did not change stool consistency from baseline. Multivariate analysis of the NGS results demonstrated no change in faecal microbiota phyla among the placebo-control and BMO supplement groups. In conclusion, BMO supplementation was well tolerated in healthy adults and has the potential to shift faecal microbiota toward beneficial strains as part of a synbiotic treatment with probiotic cultures that selectively metabolise oligosaccharides.

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.

Role of metabolism during viral infections, and crosstalk with the innate immune system

Viruses have been for long polemic biological particles which stand in the twilight of being living entities or not. As their genome is reduced, they rely on the metabolic machinery of their host in order to replicate and be able to continue with their infection process. The understanding of their metabolic requirements is thus of paramount importance in order to develop tailored drugs to control their population, without affecting the normal functioning of their host. New advancements in high throughput technologies, especially metabolomics are allowing researchers to uncover the metabolic mechanisms of viral replication. In this short review, we present the latest discoveries that have been made in the field and an overview of the intrinsic relationship between metabolism and innate immunity as an important part of the immune system.

Tuberculous meningitis in infants and children: Insights from nuclear magnetic resonance metabolomics

Abstract Tuberculous meningitis (TBM) is a prevalent form of central nervous system tuberculosis (CNS-TB) and the most severe common form of bacterial meningitis in infants and children below the age of 13 years, especially in the Western Cape Province of South Africa. Research to identify markers for timely and accurate diagnosis and treatment outcomes remains high on the agenda for TBM, in respect of which the field of metabolomics is as yet largely unexploited. However, the national Department of Science and Technology (DST) recently established several biotechnology platforms at South African institutions, including one for metabolomics hosted at North-West University. We introduce this national platform for nuclear magnetic resonance (NMR) metabolomics by providing an overview of work on TBM. We focus on selected collaborative multidisciplinary approaches to this disease and conclude with the outcomes of an untargeted NMR metabolomics study of cerebrospinal fluid from TBM patients. This study enabled the formulation of a conceptual shuttle representing the unique metabolic plasticity of CNS metabolism towards the energy requirements for the microglia-driven neuroinflammatory responses, of which TBM is one example. From insights generated by this explorative NMR metabolomics investigation, we propose directions for future in-depth research strategies to address this devastating disease. In our view, the timely initiative of the DST, the operational expertise in metabolomics now available and the potential for involving national and international networks in this field of research offers remarkable opportunities for the future of metabolomics in South Africa and for an ever greater understanding of disease mechanisms.

Metabonomics and intensive care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

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.

Metabolomics and Its Application to Acute Lung Diseases

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

Serum procalcitonin levels in combination with (1)H NMR spectroscopy: A rapid indicator for differentiation of urosepsis

BACKGROUND

Urosepsis, a severe form of sepsis requires immediate medical attention for prognosis. It is clinically diagnosed by estimating serum procalcitonin (PCT) levels along with time taking urine and blood cultures. We explored NMR based profiling, deriving metabolites that could potentially aid diagnosis.

METHODS

The proton NMR of serum and urine samples of healthy control subjects (n=32) and urosepsis cases (n=35) based on PCT levels, were analyzed. Four clinically identified non-urosepsis cases with high PCT levels were also differentiated through principal component analysis (PCA) of the serum samples.

RESULTS

Quantification of serum and urine through Discriminant Function Analysis (DFA) afforded 93.7% and 91.7% correct classification respectively, along with identification of malonate and urea as potential biomarkers for the disease in both urine and serum samples. The partial least square discriminant analysis (PLS-DA) showed an R(2) value of 0.97 in both biofluids with Q(2)=0.87 and 0.85 for serum and urine respectively. The training set of serum samples provided precise prediction of the test set in a small cohort through random re-sampling method, while in urine samples, the predictions were inconclusive.

CONCLUSIONS

Our pilot study reveals that (1)H NMR of serum metabolic profiling in combination with PCT levels may provide a rapid method for differentiation of urosepsis.

A review of the role of Haemophilus influenzae in community-acquired pneumonia

In an era when Haemophilus influenzae type b (Hib) conjugate vaccine is widely used, the incidence of Hib as a cause of community-acquired pneumonia (CAP) has dramatcally declined. Non-typeable H. influenzae (NTHi) strains and, occasionally, other encapsulated serotypes of H. influenzae are now the cause of the majority of invasive H. influenzae infectons, including bacteraemic CAP. NTHi have long been recognised as an important cause of lower respiratory tract infecton, including pneumonia, in adults, especially those with underlying diseases. The role of NTHi as a cause of non-bacteraemic CAP in children is less clear. In this review the evidence for the role of NTHi and capsulated strains of H. influenzae will be examined.

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.

A metabolomics approach to studying the effects of Jinxin oral liquid on RSV-infected mice using UPLC/LTQ-Orbitrap mass spectrometry

ETHNOPHARMACOLOGICAL RELEVANCE

Jinxin oral liquid (JOL) is a traditional Chinese medicine (TCM) formula modified from ma-xing-shi-gan-tang, an ancient formula widely used in the treatment of respiratory diseases such as bronchitis, pneumonia, and asthma. In our previous studies, JOL was shown to safely and effectively treat viral pneumonia, especially that involving respiratory syncytial virus (RSV).

AIM OF THE STUDY

To investigate the mechanism of the effect of JOL in RSV infected mice, using a metabolomics approach based on ultra-performance liquid chromatography coupled with linear ion trap quadrupole-Orbitrap mass spectrometry (UPLC/LTQ-Orbitrap-MS).

MATERIALS AND METHODS

BALB/c mice were divided into four groups, the control group (saline inoculation/no treatment), RSV group (RSV inoculation/saline treatment), RSV+JOL group (RSV inoculation/JOL treatment), and RSV+Riba group (RSV inoculation/ribavirin treatment). Plasma and lung tissue samples were collected 7 days after the inoculation/treatment protocols, and UPLC/LTQ-Orbitrap-MS method based on metabolomics was developed. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were utilized to identify biomarkers potentially associated with the anti-RSV activity of JOL.

RESULTS

JOL was associated with reduced inflammatory responses in RSV-infected lung tissue. The combination of PCA and OPLS-DA revealed deviations in 11 biomarkers in plasma, and 16 biomarkers in lung tissue induced by RSV that were corrected with JOL treatment. These biomarkers were primarily components of metabolic pathways involving glycerophosphocholines, sphingolipids, and glycerolipids. JOL was able to restore the abnormal levels of these biomarkers detected in the plasma and lung tissue of RSV-infected mice to approximately normal levels.

CONCLUSIONS

This study suggested that JOL can treat RSV pneumonia effectively, partially by ameliorating the associated disturbances to lipid metabolism. The results provided insight into the anti-RSV mechanism of JOL, and also demonstrated that metabolomics is a valuable tool for investigating the efficacy of TCM treatment for RSV pneumonia, and the associated biomarkers involved.

Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis

BACKGROUND

Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much.

METHODS

Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann-Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects.

RESULTS

From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863-0.944), 0.93 (95% CI: 0.893-0.966), and 0.964 (95% CI: 00.941-0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991.

CONCLUSION

The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.

Metabolomics specificity of tuberculosis plasma revealed by (1)H NMR spectroscopy

Tuberculosis (TB) is a communicable disease of major global importance and causes metabolic disorder of the patients. In a previous study, we found that the plasma metabolite profile of TB patients differs from that of healthy control subjects based on nuclear magnetic resonance (NMR) spectroscopy. In order to evaluate the TB specificity of the metabolite profile, a total of 110 patients, including 40 with diabetes, 40 with malignancy, and 30 with community-acquired pneumonia (CAP), assessed by NMR spectroscopy, and compared to those of patients with TB. Based on the orthogonal partial least-squares discriminant analysis (OPLS-DA), the metabolic profiles of these diseases were significant different, as compared to the healthy controls and TB patients, respectively. The score plots of the OPLS-DA model demonstrated that TB was easily distinguishable from diabetes, CAP and malignancy. Plasma levels of ketone bodies, lactate, and pyruvate were increased in TB patient compared to healthy control, but lower than CAP and malignancy. We conclude that the metabolic profiles were TB-specific and reflected MTB infection. Our results strongly support the NMR spectroscopy-based metabolomics could contribute to an improved understanding of disease mechanisms and may offer clues to new TB clinic diagnosis and therapies.

Biomarkers for community-acquired pneumonia in the emergency department

Community-acquired pneumonia is one of the most common reasons for emergency department (ED) visits in children and adults. Despite its prevalence, there are many challenges to proper diagnosis and management of pneumonia. There is no accurate and timely etiologic gold standard to differentiate bacterial from viral disease, and there are limitations with precise risk stratification of patients to ensure appropriate site-of-care decisions. Clinical factors obtained by history and physical examination have limited the ability to diagnose pneumonia etiology and severity. Biomarkers offer information about the host response to infection and pathogen activity within the host that can serve to augment clinical features in decision-making. As science and technology progress, novel biomarkers offer great potential in aiding critical decisions for patients with pneumonia. This review summarizes existing knowledge about biomarkers of host response and pathogen activity, in addition to briefly reviewing emerging biomarkers using novel technologies.

Metabolomics of bronchoalveolar lavage differentiate healthy HIV-1-infected subjects from controls

Despite antiretroviral therapy, pneumonias from pathogens such as pneumococcus continue to cause significant morbidity and mortality in HIV-1-infected individuals. Respiratory infections occur despite high CD4 counts and low viral loads; therefore, better understanding of lung immunity and infection predictors is necessary. We tested whether metabolomics, an integrated biosystems approach to molecular fingerprinting, could differentiate such individual characteristics. Bronchoalveolar lavage fluid (BALf ) was collected from otherwise healthy HIV-1-infected individuals and healthy controls. A liquid chromatography-high-resolution mass spectrometry method was used to detect metabolites in BALf. Statistical and bioinformatic analyses used false discovery rate (FDR) and orthogonally corrected partial least-squares discriminant analysis (OPLS-DA) to identify groupwise discriminatory factors as the top 5% of metabolites contributing to 95% separation of HIV-1 and control. We enrolled 24 subjects with HIV-1 (median CD4=432) and 24 controls. A total of 115 accurate mass m/z features from C18 and AE analysis were significantly different between HIV-1 subjects and controls (FDR=0.05). Hierarchical cluster analysis revealed clusters of metabolites, which discriminated the samples according to HIV-1 status (FDR=0.05). Several of these did not match any metabolites in metabolomics databases; mass-to-charge 325.065 ([M+H](+)) was significantly higher (FDR=0.05) in the BAL of HIV-1-infected subjects and matched pyochelin, a siderophore-produced Pseudomonas aeruginosa. Metabolic profiles in BALf differentiated healthy HIV-1-infected subjects and controls. The lack of association with known human metabolites and inclusion of a match to a bacterial metabolite suggest that the differences could reflect the host's lung microbiome and/or be related to subclinical infection in HIV-1-infected patients.

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.

Diagnosis of pneumococcal pneumonia: current pitfalls and the way forward

Streptococcus pneumoniae is the most common cause of community-acquired pneumonia. However, it can also asymptomatically colonize the upper respiratory tract. Because of the need to distinguish between S. pneumoniae that is simply colonizing the upper respiratory tract and S. pneumoniae that is causing pneumonia, accurate diagnosis of pneumococcal pneumonia is a challenging issue that still needs to be solved. Sputum Gram stains and culture are the first diagnostic step for identifying pneumococcal pneumonia and provide information on antibiotic susceptibility. However, these conventional methods are relatively slow and insensitive and show limited specificity. In the past decade, new diagnostic tools have been developed, particularly antigen (teichoic acid and capsular polysaccharides) and nucleic acid (ply, lytA, and Spn9802) detection assays. Use of the pneumococcal antigen detection methods along with biomarkers (C-reactive protein and procalcitonin) may enhance the specificity of diagnosis for pneumococcal pneumonia. This article provides an overview of current methods of diagnosing pneumococcal pneumonia and discusses new and future test methods that may provide the way forward for improving its diagnosis.

Is 1H NMR metabolomics becoming the promising early biomarker for neonatal sepsis and for monitoring the antibiotic toxicity?

Metabolomics, the latest of omics disciplines, has been successfully used in various fields of basic research such as pharmacology and toxicology. Recently, this new science has gained an important role in the translational research of diagnostics. In this regard, the challenge for neonatologists and medical laboratories is to diagnose neonatal sepsis, a disease with high mortality and morbidity due to the difficulty in diagnosing it. Metabolomics, through its ability to identify perturbations caused by this condition, aims at recognizing metabolites that characterize neonatal sepsis with high specificity and sensitivity. The purpose of this review is to highlight the ability of metabolomics to find early biomarkers for this condition, as well as to predict the toxic effects caused by antibiotics.

Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy

BACKGROUND AND AIMS

Inflammatory bowel disease, a chronic inflammation of the intestinal tract, presents in two variations, Ulcerative Colitis (UC) and Crohn's disease (CD). Given that treatment of CD differs from UC, a single test that provided strong diagnostic ability would offer great clinical value. Two previous studies have indicated that CD can be distinguished from UC, and that both can be distinguished from non-IBD-type gastrointestinal disease, based on urinary and faecal metabolite profiling.

METHODS

Analysis of healthy as well as CD and UC patients attending an IBD clinic was performed. IBD patients were classified into two groups (CD or UC) based on chart review of clinical, endoscopic, and histological assessment. Urine samples were obtained and analyzed using nuclear magnetic resonance (NMR) spectroscopy combined with targeted profiling techniques, followed by univariate and multivariate statistical analysis.

RESULTS

Based on urinary metabolomics, individuals with IBD could be differentiated from healthy. Major differences between IBD and healthy included TCA cycle intermediates, amino acids, and gut microflora metabolites. Comparison of CD and UC patients revealed discrimination, but removal of patients with the surgical intervention confounder revealed that CD could not be discriminated from UC.

CONCLUSIONS

This study highlights the potential for metabolomics to distinguish IBD from the healthy state but shows that careful consideration must be given to establishing disease-representative cohorts that are free of confounding factors.

New markers in pneumonia

Pneumonia is one of the most common causes of death from infectious diseases worldwide, and the most common fatal infection acquired in hospitals. Despite advances in prevention strategies, such as antibiotic therapies and intensive care, significant improvement in the mortality rate is still lacking. This high mortality is largely due to the limitations in current clinical practices and laboratory tests, which delay the timing of adequate antibiotic therapy. In recent years, many indicators (biomarkers) are present in scenarios where infectious pathogens invade into the body. These biomarkers, as reflected in specific biological responses to infections, have been reported to demonstrate the ability to facilitate the diagnosis, risk stratification, and management of pneumonia. This review provides a schematic overview of these new potential biomarkers based on the categories of (1) microorganisms and their derivatives, (2) inflammation mediators, (3) inflammation response proteins, and (4) stress-sensing proteins. In addition, approaches to identifying new biomarkers are also briefly introduced. Although no current biomarker can solely achieve a definitive diagnosis, many of them can be complemented, rather than replaced outright, in routine clinical practices to improve decision-making processes regarding pneumonia.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Metabolomics of cerebrospinal fluid from humans treated for rabies

Rabies is a rapidly progressive lyssavirus encephalitis that is statistically 100% fatal. There are no clinically effective antiviral drugs for rabies. An immunologically naïve teenager survived rabies in 2004 through improvised supportive care; since then, 5 additional survivors have been associated with use of the so-called Milwaukee Protocol (MP). The MP applies critical care focused on the altered metabolic and physiologic states associated with rabies. The aim of this study was to examine the metabolic profile of cerebrospinal fluid (CSF) from rabies patients during clinical progression of rabies encephalitis in survivors and nonsurvivors and to compare these samples with control CSF samples. Unsupervised clustering algorithms distinguished three stages of rabies disease and identified several metabolites that differentiated rabies survivors from those who subsequently died, in particular, metabolites related to energy metabolism and cell volume control. Moreover, for those patients who survived, the trajectory of their metabolic profile tracked toward the control profile and away from the rabies profile. NMR metabolomics of human rabies CSF provide new insights into the mechanisms of rabies pathogenesis, which may guide future therapy of this disease.

Community-Acquired Pneumonia in the Conjugate Vaccine Era

Community-acquired pneumonia (CAP) remains one of the most common serious infections encountered among children worldwide. In this review, we highlight important literature and recent scientific discoveries that have contributed to our current understanding of pediatric CAP. We review the current epidemiology of childhood CAP in the developed world, appraise the state of diagnostic testing for etiology and prognosis, and discuss disease management and areas for future research in the context of recent national guidelines.

The dual role of biomarkers for understanding basic principles and devising novel intervention strategies in tuberculosis

There is great need for better control measures for tuberculosis (TB). High-throughput analyses, such as transcriptomic and metabolic profiling, offer a promising path toward clinically useful biosignatures. With the help of biomarkers, it will be possible not only to reliably perform diagnosis but also to gain a better understanding of the disease process and, in the future, even predict the onset of disease in infected individuals. Biomarkers based on transcriptomic and metabolic profiles as well as on cytokine composition provide important insights into the basic biological principles of TB and give an opportunity to reliably distinguish TB patients from healthy individuals. Use of biomarkers for point-of-care diagnosis, however, is still a distant goal, which to achieve will require extensive analysis of TB biosignatures across different cohorts and a combination of different platforms.