PRIMARY BIOMARKERS IN CEREBRAL SPINAL FLUID OBTAINED FROM PATIENTS WITH INFLUENZA-ASSOCIATED ENCEPHALOPATHY ANALYZED BY METABOLOMICS

Insights into the biomarkers of viral encephalitis from clinical patients

BACKGROUND

biomarkers can be helpful in identifying patients who may profit by explicit treatments or evaluating the reaction to the treatment of specific disease. Finding unique biomarkers in the process of disease could help clinicians in identifying serious disease in the early stage, so as to improve prognosis.

OBJECTIVE

these investigations, nonetheless, have made constrained progress. Numerous infections are known to cause intense viral encephalitis (VE) in people which can cause a variable level of meningeal just as parenchymal aggravation. Initial clinical manifestations in most encephalitis are nonspecific, resembling a viral-like illness. However, with disease progression, symptoms can become quite severe and fatal, including prominent cranial hypertension, cognitive problems, cerebral hernia and respiratory failure. Forwards: the clinical and research center discoveries in huge numbers of those viral issues are to a great extent comparable and in this way increasingly explicit biomarkers for indicative and prognostic intentions are justified. These biomarkers are progressively significant in the acknowledgment and treatment of the viral central nervous system (CNS) issue.

CONCLUSION

Clinical manifestations have been the indicative approaches for analysis of viral encephalitis. Lots of studies have been endeavored to distinguish progressively objective laboratory-based quantitative CSF biomarkers for VE.

Single Cell Metabolomics: A Future Tool to Unmask Cellular Heterogeneity and Virus-Host Interaction in Context of Emerging Viral Diseases

Viral emergence is an unpredictable but obvious event, particularly in the era of climate change and globalization. Efficient management of viral outbreaks depends on pre-existing knowledge and alertness. The potential hotspots of viral emergence often remain neglected and the information related to them is insufficient, particularly for emerging viruses. Viral replication and transmission rely upon usurping the host metabolic machineries. So altered host metabolic pathways can be exploited for containment of these viruses. Metabolomics provides the insight for tracing out such checkpoints. Consequently introspection of metabolic alteration at virus-host interface has evolved as prime area in current virology research. Chromatographic separation followed by mass spectrometry has been used as the predominant analytical platform in bulk of the analyses followed by nuclear magnetic resonance (NMR) and fluorescence based techniques. Although valuable information regarding viral replication and modulation of host metabolic pathways have been extracted but ambiguity often superseded the real events due to population effect over the infected cells. Exploration of cellular heterogeneity and differentiation of infected cells from the nearby healthy ones has become essential. Single cell metabolomics (SCM) emerges as necessity to explore such minute details. Mass spectrometry imaging (MSI) coupled with several soft ionization techniques such as electrospray ionization (ESI), laser ablation electrospray ionization (LAESI), matrix assisted laser desorption/ionization (MALDI), matrix-free laser desorption ionization (LDI) have evolved as the best suited platforms for SCM analyses. The potential of SCM has already been exploited to resolve several biological conundrums. Thus SCM is knocking at the door of virus-host interface.

Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (HRM) of H1N1 influenza virus infection in mice

Influenza is a significant health concern worldwide. Viral infection induces local and systemic activation of the immune system causing attendant changes in metabolism. High-resolution metabolomics (HRM) uses advanced mass spectrometry and computational methods to measure thousands of metabolites inclusive of most metabolic pathways. We used HRM to identify metabolic pathways and clusters of association related to inflammatory cytokines in lungs of mice with H1N1 influenza virus infection. Infected mice showed progressive weight loss, decreased lung function, and severe lung inflammation with elevated cytokines [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and increased oxidative stress via cysteine oxidation. HRM showed prominent effects of influenza virus infection on tryptophan and other amino acids, and widespread effects on pathways including purines, pyrimidines, fatty acids, and glycerophospholipids. A metabolome-wide association study (MWAS) of the aforementioned inflammatory cytokines was used to determine the relationship of metabolic responses to inflammation during infection. This cytokine-MWAS (cMWAS) showed that metabolic associations consisted of distinct and shared clusters of 396 metabolites highly correlated with inflammatory cytokines. Strong negative associations of selected glycosphingolipid, linoleate, and tryptophan metabolites with IFN-γ contrasted strong positive associations of glycosphingolipid and bile acid metabolites with IL-1β, TNF-α, and IL-10. Anti-inflammatory cytokine IL-10 had strong positive associations with vitamin D, purine, and vitamin E metabolism. The detailed metabolic interactions with cytokines indicate that targeted metabolic interventions may be useful during life-threatening crises related to severe acute infection and inflammation.

Limited brain metabolism changes differentiate between the progression and clearance of rabies virus

Central nervous system (CNS) metabolic profiles were examined from rabies virus (RABV)-infected mice that were either mock-treated or received post-exposure treatment (PET) with a single dose of the live recombinant RABV vaccine TriGAS. CNS tissue harvested from mock-treated mice at middle and late stage infection revealed numerous changes in energy metabolites, neurotransmitters and stress hormones that correlated with replication levels of viral RNA. Although the large majority of these metabolic changes were completely absent in the brains of TriGAS-treated mice most likely due to the strong reduction in virus spread, TriGAS treatment resulted in the up-regulation of the expression of carnitine and several acylcarnitines, suggesting that these compounds are neuroprotective. The most striking change seen in mock-treated RABV-infected mice was a dramatic increase in brain and serum corticosterone levels, with the later becoming elevated before clinical signs or loss of body weight occurred. We speculate that the rise in corticosterone is part of a strategy of RABV to block the induction of immune responses that would otherwise interfere with its spread. In support of this concept, we show that pharmacological intervention to inhibit corticosterone biosynthesis, in the absence of vaccine treatment, significantly reduces the pathogenicity of RABV. Our results suggest that widespread metabolic changes, including hypothalamic-pituitary-adrenal axis activation, contribute to the pathogenesis of RABV and that preventing these alterations early in infection with PET or pharmacological blockade helps protect brain homeostasis, thereby reducing disease mortality.

The role of metabolomics in neonatal and pediatric laboratory medicine

Metabolomics consists of the quantitative analysis of a large number of low molecular mass metabolites involving substrates or products in metabolic pathways existing in all living systems. The analysis of the metabolic profile detectable in a human biological fluid allows to instantly identify changes in the composition of endogenous and exogenous metabolites caused by the interaction between specific physiopathological states, gene expression, and environment. In pediatrics and neonatology, metabolomics offers new encouraging perspectives for the improvement of critically ill patient outcome, for the early recognition of metabolic profiles associated with the development of diseases in the adult life, and for delivery of individualized medicine. In this view, nutrimetabolomics, based on the recognition of specific cluster of metabolites associated with nutrition and pharmacometabolomics, based on the capacity to personalize drug therapy by analyzing metabolic modifications due to therapeutic treatment may open new frontiers in the prevention and in the treatment of pediatric and neonatal diseases. This review summarizes the most relevant results published in the literature on the application of metabolomics in pediatric and neonatal clinical settings. However, there is the urgent need to standardize physiological and preanalytical variables, analytical methods, data processing, and result presentation, before establishing the definitive clinical value of results.

Metabolomics as a tool for personalizing medicine: 2012 update

Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.

GC/MS-based metabolomic analysis of cerebrospinal fluid (CSF) from glioma patients

Metabolomics has recently undergone rapid development; however, metabolomic analysis in cerebrospinal fluid (CSF) is not a common practice. We analyzed the metabolite profiles of preoperative CSF samples from 32 patients with histologically confirmed glioma using gas chromatography/mass spectrometry (GC/MS). We assessed how alterations in the metabolite levels were related to the World Health Organization (WHO) tumor grades, tumor location, gadolinium enhancement on magnetic resonance imaging (MRI), and the isocitrate dehydrogenase (IDH) mutation status. Sixty-one metabolites were identified in the CSF from glioma patients using targeted, quantitative and non-targeted, semi-quantitative analysis. The citric and isocitric acid levels were significantly higher in the glioblastoma (GBM) samples than in the grades I-II and grade III glioma samples. In addition, the lactic and 2-aminopimelic acid levels were relatively higher in the GBM samples than in the grades I-II glioma samples. The CSF levels of the citric, isocitric, and lactic acids were significantly higher in grade I-III gliomas with mutant IDH than in those with wild-type IDH. The tumor location and enhancement obtained using MRI did not significantly affect the metabolite profiles. Higher CSF levels of lactic acid were statistically associated with a poorer prognosis in grades III-IV malignant gliomas. Our study suggests that the metabolomic analysis of CSF from glioma patients may be useful for predicting the glioma grade, metabolic state, and prognosis of gliomas.

Metabolomic investigations of human infections

Metabolomics has a special place among other 'omics' disciplines (genomics, transcriptomics and proteomics) as it describes the most dynamic level of biological regulation and, as such, provides the most direct reflection of the physiological status of an organism. Quick development of the analytical technologies in the first place - MS and NMR - has enabled the metabolomics analysis of such complex biological phenomena as host-pathogen interactions in the development of infection. In this review, an overview of the metabolomics studies of infectious diseases carried out on human material is provided. The relevant papers on the metabolomics of human infectious diseases are comprehensively summarized in a table, including, for example, information on the study design, number of subjects, employed technology and metabolic discriminator. Future considerations, such as importance of the time-resolved study designs and the embedment of metabolomics in large-scale epidemiological studies are discussed.

Metabolomics in the developing human being

Metabolomics is based on the detailed analysis of metabolites and represents a unique chemical fingerprint of an organism. This approach allows assessing the dynamic behavior of biologic systems with multiple network interactions among individual components. The field of metabolic profiling has rapidly developed over the last decade, with successful applications in various research areas including toxicology, disease diagnosis and classification, pharmacology, and nutrition. This article provides a comprehensive account of existing data in the literature from animal and clinical studies on the use of metabolomics for improved understanding of medical conditions affecting the neonate and the developing human being.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

Metabolomics: moving towards personalized medicine

In many fields of medicine there is a growing interest in characterizing diseases at molecular level with a view to developing an individually tailored therapeutic approach. Metabolomics is a novel area that promises to contribute significantly to the characterization of various disease phenotypes and to the identification of personal metabolic features that can predict response to therapies. Based on analytical platforms such as mass spectrometry or NMR-based spectroscopy, the metabolomic approach enables a comprehensive overview of the metabolites, leading to the characterization of the metabolic fingerprint of a given sample. These metabolic fingerprints can then be used to distinguish between different disease phenotypes and to predict a drug's effectiveness and/or toxicity.

Several studies published in the last few years applied the metabolomic approach in the field of pediatric medicine. Being a highly informative technique that can be used on samples collected non-invasively (e.g. urine or exhaled breath condensate), metabolomics has appeal for the study of pediatric diseases. Here we present and discuss the pediatric clinical studies that have taken the metabolomic approach.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.

Metabolomic analysis of the cerebrospinal fluid reveals changes in phospholipase expression in the CNS of SIV-infected macaques

HIV infiltrates the CNS soon after an individual has become infected with the virus, and can cause dementia and encephalitis in late-stage disease. Here, a global metabolomics approach was used to find and identify metabolites differentially regulated in the cerebrospinal fluid (CSF) of rhesus macaques with SIV-induced CNS disease, as we hypothesized that this might provide biomarkers of virus-induced CNS damage. The screening platform used a non-targeted, mass-based metabolomics approach beginning with capillary reverse phase chromatography and electrospray ionization with accurate mass determination, followed by novel, nonlinear data alignment and online database screening to identify metabolites. CSF was compared before and after viral infection. Significant changes in the metabolome specific to SIV-induced encephalitis were observed. Metabolites that were increased during infection-induced encephalitis included carnitine, acyl-carnitines, fatty acids, and phospholipid molecules. The elevation in free fatty acids and lysophospholipids correlated with increased expression of specific phospholipases in the brains of animals with encephalitis. One of these, a phospholipase A2 isoenzyme, is capable of releasing a number of the fatty acids identified. It was expressed in different areas of the brain in conjunction with glial activation, rather than linked to regions of SIV infection and inflammation, indicating widespread alterations in infected brains. The identification of specific metabolites as well as mechanisms of their increase illustrates the potential of mass-based metabolomics to address problems in CNS biochemistry and neurovirology, as well as neurodegenerative diseases.

Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics

A multiple ionization mass spectrometry strategy is presented based on the analysis of human serum extracts. Chromatographic separation was interfaced inline with the atmospheric pressure ionization techniques electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) in both positive (+) and negative (-) ionization modes. Furthermore, surface-based matrix-assisted laser desorption/ionization (MALDI) and desorption ionization on silicon (DIOS) mass spectrometry were also integrated with the separation through fraction collection and offline mass spectrometry. Processing of raw data using the XCMS software resulted in time-aligned ion features, which are defined as a unique m/z at a unique retention time. The ion feature lists obtained through LC-MS with ESI and APCI interfaces in both +/- ionization modes were compared, and unique ion tables were generated. Nonredundant, unique ion features, were defined as mass numbers for which no mass numbers corresponding to [M + H](+), [M - H](-), or [M + Na](+) were observed in the other ionization methods at the same retention time. Analysis of the extracted serum using ESI for both (+) and (-) ions resulted in >90% additional unique ions being detected in the (-) ESI mode. Complementing the ESI analysis with APCI resulted in an additional approximately 20% increase in unique ions. Finally, ESI/APCI ionization was combined with fraction collection and offline-MALDI and DIOS mass spectrometry. The parts of the total ion current chromatograms in the LC-MS acquired data corresponding to collected fractions were summed, and m/z lists were compiled and compared to the m/z lists obtained from the DIOS/MALDI spectra. It was observed that, for each fraction, DIOS accounted for approximately 50% of the unique ions detected. These results suggest that true global metabolomics will require multiple ionization technologies to address the inherent metabolite diversity and therefore the complexity in and of metabolomics studies.

A Multivariate Screening Strategy for Investigating Metabolic Effects of Strenuous Physical Exercise in Human Serum

A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data. The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.

From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics

Mass spectrometry (MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its utility derives from its wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing mass spectrometry for metabolomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, we will discuss the techniques involved in this exciting area and the current and future applications of this field.