An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Assessing the Readability of App Descriptions and Investigating its Role in the Choice of mHealth Apps: Retrospective and Prospective Analyses.

People with low health literacy are more likely to use mobile apps for health information. The choice of mHealth apps can affect health behaviors and outcomes. However, app descriptions may not be very readable to the target users, which can negatively impact app adoption and utilization. In this study, we assessed the readability of mHealth app descriptions and explored the relationship between description readability and other app metadata, as well as description writing styles. The results showed that app descriptions were at eleventh- to fifteenth-grade level, with only 6% of them meeting the readability recommendation (third- to seventh-grade level). The description readability played a vital role in predicting app installs when an app had no reviews. The content analysis showed copy-paste behaviors and identified two potential causes for low readability. More work is needed to improve the readability of app descriptions and optimize mHealth app adoption and utilization.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite.

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material®s (SRM®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Discovery of Metabolic Biomarkers Predicting Radiation Therapy Late Effects in Prostate Cancer Patients.

Patients presenting with prostate cancers undergo clinical staging evaluations to determine the extent of disease to guide therapeutic recommendations. Management options may include watchful waiting, surgery, or radiation therapy. Thus, initial risk stratification of prostate cancer patients is important for achieving optimal therapeutic results or cancer cure and preservation of quality of life. Predictive biomarkers for risks of complications or late effects of treatment are needed to inform clinical decisions for treatment selection. Here, we analyzed pre-treatment plasma metabolites in a cohort of prostate cancer patients (N = 99) treated with Stereotactic Body Radiation Therapy (SBRT) at Medstar-Georgetown University Hospital in a longitudinal, quality-of-life study to determine if individuals experiencing radiation toxicities can be identified by a molecular profile in plasma prior to treatment. We used a multiple reaction mass spectrometry-based molecular phenotyping of clinically annotated plasma samples in a retrospective outcome analysis to identify candidate biomarker panels correlating with adverse clinical outcomes following radiation therapy. We describe the discovery of candidate biomarkers, based on small molecule metabolite panels, showing high correlations (AUCs ≥ 95%) with radiation toxicities, suitable for validation studies in an expanded cohort of patients.

Metabolomic Studies of Tissue Injury in Nonhuman Primates Exposed to Gamma-Radiation.

Exposure to ionizing radiation induces a complex cascade of systemic and tissue-specific responses that lead to functional impairment over time in the surviving population. However, due to the lack of predictive biomarkers of tissue injury, current methods for the management of survivors of radiation exposure episodes involve monitoring of individuals over time for the development of adverse clinical symptoms and death. Herein, we report on changes in metabolomic and lipidomic profiles in multiple tissues of nonhuman primates (NHPs) that were exposed to a single dose of 7.2 Gy whole-body 60Co γ-radiation that either survived or succumbed to radiation toxicities over a 60-day period. This study involved the delineation of the radiation effects in the liver, kidney, jejunum, heart, lung, and spleen. We found robust metabolic changes in the kidney and liver and modest changes in other tissue types at the 60-day time point in a cohort of NHPs. Remarkably, we found significant elevation of long-chain acylcarnitines in animals that were exposed to radiation across multiple tissue types underscoring the role of this class of metabolites as a generic indicator of radiation-induced normal tissue injury. These studies underscore the utility of a metabolomics approach for delineating anticipatory biomarkers of exposure to ionizing radiation.

Plasma-derived extracellular vesicles yield predictive markers of cranial irradiation exposure in mice.

Ionizing radiation exposure to the brain is common for patients with a variety of CNS related malignancies. This exposure is known to induce structural and functional alterations to the brain, impacting dendritic complexity, spine density and inflammation. Over time, these changes are associated with cognitive decline. However, many of these impacts are only observable long after irradiation. Extracellular vesicles (EVs) are shed from cells in nearly all known tissues, with roles in many disease pathologies. EVs are becoming an important target for identifying circulating biomarkers. The aim of this study is to identify minimally invasive biomarkers of ionizing radiation damage to the CNS that are predictors of late responses that manifest as persistent cognitive impairments. Using a clinically relevant 9 Gy irradiation paradigm, we exposed mice to cranial (head only) irradiation. Using metabolomic and lipidomic profiling, we analyzed their plasma and plasma-derived EVs two days and two weeks post-exposure to detect systemic signs of damage. We identified significant changes associated with inflammation in EVs. Whole-plasma profiling provided further evidence of systemic injury. These studies are the first to demonstrate that profiling of plasma-derived EVs may be used to study clinically relevant markers of ionizing radiation toxicities to the brain.

Pharmacokinetic and Metabolomic Studies with BIO 300, a Nanosuspension of Genistein, in a Nonhuman Primate Model.

Genistein is a naturally occurring phytoestrogen isoflavone and is the active drug ingredient in BIO 300, a radiation countermeasure under advanced development for acute radiation syndrome (H-ARS) and for the delayed effects of acute radiation exposure (DEARE). Here we have assessed the pharmacokinetics (PK) and safety of BIO 300 in the nonhuman primate (NHP). In addition, we analyzed serum samples from animals receiving a single dose of BIO 300 for global metabolomic changes using ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight mass spectrometry (QTOF-MS). We present a comparison of how either intramuscularly (im) or orally (po) administered BIO 300 changed the metabolomic profile. We observed transient alterations in phenylalanine, tyrosine, glycerophosphocholine, and glycerophosphoserine which reverted back to near-normal levels 7 days after drug administration. We found a significant overlap in the metabolite profile changes induced by each route of administration; with the po route showing fewer metabolic alterations. Taken together, our results suggest that the administration of BIO 300 results in metabolic shifts that could provide an overall advantage to combat radiation injury. This initial assessment also highlights the utility of metabolomics and lipidomics to determine the underlying physiological mechanisms involved in the radioprotective efficacy of BIO 300.

Liquid Chromatography-Mass Spectrometry-Based Metabolomics of Nonhuman Primates after 4 Gy Total Body Radiation Exposure: Global Effects and Targeted Panels.

Rapid assessment of radiation signatures in noninvasive biofluids may aid in assigning proper medical treatments for acute radiation syndrome (ARS) and delegating limited resources after a nuclear disaster. Metabolomic platforms allow for rapid screening of biofluid signatures and show promise in differentiating radiation quality and time postexposure. Here, we use global metabolomics to differentiate temporal effects (1-60 d) found in nonhuman primate (NHP) urine and serum small molecule signatures after a 4 Gy total body irradiation. Random Forests analysis differentially classifies biofluid signatures according to days post 4 Gy exposure. Eight compounds involved in protein metabolism, fatty acid ß oxidation, DNA base deamination, and general energy metabolism were identified in each urine and serum sample and validated through tandem MS. The greatest perturbations were seen at 1 d in urine and 1-21 d in serum. Furthermore, we developed a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) with multiple reaction monitoring (MRM) method to quantify a six compound panel (hypoxanthine, carnitine, acetylcarnitine, proline, taurine, and citrulline) identified in a previous training cohort at 7 d after a 4 Gy exposure. The highest sensitivity and specificity for classifying exposure at 7 d after a 4 Gy exposure included carnitine and acetylcarnitine in urine and taurine, carnitine, and hypoxanthine in serum. Receiver operator characteristic (ROC) curve analysis using combined compounds show excellent sensitivity and specificity in urine (area under the curve [AUC] = 0.99) and serum (AUC = 0.95). These results highlight the utility of MS platforms to differentiate time postexposure and acquire reliable quantitative biomarker panels for classifying exposed individuals.

Plasma Derived Exosomal Biomarkers of Exposure to Ionizing Radiation in Nonhuman Primates.

Exposure to ionizing radiation induces a cascade of molecular events that ultimately impact endogenous metabolism. Qualitative and quantitative characterization of metabolomic profiles is a pragmatic approach to studying the risks of radiation exposure since it provides a phenotypic readout. Studies were conducted in irradiated nonhuman primates (NHP) to investigate metabolic changes in plasma and plasma-derived exosomes. Specifically, rhesus macaques (Macaca mulatta) were exposed to cobalt-60 gamma-radiation and plasma samples were collected prior to and after exposure to 5.8 Gy or 6.5 Gy radiation. Exosomes were isolated using ultracentrifugation and analyzed by untargeted profiling via ultra-performance liquid chromatography mass spectrometry (UPLC-MS) based metabolomic and lipidomic analyses, with the goal of identifying a molecular signature of irradiation. The enrichment of an exosomal fraction was confirmed using quantitative ELISA. Plasma profiling showed markers of dyslipidemia, inflammation and oxidative stress post-irradiation. Exosomal profiling, on the other hand, enabled detection and identification of low abundance metabolites that comprise exosomal cargo which would otherwise get obscured with plasma profiling. We discovered enrichment of different classes of metabolites including N-acyl-amino acids, Fatty Acid ester of Hydroxyl Fatty Acids (FAHFA's), glycolipids and triglycerides as compared to the plasma metabolome composition with implications in mediation of systemic response to radiation induced stress signaling.

Exposure to Ionizing Radiation Causes Endoplasmic Reticulum Stress in the Mouse Hippocampus.

It is well known that ionizing radiation-induced toxicity to normal tissue has functional consequences in the brain. However, the underlying molecular alterations have yet to be elucidated. We have previously reported cognitive impairments with concomitant changes in dendritic complexity, spine density and inflammation in mice at 6-24 weeks postirradiation. The goal of this study was to determine whether metabolic changes in the mouse hippocampus after whole-body (4 Gy) or cranial (9 Gy) X-ray irradiation might trigger some of the incipient changes contributing to the persisting pathology in the radiation-injured brain. Metabolomic and lipidomic profiling of hippocampal tissue revealed that radiation induced dyslipidemia in mice at two days and two weeks postirradiation. Strikingly, significant changes were also observed in metabolites of the hexosamine biosynthesis pathway, a finding that was further confirmed using orthogonal methodologies. We hypothesize that these changes in hexosamine metabolism could induce endoplasmic reticulum stress and contribute to radiation-induced cognitive impairments. Taken together, our results show that molecular phenotyping is a valuable approach to identify potentially detrimental pathway perturbations that manifest significantly earlier than gross structural and functional changes in the irradiated brain.

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.

A Metabolomic and Lipidomic Serum Signature from Nonhuman Primates Administered with a Promising Radiation Countermeasure, Gamma-Tocotrienol.

The development of radiation countermeasures for acute radiation syndrome (ARS) has been underway for the past six decades, leading to the identification of multiple classes of radiation countermeasures. However, to date, only two growth factors (Neupogen and Neulasta) have been approved by the United States Food and Drug Administration (US FDA) for the mitigation of hematopoietic acute radiation syndrome (H-ARS). No radioprotector for ARS has been approved by the FDA yet. Gamma-tocotrienol (GT3) has been demonstrated to have radioprotective efficacy in murine as well as nonhuman primate (NHP) models. Currently, GT3 is under advanced development as a radioprotector that can be administered prior to radiation exposure. We are studying this agent for its safety profile and efficacy using the NHP model. In this study, we analyzed global metabolomic and lipidomic changes using ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight mass spectrometry (QTOF-MS) in serum samples of NHPs administered GT3. Our study, using 12 NHPs, demonstrates that alterations in metabolites manifest only 24 h after GT3 administration. Furthermore, metabolic changes are associated with transient increase in the bioavailability of antioxidants, including lactic acid and cholic acid and anti-inflammatory metabolites 3 deoxyvitamin D3, and docosahexaenoic acid. Taken together, our results show that the administration of GT3 to NHPs causes metabolic shifts that would provide an overall advantage to combat radiation injury. This initial assessment also highlights the utility of metabolomics and lipidomics to determine the underlying physiological mechanisms involved in the radioprotective efficacy of GT3.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study.

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.