Hepatitis C virus infection diagnosis using metabonomics

Harnessing Schistosoma-associated metabolite changes in the human host to identify biomarkers of infection and morbidity: Where are we and what should we do next?

Schistosomiasis is the second most widespread parasitic disease affecting humans. A key component of today's infection control measures is the diagnosis and monitoring of infection, informing individual- and community-level treatment. However, newly acquired infections and/or low parasite burden are still difficult to diagnose reliably. Furthermore, even though the pathological consequence of schistosome egg sequestration in host tissues is well described, the evidence linking egg burden to morbidity is increasingly challenged, making it inadequate for pathology monitoring. In the last decades, omics-based instruments and methods have been developed, adjusted, and applied in parasitic research. In particular, the profiling of the most reliable determinants of phenotypes, metabolites by metabolomics, emerged as a powerful boost in the understanding of basic interactions within the human host during infection. As such, the fine detection of host metabolites produced upon exposure to parasites such as Schistosoma spp. and the ensuing progression of the disease are believed to enable the identification of Schistosoma spp. potential biomarkers of infection and associated pathology. However, attempts to provide such a comprehensive understanding of the alterations of the human metabolome during schistosomiasis are rare, limited in their design when performed, and mostly inconclusive. In this review, we aimed to briefly summarize the most robust advances in knowledge on the changes in host metabolic profile during Schistosoma infections and provide recommendations for approaches to optimize the identification of metabolomic signatures of human schistosomiasis.

The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

Metabolomics in viral hepatitis: advances and review

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Urinary metabolomics of HCV patients with severe liver fibrosis before and during the sustained virologic response achieved by direct acting antiviral treatment

Hepatitis C virus (HCV) infection induces a long-term inflammatory response and oxidative-stress in the liver microenvironment, leading to hepatic fibrosis and metabolic alterations. Direct-acting-antiviral-agents (DAAs) induce HCV-clearance, even though liver damage is only partially restored. In this context, understanding the impact of viral-eradication on liver metabolic activities could allow optimizing the metabolic care of the patient. The present prospective longitudinal study aims at characterizing the urinary metabolic profile of HCV-induced severe liver fibrosis and the metabolic changes induced by DAAs and HCV-clearance by nuclear magnetic resonance-based metabolomics. The urinary metabolic profile of 23 HCV males with severe liver fibrosis and 20 age-matched healthy-controls was analyzed by NMR-based-metabolomics before starting DAAs, at the end-of-therapy, after one and three months of follow-up. The urinary metabolic profile of patients with severe liver fibrosis was associated to pseudouridine, hypoxanthine, methylguanidine and dimethylamine, highlighting a profile related to oxidative damage, and to tyrosine and glutamine, related to a decreased breakdown of aromatic aminoacids and ammonia detoxification, respectively. 1-methylnicotinamide, a catabolic intermediate of nicotinamide-adenine-dinucleotide, was significantly increased in HCV-patients and restored after HCV-clearance, probably due to the reduced hepatic inflammation. 3-hydroxy-3-methylbutyrate, an intermediate of leucine-catabolism which was permanently restored after HCV-clearance, suggested an improvement of skeletal muscle protein synthesis. Finally, 3-hydroxyisobutyrate and 2,3-dihydroxy-2-methylbutyrate, intermediates of valine-catabolism, glycine and choline increased temporarily during therapy, resulting as potential biomarkers of DAAs systemic effects.

Metabolomics in infectious diseases and drug discovery

Metabolomics has emerged as an invaluable tool that can be used along with genomics, transcriptomics and proteomics to understand host-pathogen interactions at small-molecule levels. Metabolomics has been used to study a variety of infectious diseases and applications. The most common application of metabolomics is for prognostic and diagnostic purposes, specifically the screening of disease-specific biomarkers by either NMR-based or mass spectrometry-based metabolomics. In addition, metabolomics is of great significance for the discovery of druggable metabolic enzymes and/or metabolic regulators through the use of state-of-the-art flux analysis, for example, via the elucidation of metabolic mechanisms. This review discusses the application of metabolomics technologies to biomarker screening, the discovery of drug targets in infectious diseases such as viral, bacterial and parasite infections and immunometabolomics, highlights the challenges associated with accessing metabolite compartmentalization and discusses the available tools for determining local metabolite concentrations.

Saliva NMR-Based Metabolomics in the War Against COVID-19

COVID-19 is an emergent, worldwide public health concern. Joint efforts have been made by scientific communities of various fields to better understand the mechanisms of action of SARS-CoV-2. The need to understand the pathophysiological fingerprint and pathways of this disease make metabolomics-related approaches an indispensable tool for properly answering concerns relating to disease course. Determination of the metabolomic profile may help to explain the heterogeneous spectra of COVID-19 clinical phenotypes and be useful in monitoring disease progression as well as therapeutic treatments. In this sense, saliva has proven to be a strategic biofluid, owing not only to its appeal as a noninvasive sampling method but also due to the capacity of the virus to invade epithelial cells of the oral mucosa and salivary gland ducts via ACE2 receptors. Accordingly, important changes in metabolism have been described relating to COVID-19, indicating that metabolomics may open new avenues for understanding the pathophysiology of this disease, especially via longitudinal study designs. Thus, we discuss the importance of comprehending the SARS-CoV-2 salivary metabolomic fingerprint and also highlight the situation of Brazil on the frontlines of the war against COVID-19.

1H NMR-based metabonomics for infertility diagnosis in men with varicocele

PURPOSE

"Omics" techniques have been used to understand and to identify biomarkers of male infertility. We report on the first metabonomics models created to diagnose varicocele and infertility among men with varicocele.

METHODS

We recruited 35 infertile men with varicocele (VI group), 21 fertile men with varicocele (VF group) and 24 fertile men without varicocele (C group). All men underwent standard semen analysis, scrotal duplex ultrasonography, and sexual hormone level measurement. Hydrogen-1 nuclear magnetic resonance (1H NMR) spectra of seminal plasma were used to create metabonomics models to discriminate between men with and without varicocele, and between fertile and infertile men with varicocele.

RESULTS

Using the statistical formalisms partial least square discriminants analysis and genetic algorithm-based linear discriminant analysis (GA-LDA), we created two models that discriminated the three groups from each other with accuracy of 92.17%. We also created metabonomics models using orthogonal partial least square discriminants analysis and GA-LDA that discriminated VF group from VI group, with an accuracy of 94.64% and 100% respectively. We identified 19 metabolites that were important in group segregation: caprate, 2-hydroxy-3-methylvalerate, leucine, valine, 3-hydroxybutyrate, lactate, alanine, 4-aminobutyrate, isoleucine, citrate, methanol, glucose, glycosides, glycerol-3-phosphocoline, n-acetyltyrosine, glutamine, tyrosine, arginine, and uridine.

CONCLUSIONS

1HNMR-based metabonomics of seminal plasma can be used to create metabonomics models to discriminate between men with varicocele from those without varicocele, and between fertile men with varicocele from those infertile with varicocele. Furthermore, the most important metabolites for group segregation are involved in the oxidative stress caused by varicocele.

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.

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics

This is a report on how ¹H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that ¹H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.

Proton nuclear magnetic resonance-based metabonomic models for non-invasive diagnosis of liver fibrosis in chronic hepatitis C: Optimizing the classification of intermediate fibrosis

AIM

To develop metabonomic models (MMs), using ¹H nuclear magnetic resonance (NMR) spectra of serum, to predict significant liver fibrosis (SF: Metavir ≥ F2), advanced liver fibrosis (AF: METAVIR ≥ F3) and cirrhosis (C: METAVIR = F4 or clinical cirrhosis) in chronic hepatitis C (CHC) patients. Additionally, to compare the accuracy of the MMs with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4).

METHODS

Sixty-nine patients who had undergone biopsy in the previous 12 mo or had clinical cirrhosis were included. The presence of any other liver disease was a criterion for exclusion. The MMs, constructed using partial least squares discriminant analysis and linear discriminant analysis formalisms, were tested by cross-validation, considering SF, AF and C.

RESULTS

Results showed that forty-two patients (61%) presented SF, 28 (40%) AF and 18 (26%) C. The MMs showed sensitivity and specificity of 97.6% and 92.6% to predict SF; 96.4% and 95.1% to predict AF; and 100% and 98.0% to predict C. Besides that, the MMs correctly classified all 27 (39.7%) and 25 (38.8%) patients with intermediate values of APRI and FIB-4, respectively.

CONCLUSION

The metabonomic strategy performed excellently in predicting significant and advanced liver fibrosis in CHC patients, including those in the gray zone of APRI and FIB-4, which may contribute to reducing the need for these patients to undergo liver biopsy.

Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV) or C (HCV), with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.

Spectroscopy with computational analysis in virological studies: A decade (2006-2016)

This review presents a retrospective of the studies carried out in the last 10 years (2006–2016) using spectroscopic methods as a research tool in the field of virology. Spectroscopic analyses are sensitive to variations in the biochemical composition of the sample, are non-destructive, fast and require the least sample preparation, making spectroscopic techniques tools of great interest in biological studies. Herein important chemometric algorithms that have been used in virological studies are also evidenced as a good alternative for analyzing the spectra, discrimination and classification of samples. Techniques that have not yet been used in the field of virology are also suggested. This methodology emerges as a new and promising field of research, and may be used in the near future as diagnosis tools for detecting diseases caused by viruses.

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A retrospective study of 2006–2016 using spectroscopic methods as a research tool in the field of virology.

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Chemometric algorithms used in virological studies were evidenced.

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This review emerges as a new and promising field of research in virology.

Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics

BACKGROUND

Diagnosis of liver involvement due to schistosomiasis in asymptomatic patients from endemic areas previously diagnosed with chronic hepatitis B (HBV) or C (HCV) and periportal fibrosis is challenging. H-1 Nuclear Magnetic Resonance (NMR)-based metabonomics strategy is a powerful tool for providing a profile of endogenous metabolites of low molecular weight in biofluids in a non-invasive way. The aim of this study was to diagnose periportal fibrosis due to schistosomiasis mansoni in patients with chronic HBV or HCV infection through NMR-based metabonomics models.

METHODOLOGY/PRINCIPAL FINDINGS

The study included 40 patients divided into two groups: (i) 18 coinfected patients with schistosomiasis mansoni and HBV or HCV; and (ii) 22 HBV or HCV monoinfected patients. The serum samples were analyzed through H-1 NMR spectroscopy and the models were based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). Ultrasonography examination was used to ascertain the diagnosis of periportal fibrosis. Exploratory analysis showed a clear separation between coinfected and monoinfected samples. The supervised model built from PLS-DA showed accuracy, R2 and Q2 values equal to 100%, 98.1% and 97.5%, respectively. According to the variable importance in the projection plot, lactate serum levels were higher in the coinfected group, while the signals attributed to HDL serum cholesterol were more intense in the monoinfected group.

CONCLUSIONS/SIGNIFICANCE

The metabonomics models constructed in this study are promising as an alternative tool for diagnosis of periportal fibrosis by schistosomiasis in patients with chronic HBV or HCV infection from endemic areas for Schistosoma mansoni.

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.

Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis

Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms. Because they intimately utilize and often rewire host metabolism, viruses are an excellent choice to study by metabolomics techniques. Studies of the effects of viruses on metabolism during replication in vitro and infection in animal models or human subjects have provided novel insights into these networks and provided new targets for therapy and biomarker development. Identifying the common metabolic pathways utilized by viruses has the potential to reveal those that can be targeted by broad-spectrum antiviral and vaccine approaches.

Nuclear magnetic resonance metabolomics and human liver diseases: The principles and evidence associated with protein and carbohydrate metabolism

During the last decade, metabolomics has become widely used in the field of human diseases. Numerous studies have demonstrated that this is a powerful technique for improving the understanding, diagnosis and management of various types of liver disease, such as acute and chronic liver diseases, and liver transplantation. Nuclear magnetic resonance (NMR) spectroscopy is one of the two most commonly applied methods for metabolomics. The aim of the present review was to investigate the results from recent key publications focusing on aspects of protein and carbohydrate metabolism. The review includes existing procedures, which are currently used for NMR data acquisition and statistical analysis. In addition, notable results obtained by these studies on protein and carbohydrate metabolism concerning human liver diseases are presented.

ATR-FTIR spectroscopy coupled with multivariate analysis techniques for the identification of DENV-3 in different concentrations in blood and serum: a new approach

In most cases of virus infections the viral load is directly related to the intensity of the disease.

Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions

Chikungunya and dengue are arboviral infections with overlapping clinical symptoms. A subset of chikungunya infection occurs also as co-infections with dengue, resulting in complications during diagnosis and patient management. The present study was undertaken to identify the global metabolome of patient sera infected with chikungunya as mono infections and with dengue as co-infections. Using nuclear magnetic resonance (NMR) spectroscopy, the metabolome of sera of three disease conditions, namely, chikungunya and dengue as mono-infections and when co-infected were ascertained and compared with healthy individuals. Further, the cohorts were analyzed on the basis of age, onset of fever and joint involvement. Here we show that many metabolites in the serum are significantly differentially regulated during chikungunya mono-infection as well as during chikungunya co-infection with dengue. We observed that glycine, serine, threonine, galactose and pyrimidine metabolisms are the most perturbed pathways in both mono and co-infection conditions. The affected pathways in our study correlate well with the clinical manifestation like fever, inflammation, energy deprivation and joint pain during the infections. These results may serve as a starting point for validations and identification of distinct biomolecules that could be exploited as biomarker candidates thereby helping in better patient management.

Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders

Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future.

Metabolic Characterization of Advanced Liver Fibrosis in HCV Patients as Studied by Serum 1H-NMR Spectroscopy

Several etiologies result in chronic liver diseases including chronic hepatitis C virus infection (HCV). Despite its high incidence and the severe economic and medical consequences, liver disease is still commonly overlooked due to the lack of efficient non-invasive diagnostic methods. While several techniques have been tested for the detection of fibrosis, the available biomarkers still present severe limitations that preclude their use in clinical diagnostics. Liver diseases have also been the subject of metabolomic analysis. Here, we demonstrate the suitability of ¹H NMR spectroscopy for characterizing the metabolism of liver fibrosis induced by HCV. Serum samples from HCV patients without fibrosis or with liver cirrhosis were analyzed by NMR spectroscopy and the results were submitted to multivariate and univariate statistical analysis. PLS-DA test was able to discriminate between advanced fibrotic and non-fibrotic patients and several metabolites were found to be up or downregulated in patients with cirrhosis. The suitability of the most significantly regulated metabolites was validated by ROC analysis. Our study reveals that choline, acetoacetate and low-density lipoproteins are the most informative biomarkers for predicting cirrhosis in HCV patients. Our results demonstrate that statistical analysis of ¹H-NMR spectra is able to distinguish between fibrotic and non-fibrotic patients suffering from HCV, representing a novel diagnostic application for NMR spectroscopy.

Nuclear magnetic resonance based metabolomics and liver diseases: Recent advances and future clinical applications

Metabolomics is defined as the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. It is an “omics” technique that is situated downstream of genomics, transcriptomics and proteomics. Metabolomics is recognized as a promising technique in the field of systems biology for the evaluation of global metabolic changes. During the last decade, metabolomics approaches have become widely used in the study of liver diseases for the detection of early biomarkers and altered metabolic pathways. It is a powerful technique to improve our pathophysiological knowledge of various liver diseases. It can be a useful tool to help clinicians in the diagnostic process especially to distinguish malignant and non-malignant liver disease as well as to determine the etiology or severity of the liver disease. It can also assess therapeutic response or predict drug induced liver injury. Nevertheless, the usefulness of metabolomics is often not understood by clinicians, especially the concept of metabolomics profiling or fingerprinting. In the present work, after a concise description of the different techniques and processes used in metabolomics, we will review the main research on this subject by focusing specifically on in vitro proton nuclear magnetic resonance spectroscopy based metabolomics approaches in human studies. We will first consider the clinical point of view enlighten physicians on this new approach and emphasis its future use in clinical “routine”.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

An magnetic resonance-based plasma metabonomic investigation on abnormal Savda in different complicated diseases

OBJECTIVE

To provide potential evidence for the existence of abnormal Savda, we assessed host metabonomic responses and dynamic changes occurring in various diseases using 1H nuclear magnetic resonance (NMR)-based metabonomics.

METHODS

Plasma samples taken from patients with complicated diseases with abnormal Savda (n = 140, including 35 cases each of diabetes, asthma, breast cancer, and cervical carcinoma) and from healthy controls (n = 35) were analyzed by 1H NMR (600 MHz), and the spectral profiles were analyzed by multivariate analysis using orthogonal projection to latent structure with discriminant analysis.

RESULTS

Supervised modeling of the data provided very good discrimination between patients and healthy controls. Compared with the healthy controls, the patient groups with different disease conditions displayed similar metabolic changes, characterized by lower creatine, creatinine, lactate, and amino acid levels (including isoleucine, leucine, valine, alanine, and 1-methylhistidine) and higher lipid levels (very low-density lipoproteins and unsaturated lipids). Additionally, cancer patients (breast and cervical) showed decreased myo-inositol, a-glucose, and beta-glucose, and increased pyruvate and carnitine in plasma.

CONCLUSION

The data indicate that decreased oxidative defense, liver function abnormalities, amino acid deficiencies, and energy metabolism disorders are common characteristics of complicated diseases, which may be related to the formation of abnormal Savda.

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

OBJECTIVES

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

DESIGN

Laboratory-based study.

SETTING

University research laboratory.

SUBJECTS

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

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Ex vivo assessment of carbon tetrachloride (CCl(4))-induced chronic injury using polarized light spectroscopy

The liver performs various functions, such as the production and detoxification of chemicals; therefore, it is susceptible to hepatotoxins such as carbon tetrachloride (CCl4), which causes chronic injury. Thus, assessment of injury and its status of severity are of prime importance. Current work reports an ex vivo study for probing the severance of hepatic injury induced by CCl4 with polarized light over the spectral range 400-800 nm. Different concentrations of CCl4 were used to induce varying severity of hepatic injury in a rat model. Linear retardance, depolarization rates, and diagonal Mueller matrix elements (m22, m33, and m44), were successfully used as the distinguishing criterion for normal and different liver injuries. Our results show that linear retardance for injured liver samples with lower doses of CCl4 tends to increase when compared with normal liver samples, while samples injured at higher doses of CCl4 offer almost no retardance. Total, linear, and circular depolarizations follow decreasing trends with increased liver injury severity over the entire investigated wavelength range. Linear polarization states were observed to be better maintained as compared to circular polarization states for all samples. Furthermore, numerical values of diagonal elements of the experimentally measured Mueller matrix also increase with increasing doses of CCl4. Liver fibroses, change in transport albedo, and the relative refractive index of the extracellular matrix caused by CCl4 are responsible for the observed differences. These results will provide a pathway to gauge the severity of injury caused by toxic chemicals.

The metabolomic window into hepatobiliary disease

The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation, and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develops. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver.

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.

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

RATIONALE

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

OBJECTIVES

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

METHODS

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

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Nuclear magnetic resonance based profiling of biofluids reveals metabolic dysregulation in HIV-infected persons and those on anti-retroviral therapy

BACKGROUND

Although HIV causes immune deficiency by infection and depletion of immunocytes, metabolic alterations with clinical manifestations are also reported in HIV/AIDS patients. Here we aimed to profile metabolite changes in the plasma, urine, and saliva of HIV/AIDS patients, including those on anti-retroviral therapy (ART).

METHODS

Metabolic profiling of biofluids collected from treatment naïve HIV/AIDS patients and those receiving ART was done with solution-state nuclear magnetic resonance (NMR) spectroscopy followed by statistical analysis and annotation.

RESULTS

In Principal Component Analysis (PCA) of the NMR spectra, Principal Component 1 (PC1) alone accounted for 99.3%, 87.2% and 78.8% variations in plasma, urine, and saliva, respectively. Partial least squares discriminant analysis (PLS-DA) was applied to generate three-component models, which showed plasma and urine to be better than saliva in discriminating between patients and healthy controls, and between ART-naïve patients and those receiving therapy. Twenty-six metabolites were differentially altered in any or two types of samples. Our results suggest that urinary Neopterin, and plasma Choline and Sarcosine could be used as metabolic biomarkers of HIV/AIDS infection. Pathway analysis revealed significant alternations in 12 metabolic pathways.

CONCLUSIONS

This study catalogs differentially regulated metabolites in biofluids, which helped classify subjects as healthy controls, HIV/AIDS patients, and those on ART. It also underscores the importance of further studying the consequences of HIV infection on host metabolism and its implications for pathogenesis.

A Metabolomics Profiling Study in Hand-Foot-and-Mouth Disease and Modulated Pathways of Clinical Intervention Using Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry

Hand-foot-and-mouth disease (HFMD), with poorly understood pathogenesis, has become a major public health threat across Asia Pacific. In order to characterize the metabolic changes of HFMD and to unravel the regulatory role of clinical intervention, we have performed a metabolomics approach in a clinical trial. In this study, metabolites profiling was performed by liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) platform from the HFMD clinical patient samples. The outcome of this study suggested that 31 endogenous metabolites were mainly involved and showed marked perturbation in HFMD patients. In addition, combination therapy intervention showed normalized tendency in HFMD patients in differential pathway. Taken together, these results indicate that metabolomics approach can be used as a complementary tool for the detection and the study of the etiology of HFMD.

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.

Assessment of methods for detection of Helicobacter pylori in animal models

Helicobacter pylori (H. pylori) is a major risk factor for chronic gastritis and peptic ulcer and is closely related to the occurrence of gastric cancer and gastric mucosa-associated lymphoid tissue (MALT) lymphoma. Rational use of animal models is very helpful for the clinical and preclinical investigation of H. pylori. However, the methods for detection of H. pylori in animal models were less reported. Most of the reported methods require the animal to be executed, and those not requiring killing the animal were less developed. This review will introduce seven methods for detection of H. pylori in animal models. These methods can meet the requirements for appropriate and accurate detection of H. pylori in different investigation conditions.

Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology

Metabolic profiling or 'metabonomics' is an investigatory method that allows metabolic changes associated with the presence of an underlying pathological process to be investigated. Various biofluids can be utilized in the process but urine, serum and fecal extract are most pertinent to the investigation of gastrointestinal and hepatological disease. Nuclear magnetic resonance spectroscopy-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet retain high sensitivity and specificity characteristics. The process involves a number of key steps, including sample collection, data acquisition, chemometric techniques and, finally, validation. This technique-driven review aims to demystify the metabonomics pathway, while also illustrating the potential of this technique with recent examples of its application in hepato-gastroenterological disease.

Metabonomic analysis of hepatitis E patients shows deregulated metabolic cycles and abnormalities in amino acid metabolism

Hepatitis E, which is endemic to resource-poor regions of the world, is largely an acute and self-limiting disease, but some patients have an increased susceptibility to develop fulminant hepatitis. The pathogenesis of hepatitis E in humans is poorly characterized. To understand the metabolic pathways involved in the pathophysiology of hepatitis E, we have used (1) H nuclear magnetic resonance spectroscopy to quantify various metabolites in the plasma and urine of the patients with hepatitis E. These were compared with specimens from patients with acute hepatitis B as disease controls and healthy volunteers. Data were analysed using chemometric statistical methods and metabolite databases. The main metabonomic changes found in patients with hepatitis E, but not in those with hepatitis B, included increased plasma levels of L-isoleucine, acetone, and glycerol, reduced plasma levels of glycine, and reduced urinary levels of imidazole, 3-aminoisobutanoic acid, 1-methylnicotinamide, biopterin, adenosine, 1-methylhistidine, and salicyluric acid. Patients with hepatitis E or B both showed increased levels of plasma and urinary L-proline and decreased levels of various other metabolites. Pathway analysis tools suggest the involvement of glycolysis, tricarboxylic acid cycle, urea cycle, and amino acid metabolism in patients with acute hepatitis E. These findings may help better understand the clinical and biochemical manifestations in this disease and the underlying pathophysiologic processes. Based on our findings, it would be worthwhile determining whether patients with hepatitis E are more prone to develop lactic acidosis and ketosis compared with other forms of viral hepatitis.

Pattern recognition analysis of proton nuclear magnetic resonance spectra of brain tissue extracts from rats anesthetized with propofol or isoflurane

BACKGROUND

General anesthesia is routinely used as a surgical procedure and its safety has been endorsed by clinical outcomes; however, its effects at the molecular level have not been elucidated. General anesthetics influence glucose metabolism in the brain. However, the effects of anesthetics on brain metabolites other than those related to glucose have not been well characterized. We used a pattern recognition analysis of proton nuclear magnetic resonance spectra to visualize the changes in holistic brain metabolic phenotypes in response to the widely used intravenous anesthetic propofol and the volatile anesthetic isoflurane.

METHODOLOGY/PRINCIPAL FINDINGS

Rats were randomized into five groups (n = 7 each group). Propofol and isoflurane were administered to two groups each, for 2 or 6 h. The control group received no anesthesia. Brains were removed directly after anesthesia. Hydrophilic compounds were extracted from excised whole brains and measured by proton nuclear magnetic resonance spectroscopy. All spectral data were processed and analyzed by principal component analysis for comparison of the metabolite profiles. Data were visualized by plotting principal component (PC) scores. In the plots, each point represents an individual sample. The propofol and isoflurane groups were clustered separately on the plots, and this separation was especially pronounced when comparing the 6-h groups. The PC scores of the propofol group were clearly distinct from those of the control group, particularly in the 6-h group, whereas the difference in PC scores was more subtle in the isoflurane group and control groups.

CONCLUSIONS/SIGNIFICANCE

The results of the present study showed that propofol and isoflurane exerted differential effects on holistic brain metabolism under anesthesia.