Metabolic phenotyping in clinical and surgical environments

Neuroendocrine Neoplasms: Identification of Novel Metabolic Circuits of Potential Diagnostic Utility

The incidence of neuroendocrine neoplasms (NEN) is increasing, but established biomarkers have poor diagnostic and prognostic accuracy. Here, we aim to define the systemic metabolic consequences of NEN and to establish the diagnostic utility of proton nuclear magnetic resonance spectroscopy (¹H-NMR) for NEN in a prospective cohort of patients through a single-centre, prospective controlled observational study. Urine samples of 34 treatment-naïve NEN patients (median age: 59.3 years, range: 36-85): 18 had pancreatic (Pan) NEN, of which seven were functioning; 16 had small bowel (SB) NEN; 20 age- and sex-matched healthy control individuals were analysed using a 600 MHz Bruker ¹H-NMR spectrometer. Orthogonal partial-least-squares-discriminant analysis models were able to discriminate both PanNEN and SBNEN patients from healthy control (Healthy vs. PanNEN: AUC = 0.90, Healthy vs. SBNEN: AUC = 0.90). Secondary metabolites of tryptophan, such as trigonelline and a niacin-related metabolite were also identified to be universally decreased in NEN patients, while upstream metabolites, such as kynurenine, were elevated in SBNEN. Hippurate, a gut-derived metabolite, was reduced in all patients, whereas other gut microbial co-metabolites, trimethylamine-N-oxide, 4-hydroxyphenylacetate and phenylacetylglutamine, were elevated in those with SBNEN. These findings suggest the existence of a new systems-based neuroendocrine circuit, regulated in part by cancer metabolism, neuroendocrine signalling molecules and gut microbial co-metabolism. Metabonomic profiling of NEN has diagnostic potential and could be used for discovering biomarkers for these tumours. These preliminary data require confirmation in a larger cohort.

Biomarkers of Neurodegeneration and Precision Therapy in Retinal Disease

Vision-threatening retinal diseases affect millions of people worldwide, representing an important public health issue (high social cost) for both technologically advanced and new-industrialized countries. Overall RD group comprises the retinitis pigmentosa, the age-related macular degeneration (AMD), the diabetic retinopathy (DR), and idiopathic epiretinal membrane formation. Endocrine, metabolic, and even lifestyles risk factors have been reported for these age-linked conditions that represent a "public priority" also in this COVID-19 emergency. Chronic inflammation and neurodegeneration characterize the disease evolution, with a consistent vitreoretinal interface impairment. As the vitreous chamber is significantly involved, the latest diagnostic technologies of imaging (retina) and biomarker detection (vitreous) have provided a huge input at both medical and surgical levels. Complement activation and immune cell recruitment/infiltration as well as detrimental intra/extracellular deposits occur in association with a reactive gliosis. The cell/tissue aging route shows a specific signal path and biomolecular profile characterized by the increased expression of several glial-derived mediators, including angiogenic/angiostatic, neurogenic, and stress-related factors (oxidative stress metabolites, inflammation, and even amyloid formation). The possibility to access vitreous chamber by collecting vitreous reflux during intravitreal injection or obtaining vitreous biopsy during a vitrectomy represents a step forward for an individualized therapy. As drug response and protein signature appear unique in each single patient, therapies should be individualized. This review addresses the current knowledge about biomarkers and pharmacological targets in these vitreoretinal diseases. As vitreous fluids might reflect the early stages of retinal sufferance and/or late stages of neurodegeneration, the possibility to modulate intravitreal levels of growth factors, in combination to anti-VEGF therapy, would open to a personalized therapy of retinal diseases.

Gut Microbial Ecosystem in Parkinson Disease: New Clinicobiological Insights from Multi-Omics

OBJECTIVE

Gut microbiome alterations in Parkinson disease (PD) have been reported repeatedly, but their functional relevance remains unclear. Fecal metabolomics, which provide a functional readout of microbial activity, have scarcely been investigated. We investigated fecal microbiome and metabolome alterations in PD, and their clinical relevance.

METHODS

Two hundred subjects (104 patients, 96 controls) underwent extensive clinical phenotyping. Stool samples were analyzed using 16S rRNA gene sequencing. Fecal metabolomics were performed using two platforms, nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry.

RESULTS

Fecal microbiome and metabolome composition in PD was significantly different from controls, with the largest effect size seen in NMR-based metabolome. Microbiome and NMR-based metabolome compositional differences remained significant after comprehensive confounder analyses. Differentially abundant fecal metabolite features and predicted functional changes in PD versus controls included bioactive molecules with putative neuroprotective effects (eg, short chain fatty acids [SCFAs], ubiquinones, and salicylate) and other compounds increasingly implicated in neurodegeneration (eg, ceramides, sphingosine, and trimethylamine N-oxide). In the PD group, cognitive impairment, low body mass index (BMI), frailty, constipation, and low physical activity were associated with fecal metabolome compositional differences. Notably, low SCFAs in PD were significantly associated with poorer cognition and low BMI. Lower butyrate levels correlated with worse postural instability-gait disorder scores.

INTERPRETATION

Gut microbial function is altered in PD, characterized by differentially abundant metabolic features that provide important biological insights into gut-brain pathophysiology. Their clinical relevance further supports a role for microbial metabolites as potential targets for the development of new biomarkers and therapies in PD. ANN NEUROL 2021;89:546-559.

Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment

INTRODUCTION

Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease.

OBJECTIVES

The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases.

METHODS

Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool.

RESULTS

Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99.

CONCLUSIONS

This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).

Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis

Background

Systemic inflammation in rheumatoid arthritis (RA) is associated with metabolic changes. We used nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to assess the relationship between an objective measure of systemic inflammation [C-reactive protein (CRP)] and both the serum and urinary metabolome in patients with newly presenting RA.

Methods

Serum (n=126) and urine (n=83) samples were collected at initial presentation from disease modifying anti-rheumatic drug naïve RA patients for metabolomic profile assessment using 1-dimensional ¹H-NMR spectroscopy. Metabolomics data were analysed using partial least square regression (PLS-R) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) with cross validation.

Results

Using PLS-R analysis, a relationship between the level of inflammation, as assessed by CRP, and the serum (p=0.001) and urinary (p<0.001) metabolome was detectable. Likewise, following categorisation of CRP into tertiles, patients in the lowest CRP tertile and the highest CRP tertile were statistically discriminated using OPLS-DA analysis of both serum (p=0.033) and urinary (p<0.001) metabolome. The most highly weighted metabolites for these models included glucose, amino acids, lactate, and citrate. These findings suggest increased glycolysis, perturbation in the citrate cycle, oxidative stress, protein catabolism and increased urea cycle activity are key characteristics of newly presenting RA patients with elevated CRP.

Conclusions

This study consolidates our understanding of a previously identified relationship between serum metabolite profile and inflammation and provides novel evidence that there is a relationship between urinary metabolite profile and inflammation as measured by CRP. Identification of these metabolic perturbations provides insights into the pathogenesis of RA and may help in the identification of therapeutic targets.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Metabolomic Analysis to Elucidate Mechanisms of Sunitinib Resistance in Renal Cell Carcinoma

Metabolomics analysis possibly identifies new therapeutic targets in treatment resistance by measuring changes in metabolites accompanying cancer progression. We previously conducted a global metabolomics (G-Met) study of renal cell carcinoma (RCC) and identified metabolites that may be involved in sunitinib resistance in RCC. Here, we aimed to elucidate possible mechanisms of sunitinib resistance in RCC through intracellular metabolites. We established sunitinib-resistant and control RCC cell lines from tumor tissues of RCC cell (786-O)-injected mice. We also quantified characteristic metabolites identified in our G-Met study to compare intracellular metabolism between the two cell lines using liquid chromatography-mass spectrometry. The established sunitinib-resistant RCC cell line demonstrated significantly desuppressed protein kinase B (Akt) and mesenchymal-to-epithelial transition (MET) phosphorylation compared with the control RCC cell line under sunitinib exposure. Among identified metabolites, glutamine, glutamic acid, and α-KG (involved in glutamine uptake into the tricarboxylic acid (TCA) cycle for energy metabolism); fructose 6-phosphate, D-sedoheptulose 7-phosphate, and glucose 1-phosphate (involved in increased glycolysis and its intermediate metabolites); and glutathione and myoinositol (antioxidant effects) were significantly increased in the sunitinib-resistant RCC cell line. Particularly, glutamine transporter (SLC1A5) expression was significantly increased in sunitinib-resistant RCC cells compared with control cells. In this study, we demonstrated energy metabolism with glutamine uptake and glycolysis upregulation, as well as antioxidant activity, was also associated with sunitinib resistance in RCC cells.

Therapeutic drug monitoring of tranexamic acid in plasma and urine of renally impaired patients using solid phase microextraction

The purpose of the research was to develop an improved solid phase microextraction (SPME)-based sampling protocol for the therapeutic drug monitoring of tranexamic acid (TXA) from plasma and urine of patients with chronic renal dysfunction (CRD) in order to correct the current dosing schedule to accommodate these patients. A 12-fold improvement in sampling efficiency (25 min for 96 samples -22 s per sample) was achieved with the use of hydrophilic-lipophilic balance (HLB)-coated SPME devices, thereby enabling high throughput profiling of TXA in the plasma and urine of 49 CRD patients undergoing cardiac surgery. A limit of quantification of 10 μg/mL and 25 μg/mL was obtained for plasma and urine respectively while a method accuracy of 103-105% and a precision of less than 8% was achieved. The results from this study were ultimately used by clinicians at the Toronto General Hospital to design a corrective pharmacokinetic dosing schedule for CRD patients. This green method further presents potential application in the clinical field for the fast high throughput monitoring of TXA not only in plasma but also in urine - a biological matrix seldom explored for the analysis of TXA - without the need for solvent-assisted extraction, extensive sample pre-treatment or clean-up, derivatization or excessive pH adjustment to improve amenability for analytical separation.

Associations between outdoor temperature and bright sunlight with metabolites in two population-based European cohorts

BACKGROUND AND AIMS

Outdoor temperature and bright sunlight may directly and/or indirectly modulate systemic metabolism. We assessed the associations between outdoor temperature and bright sunlight duration with metabolomics.

METHODS AND RESULTS

Cross-sectional analyses were undertaken in non-diabetic individuals from the Oxford BioBank (OBB; N = 6368; mean age 47.0 years, males 44%) and the Netherlands Epidemiology of Obesity (NEO; N = 5916; mean age 55.6 years, males 43%) study. Data on mean outdoor bright sunlight and temperature were collected from local weather stations in the week prior to blood sampling. Fasting serum levels of 148 metabolites, including 14 lipoprotein subclasses, were measured using NMR spectroscopy. Linear regression analyses were performed to assess the associations between mean outdoor temperature and bright sunlight duration with metabolomics adjusted for age, sex, body mass index, season and either outdoor temperature or bright sunlight. A higher mean outdoor temperature was associated with increased serum concentrations of lipoprotein (sub)particles (β (SE) = 0.064 (0.018) SD per 5 °C, p = 5.03e^-4) and certain amino acids such as phenylalanine (0.066 (0.016) SD, p = 6.44e^-05) and leucine (0.111 (0.018) SD, p = 1.25e^-09). In contrast, longer duration of bright sunlight was specifically associated with lower concentrations of very low-density lipoprotein (sub)particles (e.g., VLDL cholesterol (-0.024 (0.005) SD per 1-h bright sunlight, p = 8.06e^-6)). The direction of effects was generally consistent between the OBB and NEO, although effect sizes were generally larger in the OBB.

CONCLUSIONS

Increased bright sunlight duration is associated with an improved metabolic profile whilst higher outdoor temperature may adversely impact cardiometabolic health.

Characterization of Monophasic Solvent-Based Tissue Extractions for the Detection of Polar Metabolites and Lipids Applying Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry Clinical Metabolic Phenotyping Assays

Metabolic phenotyping of tissues uses metabolomics and lipidomics to measure the relative polar and nonpolar (lipid) metabolite levels in biological samples. This approach aims to understand disease biochemistry and identify biochemical markers of disease. Sample preparation methods must be reproducible, sensitive (high metabolite and lipid yield), and ideally rapid. We evaluated three biphasic methods for polar and nonpolar compound extraction (chloroform/methanol/water, dichloromethane/methanol/water, and methyl tert-butyl ether [MTBE]/methanol/water), a monophasic method for polar compound extraction (acetonitrile/methanol/water), and a monophasic method for nonpolar compound extraction (isopropanol/water). All methods were applied to mammalian heart, kidney, and liver tissues. Polar extracts were analyzed by hydrophilic interaction chromatography (HILIC) ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) and nonpolar extracts by C₁₈ reversed-phase UHPLC-MS. Method reproducibility and yield were assessed using multiple annotated endogenous compounds (putatively and MS/MS annotated). Monophasic methods had the highest yield and high reproducibility for both polar (positive ion: median relative standard deviation (RSD) < 18%; negative ion: median RSD < 28%) and nonpolar (positive and negative ion: median RSD < 15%) extractions for heart, kidneys, and liver. The polar monophasic method extracted higher levels of lipid than biphasic polar extractions, and these lipids caused minimal detection suppression for other compounds during HILIC UHPLC-MS. The nonpolar monophasic method had similar or greater detection responses of all detected lipid classes compared to biphasic methods (including increased phosphatidylinositol, phosphatidylserine, and cardiolipin responses). Monophasic methods are quicker and simpler than biphasic methods and are therefore most suited for future automation.

The Prognostic Value of Metabolic Profiling in Older Patients With a Proximal Femoral Fracture

Introduction:

High mortality rates of approximately 20% within 1 year after treatment are observed for patients with proximal femoral fractures. This preliminary study explores the prognostic value of a previously constructed mortality risk score based on a set of 14 metabolites for the survival and functional recovery in patients with proximal femoral fractures.

Materials and Methods:

A prospective observational cohort study was conducted including patients admitted with a proximal femoral fracture. The primary outcome was patient survival, and the recovery of independence in activities of daily living was included as a secondary outcome. The mortality risk score was constructed for each patient and its prognostic value was tested for the whole population.

Results:

Data was available form 136 patients. The mean age of all patients was 82.1 years, with a median follow-up of 6 months. Within this period, 19.0% of all patients died and 51.1% recovered to their prefracture level of independence. The mortality score was significantly associated with mortality (HR, 2.74; 95% CI, 1.61-4.66; P < 0.001), but showed only a fair prediction accuracy (AUC = 0.68) and a borderline significant comparison of the mortality score tertile groups in survival analyses (P = 0.049). No decisive associations were found in any of the analyses for the functional recovery of patients.

Discussion:

These findings support the previously determined prognostic value of the mortality risk score. However, the independent prognostic value when adjusted for potential confounding factors is yet to be assessed. Also, a risk score constructed for this specific patient population might achieve higher accuracies for the prediction of survival and functional recovery.

Conclusions:

A modest prediction accuracy was observed for the mortality risk score in this population. More elaborate studies are needed to validate these findings and develop a tailored model for clinical purposes in this patient population.

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.

Dietary supplementation with citrus extract alters the plasma parameters, circulating amino acid profiles and gene expression of small intestinal nutrient transporters in Chinese yellow-feathered broilers

BACKGROUND

This study evaluated the effects of citrus extract (CE) on growth performance, plasma amino acid (AA) profiles, intestinal development and small intestine AA and peptide transporter expression levels in broilers. A total of 540 one-day-old yellow-feathered broilers were fed a basal diet without any antibiotic (control group), or a basal diet containing 10 mg kg^-1 zinc bacitracin (antibiotic group), or a basal diet supplemented with 10 mg kg^-1 CE (CE group). After 63 days of feeding, two broilers per pen were slaughtered to collect tissues for further analysis.

RESULTS

Results showed that CE increased (P < 0.05) the final body weight and average daily gain from day 1 to 63, and decreased (P < 0.05) the feed/gain ratio from day 1 to 63. Dietary CE supplementation increased (P < 0.05) plasma total protein, albumin and glucose concentration, and decreased (P < 0.05) urea concentration. CE supplementation increased (P < 0.05) the villus height in the ileum and the villus height/crypt depth in the jejunum and ileum, but decreased (P < 0.05) the crypt depth in the jejunum and ileum. CE supplementation increased (P < 0.05) most plasma essential AA concentrations. Additionally, CE supplementation upregulated (P < 0.05) ASCT1, b^0,+ AT, B⁰ AT1, EAAT3, rBAT, y⁺ LAT2 and PepT1 expression in the jejunum, and b^0,+ AT, EAAT3, rBAT, y⁺ LAT2, CAT1 and PepT1 in the ileum.

CONCLUSIONS

Collectively, our results indicated that CE supplementation promotes intestinal physiological absorption of AAs by upregulating gene expression of small intestinal key AA and peptide transporters, thereby enhancing the growth performance of broilers. © 2020 Society of Chemical Industry.

1H NMR serum metabolomic profiling of patients at risk of cardiovascular diseases performing stress test

Cardiovascular diseases are the leading cause of death worldwide. Changes in lifestyle and/or pharmacological treatment are able to reduce the burden of coronary artery diseases (CAD) and early diagnosis is crucial for the timely and optimal management of the disease. Stress testing is a good method to measure the burden of CAD but it is time consuming and pharmacological testing may not fully mimic exercise test. The objectives of the present project were to characterize the metabolic profile of the population undergoing pharmacological and exercise stress testing to evaluate possible differences between them, and to assess the capacity of ¹H NMR spectroscopy to predict positive stress testing. Pattern recognition was applied to ¹H NMR spectra from serum of patients undergoing stress test and metabolites were quantified. The effects of the stress test, confounding variables and the ability to predict ischemia were evaluated using OPLS-DA. There was an increase in lactate and alanine concentrations in post-test samples in patients undergoing exercise test, but not in those submitted to pharmacological testing. However, when considering only pharmacological patients, those with a positive test result, showed increased serum lactate, that was masked by the much larger amount of lactate associated to exercise testing. In conclusion, we have established that pharmacological stress test does not reproduce the dynamic changes observed in exercise stress. Although there is promising evidence suggesting that ¹H NMR based metabolomics could predict stress test results, further studies with much larger populations will be required in order to obtain a definitive answer.

Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

The postprandial period represents one of the most challenging phenomena in whole-body metabolism, and it can be used as a unique window to evaluate the phenotypic flexibility of an individual in response to a given meal, which can be done by measuring the resilience of the metabolome. However, this exploration of the metabolism has never been applied to the arteriovenous (AV) exploration of organs metabolism. Here, we applied an AV metabolomics strategy to evaluate the postprandial flexibility across the liver and the intestine of mini-pigs subjected to a high fat–high sucrose (HFHS) diet for 2 months. We identified for the first time a postprandial signature associated to the insulin resistance and obesity outcomes, and we showed that the splanchnic postprandial metabolome was considerably affected by the meal and the obesity condition. Most of the changes induced by obesity were observed in the exchanges across the liver, where the metabolism was reorganized to maintain whole body glucose homeostasis by routing glucose formed de novo from a large variety of substrates into glycogen. Furthermore, metabolites related to lipid handling and energy metabolism showed a blunted postprandial response in the obese animals across organs. Finally, some of our results reflect a loss of flexibility in response to the HFHS meal challenge in unsuspected metabolic pathways that must be further explored as potential new events involved in early obesity and the onset of insulin resistance.

Urinary 1H-NMR Metabolic Signature in Subjects Undergoing Colonoscopy for Colon Cancer Diagnosis

Metabolomics represents a promising non-invasive approach that can be applied to identify biochemical changes in colorectal cancer patients (CRC) and is potentially useful for diagnosis and follow-up. Despite the literature regarding metabolomics CRC-specific profiles, discrimination between metabolic changes specifically related to CRC and intra-individual variability is still a problem to be solved. This was a preliminary case-control study, in which 1H-NMR spectroscopy combined with multivariate statistical analysis was used to profile urine metabolites in subjects undergoing colonoscopy for colon cancer diagnosis. To reduce intra-individual variability, metabolic profiles were evaluated in participants’ urine samples, collected just before the colonoscopy and after a short-term dietary regimen required for the endoscopy procedure. Data obtained highlighted different urinary metabolic profiles between CRC and unaffected subjects (C). The metabolites altered in the CRC urine (acetoacetate, creatine, creatinine, histamine, phenylacetylglycine, and tryptophan) significantly correlated with colon cancer and discriminated with accuracy CRC patients from C patients (receiver operator characteristic (ROC) curve with an area under the curve (AUC) of 0.875; 95% CI: 0.667–1). These results confirm that urinary metabolomic analysis can be a valid tool to improve CRC diagnosis, prognosis, and response to therapy, representing a noninvasive approach that could precede more invasive tests.

Precision Medicine for Lysosomal Disorders

Precision medicine (PM) is an emerging approach for disease treatment and prevention that accounts for the individual variability in the genes, environment, and lifestyle of each person. Lysosomal diseases (LDs) are a group of genetic metabolic disorders that include approximately 70 monogenic conditions caused by a defect in lysosomal function. LDs may result from primary lysosomal enzyme deficiencies or impairments in membrane-associated proteins, lysosomal enzyme activators, or modifiers that affect lysosomal function. LDs are heterogeneous disorders, and the phenotype of the affected individual depends on the type of substrate and where it accumulates, which may be impacted by the type of genetic change and residual enzymatic activity. LDs are individually rare, with a combined incidence of approximately 1:4000 individuals. Specific therapies are already available for several LDs, and many more are in development. Early identification may enable disease course prediction and a specific intervention, which is very important for clinical outcome. Driven by advances in omics technology, PM aims to provide the most appropriate management for each patient based on the disease susceptibility or treatment response predictions for specific subgroups. In this review, we focused on the emerging diagnostic technologies that may help to optimize the management of each LD patient and the therapeutic options available, as well as in clinical developments that enable customized approaches to be selected for each subject, according to the principles of PM.

Identifying potential serum biomarkers of breast cancer through targeted free fatty acid profiles screening based on a GC-MS platform

Recent advances suggest that abnormal fatty acid metabolism highly correlates with breast cancer, which provide clues to discover potential biomarkers of breast cancer. This study aims to identify serum free fatty acid (FFA) metabolic profiles and screen potential biomarkers for breast cancer diagnosis. Gas chromatography-mass spectrometry and our in-house fatty acid methyl ester standard substances library were combined to accurately identify FFA profiles in serum samples of breast cancer patients and breast adenosis patients (as controls). Potential biomarkers were screened by applying statistical analysis. A total of 18 FFAs were accurately identified in serum sample. Two groups of patients were correctly discriminated by the orthogonal partial least squares-discriminant analysis model based on FFA profiles. Seven FFA levels were significantly higher in serum from breast cancer patients than that in controls, and exhibited positive correlation with malignant degrees of disease. Furthermore, five candidates (palmitic acid, oleic acid, cis-8,11,14-eicosatrienoic acid, docosanoic acid and the ratio of oleic acid to stearic acid) were selected as potential serum biomarkers for differential diagnosis of breast cancer. Our study will help to reveal the metabolic signature of FFAs in breast cancer patients, and provides valuable information for facilitating clinical noninvasive diagnosis.

Effects of 9,10-phenanthrenequione on antioxidant indices and metabolite profiles in Takifugu obscurus plasma

Derived from polycyclic aromatic hydrocarbons (PAHs), oxygenated-PAHs (oxy-PAHs) may pose hazards to aquatic organisms, which remain largely unknown. Takifugu obscurus is an important anadromous fish species of high economic and ecological values. In the present study, T. obscurus was acutely exposed to 44.29 µg l-1 9,10-phenanthrenequione (9,10-PQ) for 96 h. Changes of antioxidant indices and metabolite profiles in plasma were compared between 9,10-PQ treatment and the control. The results showed that 9,10-PQ treatment significantly increased malondialdehyde (MDA) content during 6 to 96 h, increased superoxide dismutase (SOD) and catalase (CAT) activities at 6 h, but decreased them at 96 h. These results indicated that 9,10-PQ induced oxidative stress to fish. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis revealed that four metabolic pathways were influenced in response to treatment with 9,10-PQ, including glycerophospholipid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, purine metabolism and sulfur metabolism. These pathways are associated with antioxidant mechanisms, biosynthesis of neurotransmitters and innate immune functions. Thus, the as-obtained results confirmed that 9,10-PQ induced oxidative stress and raised concerns of neurotoxicity and immunotoxicity to fish. Overall, the present study posed a high environmental risk of oxy-PAHs to aquatic ecosystems.

Metabolic Fingerprinting Links Oncogenic PIK3CA with Enhanced Arachidonic Acid-Derived Eicosanoids

Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction. VIDEO ABSTRACT.

Nutriome-metabolome relationships provide insights into dietary intake and metabolism

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

Trunk Girdling Increased Stomatal Conductance in Cabernet Sauvignon Grapevines, Reduced Glutamine, and Increased Malvidin-3-Glucoside and Quercetin-3-Glucoside Concentrations in Skins and Pulp at Harvest

Girdling is a traditional horticultural practice applied at fruit set or other phenological stages, and is used mostly as a vine management. In grapevines, it is used primarily for table grapes to improve berry weight, sugar content, color, and to promote early harvest. The objective of this study was to evaluate the effect of trunk girdling applied at veraison, in 'Cabernet Sauvignon' wine grapes (Vitis vinifera L.), on agronomical and physiological parameters during vine development from the onset of ripening (veraison) to harvest, and additionally to quantify the effect of girdling on primary and secondary metabolism. Girdling was applied 146 days after pruning (dap) at veraison, when berry sampling for metabolomics and agronomical evaluations commenced, with a further three sampling dates until harvest, at 156 dap (30% maturation, 10 days after girdling-dag), 181 dap (70% maturation, 35 dag), and 223 dap (commercial harvest, 77 dag). Skin/pulp and seed tissues were extracted separately and metabolomics was performed using one-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy and high performance liquid chromatography (HPLC-DAD). At harvest, girdling significantly increased stomatal conductance (gs) in vines, decreased glutamine concentrations, and increased anthocyanin and flavonol concentrations in the skin/pulp tissues of grape berries. Berry weight was reduced by 27% from 181 dap to harvest, and was significantly higher in grapes from girdled vines at 181 dap. Sugars, organic acids, and other amino acids in skin/pulp or seeds were not significantly different, possibly due to extra-fascicular phloem vessels transporting metabolites from leaves to the roots. Using a metabolomics approach, differences between skin/pulp and seeds tissues were meaningful, and a greater number of secondary metabolites in skin/pulp was affected by girdling than in seeds. Girdling is a simple technique that could easily be applied commercially on vine management to improve berry color and other phenolics in 'Cabernet Sauvignon' grapes.

Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study

There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

Liquid Biopsies in Hepatocellular Carcinoma: Are We Winning?

Hepatocellular carcinoma (HCC) represents the sixth most common cancer worldwide and the third most common cause of cancer-related death. One of the major problems faced by researchers and clinicians in this area is the lack of reliable disease biomarkers, which would allow for an earlier diagnosis, follow-up or prediction of treatment response, among others. In this regard, the “HCC circulome”, defined as the pool of circulating molecules in the bloodstream derived from the primary tumor, represents an appealing target, the so called liquid biopsy. Such molecules encompass circulating tumor proteins, circulating tumor cells (CTCs), extracellular vesicles (EVs), tumor-educated platelets (TEPs), and circulating tumor nucleic acids, namely circulating tumor DNA (ctDNA) and circulating tumor RNA (ctRNA). In this article, we summarize recent findings highlighting the promising role of liquid biopsies as novel potential biomarkers in HCC, emphasizing on its clinical performance.

After another decade: LC-MS/MS became routine in clinical diagnostics

Tandem mass spectrometry - especially in combination with liquid chromatography (LC-MS/MS) - is applied in a multitude of important diagnostic niches of laboratory medicine. It is unquestioned in its routine use and is often unreplaceable by alternative technologies. This overview illustrates the development in the past decade (2009-2019) and intends to provide insight into the current standing and future directions of the field. The instrumentation matured significantly, the applications are well understood, and the in vitro diagnostics (IVD) industry is shaping the market by providing assay kits, certified instruments, and the first laboratory automated LC-MS/MS instruments as an analytical core. In many settings the application of LC-MS/MS is still burdensome with locally lab developed test (LDT) designs relying on highly specialized staff. The current routine applications cover a wide range of analytes in therapeutic drug monitoring, endocrinology including newborn screening, and toxicology. The tasks that remain to be mastered are, for example, the quantification of proteins by means of LC-MS/MS and the transition from targeted to untargeted omics approaches relying on pattern recognition/pattern discrimination as a key technology for the establishment of diagnostic decisions.

Malignancy prediction among tissues from Oral SCC patients including neck invasions: a 1H HRMAS NMR based metabolomic study

INTRODUCTION

Oral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis.

OBJECTIVES

To profile and generate precise ¹H HRMAS NMR spectral and quantitative statistical models of oral squamous cell carcinoma (OSCC) in tissue specimens including tumor, bed, margin and facial muscles. To apply the model in blinded prediction of malignancy among oral and neck tissues in an unknown set of patients suffering from OSCC along with neck invasion.

METHODS

Statistical models of ¹H HRMAS NMR spectral data on 180 tissues comprising tumor, margin and bed from 43 OSCC patients were performed. The combined metabolites, lipids spectral intensity and concentration-based malignancy prediction models were proposed. Further, 64 tissue specimens from twelve patients, including neck invasions, were tested for malignancy in a blinded manner.

RESULTS

Forty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCC patients with an accuracy of above 90%.

CONCLUSION

The efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.

Glucose Metabolic Characterization of Human Aqueous Humor in Relation to Wet Age-Related Macular Degeneration

Purpose

Energy compromise underpins wet age-related macular degeneration (wAMD) pathogenesis, but the relationship between glucose metabolism and the disease remains unclear. Here, we characterized aqueous humor (AH) to elucidate glucose-related metabolic signatures in patients with wAMD.

Methods

In total, 25 eyes of 25 patients with wAMD were divided into phakic (15 eyes), pseudophakic (10 eyes), and intravitreal injections of ranibizumab (13 eyes) wAMD groups. Twenty patients with cataract (21 eyes) served as controls. Ultrahigh-performance liquid chromatography tandem mass spectrometry was used to quantitatively characterize AH.

Results

Twenty-one metabolites related to glucose metabolism were identified in AH from 45 patients. Tricarboxylic acid (TCA)-related metabolic substrates, including citrate, were detected in AH and were significantly increased in AMD (P < 0.01) and AMD pseudophakic groups (P < 0.05). In contrast, α-ketoglutarate levels were decreased in the AMD group (P < 0.05). The α-ketoglutarate/citrate ratio was significantly decreased, corresponding to 71.71% and 93.6% decreases in the AMD (phakic and pseudophakic) groups as compared with controls (P < 0.001), revealing a significant positive correlation with glutamine. A lower mean glutamine and higher glutamate level were detected in AMD cases compared with controls. No significant differences were observed for lactic acid or other Krebs cycle metabolites. Intravitreal injection significantly alleviated mean central foveal thickness but did not significantly alter metabolites.

Conclusions

Compromised glucose TCA cycle and altered glutamine metabolism are implicated in the AH metabolism in wAMD. These findings highlight potential treatments for alleviating wAMD from a metabolic perspective.

Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Objectives

The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods

Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results

Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions

PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study

SCOPE

Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence.

METHODS AND RESULTS

1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat.

CONCLUSION

The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.

HJC0152 suppresses human non-small-cell lung cancer by inhibiting STAT3 and modulating metabolism

Objectives

Signal transducer and activator of transcription 3 (STAT3) is constitutively activated and overexpressed in many cancers, including non–small‐cell lung cancer (NSCLC). We recently developed HJC0152 as an orally active STAT3 inhibitor. This study focused on investigating HJC0152's effect and mechanism of action in NSCLC.

Materials and methods

We analysed cell proliferation by MTT assays, cell migration by wound healing and transwell assays, protein levels by Western blot, and apoptosis and reactive oxygen species (ROS) level by flow cytometry. A nude mouse tumorigenesis model was established for in vivo experiment. UHPLC‐QTOF/MS was used for untargeted metabolomic relative quantitation analysis.

Results

We found that HJC0152 exhibited activity against human NSCLC cells in vitro and NSCLC xenograft tumours in vivo via regulating STAT3 signalling and metabolism. HJC0152 efficiently reduced NSCLC cell proliferation, promoted ROS generation, induced apoptosis, triggered DNA damage and reduced motility in A549 and H460 NSCLC cells. Moreover, HJC0152 significantly inhibited the growth of A549 xenograft tumours in vivo. HJC0152 also affected metabolism, significantly decreasing and perturbating levels of several metabolites in the purine, glutathione and pyrimidine metabolism pathways.

Conclusions

HJC0152 reduces cellular capacity to scavenge free radicals, leading to ROS generation and accumulation and apoptosis. This study provides a rationale for further developing HJC0152 as a potential therapy for NSCLC and provides insights into the mechanisms by which HJC0152 exerts its anti‐cancer effects.

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.

Design and baseline data of a population-based metabonomics study of eye diseases in eastern China: the Yueqing Ocular Diseases Investigation

Background

China is undergoing a massive transition toward an urban and industrial economy. These changes will restructure the demographics and economy which will eventually influence the future patterns of disease. The risk factors of vision-impairing eye diseases remain ambiguous and poorly understood. Metabolomics is an ideal tool to understand and shed light on the ocular disease mechanisms for earlier treatment. This article aims to describe the design, methodology and baseline data of the Yueqing Ocular Diseases Investigation (YODI), a developed county population-based study to determine the prevalence and primary causes of visual impairment; also with metabonomics analysis we aimed to identify, predict and suggest some preventive biomarkers that cause blindness.

Methods

A population-based, cross-sectional study. Randomized clustering sampling was used to identify adults aged 50 years and older in Xiangyang Town, Yueqing county-level City. The interviews covered demographic, behavioral, ocular risk factors and mental health state. The ocular examination included visual acuity, autorefraction, intraocular pressure, anterior and posterior segment examinations, fundus photography, retinal tomography and angiography, and visual field testing. Anthropometric measurements included height and weight, waist and hip circumference, blood pressure, pulse rate, electrocardiogram, and abdominal ultrasound scan. A venous blood sample was collected for laboratory tests and metabonomics studies.

Results

Of the 5319 individuals recruited for the YODI, 4769 (89.7%) subjects were enrolled for analyses. The median age was 62.0 years, and 45.6% were male. The educational level of illiteracy or semi-illiteracy, primary, middle and high school or above was 29.8%, 45.5%, 20.1%, and 3.3%, respectively. Majority of the participants were female, younger, and less educated when compared with nonparticipants. The average body mass index and waist-hip ratios were 24.4 ± 3.4 kg/m² and 0.9 ± 0.1 respectively. Blood sample collection reached a sample size of 1909 (479 from subjects with self-reported diabetes and 1430 from one-third of the 4290 subjects without self-reported diabetes).

Conclusions

The YODI provides population-based data with a high response rate (89.7%) on the prevalence and primary causes of major vision-impairing eye diseases in developed county areas in eastern China. Metabonomics analysis from YODI will provide further association of metabolic characteristics with the visual impairment eye diseases. The risk prediction model could be created and has the potential to be generalized to developed eastern areas in China for prevention.

Systems and Precision Medicine in Necrotizing Soft Tissue Infections

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping

In this study we assess multiple sample preparation methods for UHPLC-MS metabolic phenotyping analysis of human urine and plasma. All methods are discussed in terms of metabolite and lipid coverage and reproducibility.

In vitro metabolomic footprint of the Echinococcus multilocularis metacestode

Alveolar echinococcosis (AE) is a zoonotic disease that is deadly if left untreated. AE is caused by the larval metacestode stage of the cestode Echinococcus multilocularis. Better knowledge on the host-parasite interface could yield novel targets for improvement of the treatment against AE. We analyzed culture media incubated with in vitro grown E. multilocularis metacestodes by 1H nuclear magnetic resonance spectroscopy to identify the unknown metabolic footprint of the parasite. Moreover, we quantitatively analyzed all amino acids, acetate, glucose, lactate, and succinate in time-course experiments using liquid chromatography and enzymatic assays. The E. multilocularis metacestodes consumed glucose and, surprisingly, threonine and produced succinate, acetate, and alanine as major fermentation products. The metabolic composition of vesicle fluid (VF) from in vitro grown E. multilocularis metacestodes was different from parasite-incubated culture medium with respect to the abundance, but not the spectrum, of metabolites, and some metabolites, in particular amino acids, accumulated in the VF. Overall, this study presents the first characterization of the in vitro metabolic footprint of E. multilocularis metacestodes and VF composition, and it provides the basis for analyses of potentially targetable pathways for future drug development.

The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets

SUMMARY

As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines.

AVAILABILITY AND IMPLEMENTATION

The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.

Systemic and central nervous system metabolic alterations in Alzheimer's disease

Background

Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration.

Methods

In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway.

Results

Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181.

Conclusions

This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology.

Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

1H-NMR Urinary Metabolic Profile, A Promising Tool for the Management of Infants with Human Cytomegalovirus-Infection

Congenital human cytomegalovirus (HCMV) infection is the most common mother-to-child transmitted infection in the developed world. Certain aspects of its management remain a challenge. Urinary metabolic profiling is a promising tool for use in pediatric conditions. The aim of this study was to investigate the urinary metabolic profile in HCMV-infected infants and controls during acute care hospitalization. Urine samples were collected from 53 patients at five hospitals participating in the Spanish congenital HCMV registry. Thirty-one cases of HCMV infection and 22 uninfected controls were included. Proton nuclear magnetic resonance (¹H-NMR) spectra were obtained using NOESYPR1D pulse sequence. The dataset underwent orthogonal projection on latent structures discriminant analysis to identify candidate variables affecting the urinary metabolome: HCMV infection, type of infection, sex, chronological age, gestational age, type of delivery, twins, and diet. Statistically significant discriminative models were obtained only for HCMV infection (p = 0.03) and chronological age (p < 0.01). No significant differences in the metabolomic profile were found between congenital and postnatal HCMV infection. When the HCMV-infected group was analyzed according to chronological age, a statistically significant model was obtained only in the neonatal group (p = 0.01), with the differentiating metabolites being betaine, glycine, alanine, and dimethylamine. Despite the considerable variation in urinary metabolic profiles in a real-life setting, clinical application of metabolomics to the study of HCMV infection seems feasible.

Metabolic Phenotyping in Venous Disease: The Need for Standardization

Venous thromboembolism (VTE), chronic venous disease (CVD), and venous leg ulceration (VLU) are clinical manifestations of a poorly functioning venous system. Though common, much is unknown of the pathophysiology and progression of these conditions. Metabolic phenotyping has been employed to explore mechanistic pathways involved in venous disease. A systematic literature review was performed: full text, primary research articles on the applications of nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) in human participants and animals were included for qualitative synthesis. Seventeen studies applying metabolic phenotyping to venous disease were identified: six on CVD, two on VLU, and nine on VTE; both animal (n = 6) and human (n = 10) experimental designs were reported, with one study including both. NMR, MS, and MS imaging were employed to characterize serum, plasma, urine, wound fluid, and tissue. Metabolites found to be upregulated in CVD included lipids, branched chain amino acids (BCAA), glutamate, taurine, lactate, and myo-inositol identified in vein tissue. Upregulated metabolites in VLU included lactate, BCAA, lysine, 3-hydroxybutyrate, and glutamate identified in wound fluid and ulcer biopsies. VTE cases were associated with reduced carnitine levels, upregulated aromatic amino acids, 3-hydroxybutyrate, BCAA, and lipids in plasma, serum, thrombus, and vein wall; kynurenine and tricarboxylic acid pathway dysfunction were reported. Future research should focus on targeted studies with internal and external validation.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Metabolomics Identifies Placental Dysfunction and Confirms Flt-1 (FMS-Like Tyrosine Kinase Receptor 1) Biomarker Specificity

Clinical end-stage parameters define the pregnancy disorders preeclampsia and fetal growth restriction while classification of the underlying placental dysfunction is missing and urgently needed. Flt-1 (FMS-like tyrosine kinase receptor 1) is the most promising placenta-derived predictive biomarker for preeclampsia. We aimed to classify placental dysfunction in preeclampsia and fetal growth restriction at delivery by metabolic profiling and authenticate the biomarker Flt-1 for placental dysfunction. We studied 143 pregnancies with or without preeclampsia and/or fetal growth restriction delivered by cesarean section. Metabolic placenta profiles were created by high-resolution magic angle spinning nuclear magnetic resonance spectroscopy and the resulting placental phenotypes obtained by hierarchical clustering. Placental Flt-1 expression (membrane-bound and soluble isoforms combined) and maternal serum Flt-1 expression (soluble isoforms) were analyzed by immunohistochemistry and ELISA, respectively. We identified 3 distinct placenta groups by 21 metabolites and diagnostic outcome parameters; normal placentas, moderate placental dysfunction, and severe placental dysfunction. Increased placental Flt-1 was associated with severe placental dysfunction, and increased serum Flt-1 was associated with moderate and severe placental dysfunction. The preeclamptic pregnancies with and without placental dysfunction could be distinguished by 5 metabolites and placental Flt-1. Placental Flt-1 alone could separate normal pregnancies with and without placental dysfunction. In conclusion, metabolomics could classify placental dysfunction and provide information not identified by traditional diagnostics and metabolites with biomarker potential were identified. Flt-1 was confirmed as precision biomarker for placental dysfunction, substantiating its usefulness for identification of high-risk pregnancies for preeclampsia and fetal growth restriction with placental involvement.

Mass Spectrometry as a Clinical Integrative Tool to Evaluate Hepatocellular Carcinoma: Moving to the Mainstream

Introduction: Since the pioneering work of J. J. Thomson on magnetic deflection of charged particles, mass spectrometry (MS) has become the most progressive clinical tool by continuously providing new applications in medical research. In hepatocellular carcinoma (HCC), MS can be used from surveillance in early stages of the disease to constant evaluation of effective treatments. Areas covered: This Special Report highlights the groundbreaking possibilities of mass spectrometry clinical application in the mainstream to evaluate HCC development and progression. Expert opinon: MS has been employed to understand a myriad of liver diseases, such as the identification of early biomarkers in cirrhosis and HVB and HVC, as well as metabolic alterations of lipidic imbalance in HCC due to fatty liver disease. In an integrative point-of-view, researchers worldwide are looking for molecular signatures that may represent more faithfully the complex scenario of the onset and progression of HCC. Following the steps of MELD score (Model of End-stage Liver Disease), which evaluates biochemical dysfunction of end-stage liver diseases, the necessity to use innovative attempts to pursue a molecular-MEaLD (mMEaLD - molecular Model for Early Liver Disease), shifting MS to the upstream and from the lab facilities into the mainstream, inside the surgery room.

Arterio-venous metabolomics exploration reveals major changes across liver and intestine in the obese Yucatan minipig

Blood circulation mainly aims at distributing the nutrients required for tissue metabolism and collecting safely the by-products of all tissues to be further metabolized or eliminated. The simultaneous study of arterial (A) and venous (V) specific metabolites therefore has appeared to be a more relevant approach to understand and study the metabolism of a given organ. We propose to implement this approach by applying a metabolomics (NMR) strategy on paired AV blood across the intestine and liver on high fat/high sugar (HFHS)-fed minipigs. Our objective was to unravel kinetically and sequentially the metabolic adaptations to early obesity/insulin resistance onset specifically on these two tissues. After two months of HFHS feeding our study of AV ratios of the metabolome highlighted three major features. First, the hepatic metabolism switched from carbohydrate to lipid utilization. Second, the energy demand of the intestine increased, resulting in an enhanced uptake of glutamine, glutamate, and the recruitment of novel energy substrates (choline and creatine). Third, the uptake of methionine and threonine was considered to be driven by an increased intestine turnover to cope with the new high-density diet. Finally, the unique combination of experimental data and modelling predictions suggested that HFHS feeding was associated with changes in tryptophan metabolism and fatty acid β-oxidation, which may play an important role in lipid hepatic accumulation and insulin sensitivity.

Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma

More than two-thirds of patients with hepatocellular carcinoma (HCC) cannot receive curative therapy and have poor survival due to late diagnosis and few prognostic directions. In our study, nontargeted and targeted metabolomics analyses were conducted by liquid chromatography-mass spectrometry to characterize metabolic features of HCC and identify diagnostic and prognostic biomarker candidate incorporating liver tissue and serum metabolites. A total of 552 subjects, including 432 with liver tissue and 120 with serum specimens, were recruited in China. In the discovery cohort, a series of 138 metabolites were identified to discriminate HCC tissues from matched nontumor tissues. Retinol presented with the highest area under the curve (AUC) of 0.991 and associated with Edmondson grade. In the validation cohort, all metabolites in retinol metabolism pathway were examined and the levels of retinol and retinal in tumor tissue and serum decreased in the order of normal to cirrhosis to HCC of Edmondson Grades I to IV. Retinol and retinal levels could also differentiate between HCC and cirrhosis, with AUCs of 0.996 and 0.994, respectively, in tissue and 0.812 and 0.744, respectively, in serum. The AUC of the combined retinol and retinal panel in serum was 0.852. Univariate and multivariate Cox regression identified this panel as an independent predictor for HCC and showed that low expression of retinol and retinal correlated with decreased survival time. In conclusion, the retinol metabolic signature had considerable diagnostic and prognostic value for identifying HCC patients who would benefit from prompt therapy and optimal prognostic direction.

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

Human Plasma Metabolomics in Age-Related Macular Degeneration: Meta-Analysis of Two Cohorts

The pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness worldwide, remains only partially understood. This has led to the current lack of accessible and reliable biofluid biomarkers for diagnosis and prognosis, and absence of treatments for dry AMD. This study aimed to assess the plasma metabolomic profiles of AMD and its severity stages with the ultimate goal of contributing to addressing these needs. We recruited two cohorts: Boston, United States (n = 196) and Coimbra, Portugal (n = 295). Fasting blood samples were analyzed using ultra-high performance liquid chromatography mass spectrometry. For each cohort, we compared plasma metabolites of AMD patients versus controls (logistic regression), and across disease stages (permutation-based cumulative logistic regression considering both eyes). Meta-analyses were then used to combine results from the two cohorts. Our results revealed that 28 metabolites differed significantly between AMD patients versus controls (false discovery rate (FDR) q-value: 4.1 × 10-2-1.8 × 10-5), and 67 across disease stages (FDR q-value: 4.5 × 10-2-1.7 × 10-4). Pathway analysis showed significant enrichment of glycerophospholipid, purine, taurine and hypotaurine, and nitrogen metabolism (p-value ≤ 0.04). In conclusion, our findings support that AMD patients present distinct plasma metabolomic profiles, which vary with disease severity. This work contributes to the understanding of AMD pathophysiology, and can be the basis of future biomarkers and precision medicine for this blinding condition.