John A. Ryals

A Sister Group Contrast Using Untargeted Global Metabolomic Analysis Delineates the Biochemical Regulation Underlying Desiccation Tolerance in Sporobolus stapfianus

The desiccation-tolerant Sporobolus stapfianus and desiccation-sensitive Sporobolus pyramidalis form a sister group contrast to investigate adaptive metabolic responses to dehydration using untargeted global metabolomic analysis. The metabolic profiles obtained reveal a state of preparedness and a cascade of biochemical regulation strategies critical to the survival of S. stapfianus under desiccation. Understanding how plants tolerate dehydration is a prerequisite for developing novel strategies for improving drought tolerance. The desiccation-tolerant (DT) Sporobolus stapfianus and the desiccation-sensitive (DS) Sporobolus pyramidalis formed a sister group contrast to reveal adaptive metabolic responses to dehydration using untargeted global metabolomic analysis. Young leaves from both grasses at full hydration or at 60% relative water content (RWC) and from S. stapfianus at lower RWCs were analyzed using liquid and gas chromatography linked to mass spectrometry or tandem mass spectrometry. Comparison of the two species in the fully hydrated state revealed intrinsic differences between the two metabolomes. S. stapfianus had higher concentrations of osmolytes, lower concentrations of metabolites associated with energy metabolism, and higher concentrations of nitrogen metabolites, suggesting that it is primed metabolically for dehydration stress. Further reduction of the leaf RWC to 60% instigated a metabolic shift in S. stapfianus toward the production of protective compounds, whereas S. pyramidalis responded differently. The metabolomes of S. stapfianus leaves below 40% RWC were strongly directed toward antioxidant production, nitrogen remobilization, ammonia detoxification, and soluble sugar production. Collectively, the metabolic profiles obtained uncovered a cascade of biochemical regulation strategies critical to the survival of S. stapfianus under desiccation.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

Objective. Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the ‘metabolic network’ of cells (metabolome) is now possible by using a ‘metabolomics’ approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI). Study Design. Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second. Results. (1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide). Conclusion. Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health

Significance Metabolomics has been increasingly recognized as a powerful functional tool that integrates the impacts from genetics, environment, microbiota, and xenobiotics. We used a broad-spectrum metabolomics platform to analyze plasma samples from 80 adults of normal health. The comprehensive metabolic profiles provided a functional readout to assess the penetrance of gene mutations identified by whole-exome sequencing on these individuals. Conversely, metabolic abnormalities identified by statistical analysis uncovered potential damaging mutations that were previously unappreciated. Additionally, we found metabolic signatures consistent with early signs of disease conditions and drug effects associated with efficacy and toxicity. Our findings demonstrate that metabolomics could be an effective tool in precision medicine for disease risk assessment and customized drug therapy in clinics. Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

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.

Ethylene Glycol Monomethyl Ether–Induced Toxicity Is Mediated through the Inhibition of Flavoprotein Dehydrogenase Enzyme Family

Ethylene glycol monomethyl ether (EGME) is a widely used industrial solvent known to cause adverse effects to human and other mammals. Organs with high metabolism and rapid cell division, such as testes, are especially sensitive to its actions. In order to gain mechanistic understanding of EGME-induced toxicity, an untargeted metabolomic analysis was performed in rats. Male rats were administrated with EGME at 30 and 100 mg/kg/day. At days 1, 4, and 14, serum, urine, liver, and testes were collected for analysis. Testicular injury was observed at day 14 of the 100 mg/kg/day group only. Nearly 1900 metabolites across the four matrices were profiled using liquid chromatography-mass spectrometry/mass spectrometry and gas chromatography-mass spectrometry. Statistical analysis indicated that the most significant metabolic perturbations initiated from the early time points by EGME were the inhibition of choline oxidation, branched-chain amino acid catabolism, and fatty acid β-oxidation pathways, leading to the accumulation of sarcosine, dimethylglycine, and various carnitine- and glycine-conjugated metabolites. Pathway mapping of these altered metabolites revealed that all the disrupted steps were catalyzed by enzymes in the primary flavoprotein dehydrogenase family, suggesting that inhibition of flavoprotein dehydrogenase–catalyzed reactions may represent the mode of action for EGME-induced toxicity. Similar urinary and serum metabolite signatures are known to be the hallmarks of multiple acyl-coenzyme A dehydrogenase deficiency in humans, a genetic disorder because of defects in primary flavoprotein dehydrogenase reactions. We postulate that disruption of key biochemical pathways utilizing flavoprotein dehydrogenases in conjugation with downstream metabolic perturbations collectively result in the EGME-induced tissue damage.

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

Abstract 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

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.

Proteomics and metabolomics analyses reveal the cucurbit sieve tube system as a complex metabolic space.

The plant vascular system, and specifically the phloem, plays a pivotal role in allocation of fixed carbon to developing sink organs. Although the processes involved in loading and unloading of sugars and amino acids are well characterized, little information is available regarding the nature of other metabolites in the sieve tube system (STS) at specific sites along the pathway. Here, we elucidate spatial features of metabolite composition mapped with phloem enzymes along the cucurbit STS. Phloem sap (PS) was collected from the loading (source), unloading (apical sink region) and shoot-root junction regions of cucumber, watermelon and pumpkin. Our PS analyses revealed significant differences in the metabolic and proteomic profiles both along the source-sink pathway and between the STSs of these three cucurbits. In addition, metabolite profiles established for PS and vascular tissue indicated the presence of distinct compositions, consistent with the operation of the STS as a unique symplasmic domain. In this regard, at various locations along the STS we could map metabolites and their related enzymes to specific metabolic pathways. These findings are discussed with regard to the function of the STS as a unique and highly complex metabolic space within the plant vascular system.

Toxicometabolomics: Technology and Applications

The search for new and more predictive biomarkers in toxicology is an important pursuit across many discovery technologies (particularly ‘omics’ technologies) including, genotyping, transcriptomics, proteomics, and metabolomics. In fact, many future personalized health interventions or products rest squarely on the ability to discover and validate new biomarkers and on the healthcare industrys ability to commercialize these new clinical tests. The biomarkers themselves can be nearly anything that distinguishes one individual from another. Unfortunately, likely due to technical isssues associated with ‘omics’ technologies (such as lack of annotation of detected analytes, lack of sensitivity, and difficulties in data analysis and interpretation), these general screening methods for the discovery of new biomarkers have been very challenging with relatively few success stories. Many FDA approved companion diagnostics come from a deep understanding of the drug metabolism or function and were not discovered through profiling ‘omics’ technologies. In addition, many diseases involve multiple molecular pathway disturbances and validating any one change in a large set of biomarker data is hugely challenging. One recent promising technology with the potential to overcome a number of these issues is metabolomics, or in the case of applying metabolomics to toxicology – toxicometabolomics. The focus of this chapter will be to explore biochemical biomarkers in toxicology research and ways in which toxicometabolomics is an ideal technology for gaining these new insights.