Serum metabolomics of treatment response in myasthenia gravis.

OBJECTIVE: High-dose prednisone use, lasting several months or longer, is the primary initial therapy for myasthenia gravis (MG). Upwards of a third of patients do not respond to treatment. Currently no biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone. METHODS: We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response. RESULTS: Increased serum levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), γ-cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90 irrespective of gender, age, thymectomy or baseline prednisone use. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. There was no association with outcome and gender, age, thymectomy or baseline prednisone use. INTERPRETATION: We have defined a metabolomic and lipidomic profile that can now undergo validation as a treatment predictive marker for MG patients undergoing corticosteroid therapy. Metabolomic profiles of outcome measures had limited overlap consistent with their assessing distinct aspects of treatment response and supporting unique biological underpinning for each outcome measure. Interindividual variation in prednisone metabolism may be a determinate of how well patients respond to treatment.

Peptide-based urinary monitoring of fibrotic nonalcoholic steatohepatitis by mass-barcoded activity-based sensors.

Noninvasive detection of nonalcoholic steatohepatitis (NASH), the progressive form of nonalcoholic fatty liver disease, promises to improve patient screening, accelerate drug trials, and reduce health care costs. On the basis of protease dysregulation of the biological pathways of fibrotic NASH, we developed the Glympse Bio Test System (GBTS) for multiplexed quantification of liver protease activity. GBTS-NASH comprises a mixture of 19 mass-barcoded PEGylated peptides that is administered intravenously and senses liver protease activity by releasing mass-barcoded reporters into urine for analysis by mass spectrometry. To identify a protease signature of NASH, transcriptomic analysis of 355 human liver biopsies identified a 13-protease panel that discriminated clinically relevant NASH ≥F2 fibrosis from F0-F1 with high classification accuracy across two independent patient datasets. We screened 159 candidate substrates to identify a panel of 19 peptides that exhibited high activity for our 13-protease panel. In the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) mouse model, binary classifiers trained on urine samples discriminated fibrotic NASH from simple steatosis and healthy controls across a range of nondisease conditions and indicated disease regression upon diet change [area under receiver operating characteristics (AUROCs) > 0.97]. Using a hepatoprotective triple combination treatment (FXR agonist, ACC and ASK1 inhibitors) in a rat model of NASH, urinary classification distinguished F0-F1 from ≥F2 animals and indicated therapeutic response as early as 1 week on treatment (AUROCs >0.91). Our results support GBTS-NASH to diagnose fibrotic NASH via an infusion of peptides, monitor changes in disease severity, and indicate early treatment response.

Aptasensor based optical detection of glycated albumin for diabetes mellitus diagnosis.

Glycated albumin (GA) has been reported as an important biomarker for diabetes mellitus. This study investigates an optical sensor comprised of deoxyribonucleic acid (DNA) aptamer, semiconductor quantum dot and gold (Au) nanoparticle for the detection of GA. The system functions as a 'turn on' sensor because an increase in photoluminescence intensity is observed upon the addition of GA to the sensor. This is possibly because of the structure of the DNA aptamer, which folds to form a large hairpin loop before the addition of the analyte and is assumed to open up after the addition of target to the sensor in order to bind to GA. This pushes the quantum dot and the Au nanoparticle away causing an increase in photoluminescence. A linear increase in photoluminescence intensity and quenching efficiency of the sensor is observed as the GA concentration is varied between 0-14 500 nM. Time based photoluminescence studies with the sensor show the decrease in binding rate of the aptamer to the target within a specific time period. The sensor was found to have a higher selectivity towards GA than other control proteins. Further investigation of this simple sensor with greater number of clinical samples can open up avenues for an efficient diagnosis and monitoring of diabetes mellitus when used in conjunction with the traditional method of glucose level monitoring.

Radiation Metabolomics: Current Status and Future Directions.

Human exposure to ionizing radiation (IR) disrupts normal metabolic processes in cells and organs by inducing complex biological responses that interfere with gene and protein expression. Conventional dosimetry, monitoring of prodromal symptoms, and peripheral lymphocyte counts are of limited value as organ- and tissue-specific biomarkers for personnel exposed to radiation, particularly, weeks or months after exposure. Analysis of metabolites generated in known stress-responsive pathways by molecular profiling helps to predict the physiological status of an individual in response to environmental or genetic perturbations. Thus, a multi-metabolite profile obtained from a high-resolution mass spectrometry-based metabolomics platform offers potential for identification of robust biomarkers to predict radiation toxicity of organs and tissues resulting from exposures to therapeutic or non-therapeutic IR. Here, we review the status of radiation metabolomics and explore applications as a standalone technology, as well as its integration in systems biology, to facilitate a better understanding of the molecular basis of radiation response. Finally, we draw attention to the identification of specific pathways that can be targeted for the development of therapeutics to alleviate or mitigate harmful effects of radiation exposure.

Application of metabolomics in drug resistant breast cancer research.

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor's response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other "omics" technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Second-harmonic generation scattering directionality predicts tumor cell motility in collagen gels.

Second-harmonic generation (SHG) allows for the analysis of tumor collagen structural changes throughout metastatic progression. SHG directionality, measured through the ratio of the forward-propagating to backward-propagating signal (F/B ratio), is affected by collagen fibril diameter, spacing, and disorder of fibril packing within a fiber. As tumors progress, these parameters evolve, producing concurrent changes in F/B. It has been recently shown that the F/B of highly metastatic invasive ductal carcinoma (IDC) breast tumors is significantly different from less metastatic tumors. This suggests a possible relationship between the microstructure of collagen, as measured by the F/B, and the ability of tumor cells to locomote through that collagen. Utilizing in vitro collagen gels of different F/B ratios, we explored the relationship between collagen microstructure and motility of tumor cells in a "clean" environment, free of the myriad cells, and signals found in in vivo. We found a significant relationship between F/B and the total distance traveled by the tumor cell, as well as both the average and maximum velocities of the cells. Consequently, one possible mechanism underlying the observed relationship between tumor F/B and metastatic output in IDC patient samples is a direct influence of collagen structure on tumor cell motility.