Plasma metabolomic biomarker panel to distinguish patients with amyotrophic lateral sclerosis from disease mimics.

Our objective was to identify plasma biomarkers of ALS that can aid in distinguishing patients with ALS from those with disease mimics. In this multi-center study, plasma samples were collected from 172 patients recently diagnosed with ALS, 50 healthy controls, and 73 neurological disease mimics. Samples were analyzed using metabolomics. Using all identified biochemicals detected in > 50% of all samples in the metabolomics analysis, samples were classified as ALS or mimic with 65% sensitivity and 81% specificity by LASSO analysis (AUC of 0.76). A subset panel of 32 candidate biomarkers classified these diagnosis groups with a specificity of 90%/sensitivity 58% (AUC of 0.81). Creatinine was lower in subjects with lower revised ALS Functional Rating Scale (ALSFRS-R) scores. In conclusion, ALS can be distinguished from neurological disease mimics by global biochemical profiling of plasma samples. Our analysis identified ALS versus mimics with relatively high sensitivity. We identified a subset of 32 metabolites that identify patients with ALS with a high specificity. Interestingly, lower creatinine correlates significantly with a lower ALSFRS-R score. Finally, molecules previously reported to be important in disease pathophysiology, such as urate, are included in our metabolite panel.

Serum metabolomics indicate altered cellular energy metabolism in children with cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) is a multi-system disease affecting multiple organs and cells besides the respiratory system. Metabolomic profiling allows simultaneous detection of biochemicals originating from cells, organs, or exogenous origin that may be valuable for monitoring of disease severity or in diagnosis. AIM: We hypothesized that metabolomics using serum from children would differentiate CF from non-CF lung disease subjects and would provide insight into metabolism in CF. METHODS: Serum collected from children with CF (n = 31) and 31 age and gender matched children with other lung diseases was used for metabolomic profiling by gas- and liquid-chromatography. Relative concentration of metabolites was compared between the groups using partial least square discriminant analyses (PLS-DA) and linear modeling. RESULTS: A clear separation of the two groups was seen in PLS-DA. Linear model found that among the 459 detected metabolites 92 differed between CF and non-CF. These included known biochemicals in lipid metabolism, oxidants, and markers consistent with abnormalities in bile acid processing. Bacterial metabolites were identified and differed between the groups indicating intestinal dysbiosis in CF. As a novel finding several pathways were markedly different in CF, which jointly point towards decreased activity in the ß-oxidation of fatty acids. These pathways include low ketone bodies, low medium chain carnitines, elevated di-carboxylic acids and decreased 2-hydroxybutyrate from amino acid metabolism in CF compared to non-CF. CONCLUSION: Serum metabolomics discriminated CF from non-CF and show altered cellular energy metabolism in CF potentially reflecting mitochondrial dysfunction. Future studies are indicated to examine their relation to the underlying CF defect and their use as biomarkers for disease severity or for cystic fibrosis transmembrane regulator (CFTR) function in an era of CFTR modifying drugs.

Metabolomic signatures in lipid-loaded HepaRGs reveal pathways involved in steatotic progression.

OBJECTIVES: A spectrum of disorders including simple steatosis, nonalcoholic steatohepatitis, fibrosis, and cirrhosis is described by nonalcoholic fatty liver disease (NAFLD). With the increased prevalence of obesity, and consequently NAFLD, there is a need for novel therapeutics in this area. To facilitate this effort, a cellular model of hepatic steatosis was developed using HepaRG cells and the resulting biochemical alterations were determined. DESIGN AND METHODS: Using global metabolomic profiling, by means of a novel metabolite extraction procedure, the metabolic profiles in response to the saturated fatty acid palmitate, and a mixture of saturated and unsaturated fatty acids, palmitate and oleate (1:2) were examined. RESULTS: We observed elevated levels of the branched chain amino acids, tricarboxylic acid cycle intermediates, sphingosine and acylcarnitines, and reduced levels of carnitine in the steatotic HepaRG model with both palmitate and palmitate:oleate treatments. In addition, elevated levels of diacylglycerols and monoacylglycerols as well as altered bile acid metabolism were selectively displayed by palmitate-induced steatotic cells. CONCLUSIONS: Biochemical changes in pathways important in the transition to hepatic steatosis including insulin resistance, altered mitochondrial metabolism, and oxidative stress are revealed by this global metabolomic approach. Moreover, the utility of this in vitro model for investigating the mechanisms of steatotic progression, insulin resistance, and lipotoxicity in NAFLD was demonstrated.

Application of metabolomics to diagnosis of insulin resistance.

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

Perturbation of bile acid homeostasis is an early pathogenesis event of drug induced liver injury in rats.

Drug-induced liver injury (DILI) is a significant consideration for drug development. Current preclinical DILI assessment relying on histopathology and clinical chemistry has limitations in sensitivity and discordance with human. To gain insights on DILI pathogenesis and identify potential biomarkers for improved DILI detection, we performed untargeted metabolomic analyses on rats treated with thirteen known hepatotoxins causing various types of DILI: necrosis (acetaminophen, bendazac, cyclosporine A, carbon tetrachloride, ethionine), cholestasis (methapyrilene and naphthylisothiocyanate), steatosis (tetracycline and ticlopidine), and idiosyncratic (carbamazepine, chlorzoxasone, flutamide, and nimesulide) at two doses and two time points. Statistical analysis and pathway mapping of the nearly 1900 metabolites profiled in the plasma, urine, and liver revealed diverse time and dose dependent metabolic cascades leading to DILI by the hepatotoxins. The most consistent change induced by the hepatotoxins, detectable even at the early time point/low dose, was the significant elevations of a panel of bile acids in the plasma and urine, suggesting that DILI impaired hepatic bile acid uptake from the circulation. Furthermore, bile acid amidation in the hepatocytes was altered depending on the severity of the hepatotoxin-induced oxidative stress. The alteration of the bile acids was most evident by the necrosis and cholestasis hepatotoxins, with more subtle effects by the steatosis and idiosyncratic hepatotoxins. Taking together, our data suggest that the perturbation of bile acid homeostasis is an early event of DILI. Upon further validation, selected bile acids in the circulation could be potentially used as sensitive and early DILI preclinical biomarkers.

Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies.

BACKGROUND: Metabolomics, the non-targeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. Current tissue extraction protocols involve sample destruction, precluding additional uses of the tissue. This is particularly problematic for high value samples with limited availability, such as clinical tumor biopsies that require structural preservation to histologically diagnose and gauge cancer aggressiveness. To overcome this limitation and increase the amount of information obtained from patient biopsies, we developed and characterized a workflow to perform metabolomic analysis and histological evaluation on the same biopsy sample. METHODS: Biopsies of ten human tissues (muscle, adrenal gland, colon, lung, pancreas, small intestine, spleen, stomach, prostate, kidney) were placed directly in a methanol solution to recover metabolites, precipitate proteins, and fix tissue. Following incubation, biopsies were removed from the solution and processed for histology. Kidney and prostate cancer tumor and benign biopsies were stained with hemotoxylin and eosin and prostate biopsies were subjected to PIN-4 immunohistochemistry. The methanolic extracts were analyzed for metabolites on GC/MS and LC/MS platforms. Raw mass spectrometry data files were automatically extracted using an informatics system that includes peak identification and metabolite identification software. RESULTS: Metabolites across all major biochemical classes (amino acids, peptides, carbohydrates, lipids, nucleotides, cofactors, xenobiotics) were measured. The number (ranging from 260 in prostate to 340 in colon) and identity of metabolites were comparable to results obtained with the current method requiring 30 mg ground tissue. Comparing relative levels of metabolites, cancer tumor from benign kidney and prostate biopsies could be distinguished. Successful histopathological analysis of biopsies by chemical staining (hematoxylin, eosin) and antibody binding (PIN-4, in prostate) showed cellular architecture and immunoreactivity were retained. CONCLUSIONS: Concurrent metabolite extraction and histological analysis of intact biopsies is amenable to the clinical workflow. Methanol fixation effectively preserves a wide range of tissues and is compatible with chemical staining and immunohistochemistry. The method offers an opportunity to augment histopathological diagnosis and tumor classification with quantitative measures of biochemicals in the same tissue sample. Since certain biochemicals have been shown to correlate with disease aggressiveness, this method should prove valuable as an adjunct to differentiate cancer aggressiveness.

Biochemical alterations associated with ALS.

Our objective was to identify metabolic pathways affected by ALS using non-targeted metabolomics in plasma, comparing samples from healthy volunteers to those from ALS patients. This discovery could become the basis for the identification of therapeutic targets and diagnostic biomarkers of ALS. Two distinct cross-sectional studies were conducted. Plasma was collected from 62 (Study 1) and 99 (Study 2) participants meeting El Escorial criteria for possible, probable, or definite ALS; 69 (Study 1) and 48 (Study 2) healthy controls samples were collected. Global metabolic profiling was used to detect and evaluate biochemical signatures of ALS. Twenty-three metabolites were significantly altered in plasma from ALS patients in both studies. These metabolites include biochemicals in pathways associated with neuronal change, hypermetabolism, oxidative damage, and mitochondrial dysfunction, all of which are proposed disease mechanisms in ALS. The data also suggest possible hepatic dysfunction associated with ALS. In conclusion, the data presented here provide insight into the pathophysiology of ALS while suggesting promising areas of focus for future studies. The metabolomics approach can generate novel hypotheses regarding ALS disease mechanisms with the potential to identify therapeutic targets and novel diagnostic biomarkers.

Organization of GC/MS and LC/MS metabolomics data into chemical libraries.

BACKGROUND: Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. RESULTS: LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. CONCLUSION: The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.

Metabolomic profiling reveals biochemical pathways and biomarkers associated with pathogenesis in cystic fibrosis cells.

Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. To gain an understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the cellular metabolism in the disease were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with the CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.

alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population.

BACKGROUND: Insulin resistance is a risk factor for type 2 diabetes and cardiovascular disease progression. Current diagnostic tests, such as glycemic indicators, have limitations in the early detection of insulin resistant individuals. We searched for novel biomarkers identifying these at-risk subjects. METHODS: Using mass spectrometry, non-targeted biochemical profiling was conducted in a cohort of 399 nondiabetic subjects representing a broad spectrum of insulin sensitivity and glucose tolerance (based on the hyperinsulinemic euglycemic clamp and oral glucose tolerance testing, respectively). RESULTS: Random forest statistical analysis selected alpha-hydroxybutyrate (alpha-HB) as the top-ranked biochemical for separating insulin resistant (lower third of the clamp-derived M(FFM) = 33 [12] micromol x min(-1) x kg(FFM) (-1), median [interquartile range], n = 140) from insulin sensitive subjects (M(FFM) = 66 [23] micromol x min(-1) x kg(FFM) (-1)) with a 76% accuracy. By targeted isotope dilution assay, plasma alpha-HB concentrations were reciprocally related to M(FFM); and by partition analysis, an alpha-HB value of 5 microg/ml was found to best separate insulin resistant from insulin sensitive subjects. alpha-HB also separated subjects with normal glucose tolerance from those with impaired fasting glycemia or impaired glucose tolerance independently of, and in an additive fashion to, insulin resistance. These associations were also independent of sex, age and BMI. Other metabolites from this global analysis that significantly correlated to insulin sensitivity included certain organic acid, amino acid, lysophospholipid, acylcarnitine and fatty acid species. Several metabolites are intermediates related to alpha-HB metabolism and biosynthesis. CONCLUSIONS: alpha-hydroxybutyrate is an early marker for both insulin resistance and impaired glucose regulation. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress.

Metabolomic profiling can predict which humans will develop liver dysfunction when deprived of dietary choline.

Choline is an essential nutrient, and deficiency causes liver and muscle dysfunction. Common genetic variations alter the risk of developing organ dysfunction when choline deficient, probably by causing metabolic inefficiencies that should be detectable even while ingesting a normal choline-adequate diet. We determined whether metabolomic profiling of plasma at baseline could predict whether humans will develop liver dysfunction when deprived of dietary choline. Fifty-three participants were fed a diet containing 550 mg choline/70 kg/d for 10 d and then fed < 50 mg choline/70 kg/d for up to 42 d. Participants who developed organ dysfunction on this diet were repleted with a choline-adequate diet for > or = 3 d. Plasma samples, obtained at baseline, end of depletion, and end of repletion, were used for targeted and nontargeted metabolomic profiling. Liver fat was assessed using magnetic resonance spectroscopy. Metabolomic profiling and targeted biochemical analyses were highly correlated for the analytes assessed by both procedures. In addition, we report relative concentration changes of other small molecules detected by the nontargeted metabolomic analysis after choline depletion. Finally, we show that metabolomic profiles of participants when they were consuming a control baseline diet could predict whether they would develop liver dysfunction when deprived of dietary choline.

Metabolomics for early detection of drug-induced kidney injury: review of the current status.

The identification of biomarkers of drug-induced kidney injury is an area of intensive focus in drug development. Traditional markers of renal function, including blood urea nitrogen and serum creatinine, are not region-specific and only increase significantly after substantial kidney injury. Therefore, more sensitive markers of kidney injury are needed. The ideal biomarkers will identify nephrotoxicity early in the drug-discovery process, resulting in decreased development costs and safer drugs. Metabolomics, the study of the small biochemicals present in a biological sample, has become a promising player in the nephrotoxicity arena. In this review, we describe the current status of the identification of metabolic biomarkers for drug-induced kidney toxicity screening. Many of these markers have been confirmed across multiple studies and can detect nephrotoxicity earlier than the traditional clinical chemistry and histopathology methods. Upon further validation, such markers will offer clear benefits for the pharmaceutical industry and regulatory agencies.

Analysis of the adult human plasma metabolome.

OBJECTIVE: It is well established that disease states are associated with biochemical changes (e.g., diabetes/glucose, cardiovascular disease/cholesterol), as are responses to chemical agents (e.g., medications, toxins, xenobiotics). Recently, nontargeted methods have been used to identify the small molecules (metabolites) in a biological sample to uncover many of the biochemical changes associated with a disease state or chemical response. Given that these experimental results may be influenced by the composition of the cohort, in the present study we assessed the effects of age, sex and race on the relative concentrations of small molecules (metabolites) in the blood of healthy adults. METHODS: Using gas- and liquid-chromatography in combination with mass spectrometry, a nontargeted metabolomic analysis was performed on plasma collected from an age- and sex-balanced cohort of 269 individuals. RESULTS: Of the more than 300 unique compounds that were detected, significant changes in the relative concentration of more than 100 metabolites were associated with age. Many fewer differences were associated with sex and fewer still with race. Changes in protein, energy and lipid metabolism, as well as oxidative stress, were observed with increasing age. Tricarboxylic acid intermediates, creatine, essential and nonessential amino acids, urea, ornithine, polyamines and oxidative stress markers (e.g., oxoproline, hippurate) increased with age. Compounds related to lipid metabolism, including fatty acids, carnitine, beta-hydroxybutyrate and cholesterol, were lower in the blood of younger individuals. By contrast, relative concentrations of dehydroepiandrosterone-sulfate (a proposed antiaging androgen) were lowest in the oldest age group. Certain xenobiotics (e.g., caffeine) were higher in older subjects, possibly reflecting decreases in hepatic cytochrome P450 activity. CONCLUSIONS: Our nontargeted analytical approach detected a large number of metabolites, including those that were found to be statistically altered with age, sex or race. Age-associated changes were more pronounced than those related to differences in sex or race in the population group we studied. Age, sex and race can be confounding factors when comparing different groups in clinical studies. Future studies to determine the influence of diet, lifestyle and medication are also warranted.