Early Detection of Cystic Fibrosis Acute Pulmonary Exacerbations by Exhaled Breath Condensate Metabolomics

The most common cause of death in cystic fibrosis (CF) patients is progressive lung function decline, which is punctuated by acute pulmonary exacerbations (APEs). A major challenge is to discover biomarkers for detecting an oncoming APE and allow for pre-emptive clinical interventions. Metabolic profiling of exhaled breath condensate (EBC) samples collected from CF patients before, during, and after APEs and under stable conditions (n = 210) was performed using ultraperformance liquid chromatography (UPLC) coupled to Orbitrap mass spectrometry (MS). Negative ion mode MS data showed that classification between metabolic profiles from "pre-APE" (pending APE before the CF patient had any signs of illness) and stable CF samples was possible with good sensitivities (85.7 and 89.5%), specificities (88.4 and 84.1%), and accuracies (87.7 and 85.7%) for pediatric and adult patients, respectively. Improved classification performance was achieved by combining positive with negative ion mode data. Discriminant metabolites included two potential biomarkers identified in a previous pilot study: Lactic acid and 4-hydroxycyclohexylcarboxylic acid. Some of the discriminant metabolites had microbial origins, indicating a possible role of bacterial metabolism in APE progression. The results show promise for detecting an oncoming APE using EBC metabolites, thus permitting early intervention to abort such an event.Fil: Zang, Xiaoling. Georgia Institute of Techology; Estados UnidosFil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Gaul, David A.. Georgia Institute of Techology; Estados UnidosFil: McCarty, Nael A.. University of Emory; Estados UnidosFil: Stecenko, Arlene. University of Emory; Estados UnidosFil: Fernández, Facundo M.. Georgia Institute of Techology; Estados Unido

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models

Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape

Tumor immune escape is an important strategy of tumor survival. There are many mechanisms of tumor immune escape, including immunosuppression, which has become a research hotspot in recent years. The programmed death ligand-1/programmed death-1 (PD-L1/PD-1) signaling pathway is an important component of tumor immunosuppression, which can inhibit the activation of T lymphocytes and enhance the immune tolerance of tumor cells, thereby achieving tumor immune escape. Therefore, targeting the PD-L1/PD-1 pathway is an attractive strategy for cancer treatment; however, the therapeutic effectiveness of PD-L1/PD-1 remains poor. This situation requires gaining a deeper understanding of the complex and varied molecular mechanisms and factors driving the expression and activation of the PD-L1/PD-1 signaling pathway. In this review, we summarize the regulation mechanisms of the PD-L1/PD-1 signaling pathway in the tumor microenvironment and their roles in mediating tumor escape. Overall, the evidence accumulated to date suggests that induction of PD-L1 by inflammatory factors in the tumor microenvironment may be one of the most important factors affecting the therapeutic efficiency of PD-L1/PD-1 blocking

LC and LC-free mass spectrometry applications in non-targeted metabolomics for disease detection and early prediction

Metabolomics is the science of studying small molecule composition of biological systems. Non-targeted metabolomics, as the analytical technology for unbiased simultaneous measurement and analysis of the collection of low molecular weight metabolites within biological samples, has been widely adopted as a novel and powerful approach to study pathophysiological processes and discover potential biomarkers for disease diagnosis and preventive screening. By comparing and analyzing the global metabolome of different classes of samples with different phenotypes, non-targeted metabolomics can serve as a top-down strategy to discover disease related metabolic perturbations, and it has been applied in studies of various diseases. In this thesis work, mass spectrometry (MS) based non-targeted metabolomics was applied to discover potential biomarkers of two kinds of diseases: prostate cancer (PCa) and cystic fibrosis (CF) acute pulmonary exacerbations (APEs). Current clinical practices for prostate cancer (PCa) diagnosis focus on prostate-specific antigen (PSA) level. Although it exibits fair discriminating power for PCa detection, the PSA test for PCa screening remains controversial due to the risk of over-diagnosis and overtreatment. Another disease we have studied, CF lung disease, has intermittent episodes of acute worsening of symptoms termed acute pulmonary exacerbations (APEs), which is a major cause of morbidity for CF patients. To date, however, there is no consensus diagnostic criteria for CF APEs. Also, there is no preventive screening method for stable CF patients to signal an oncoming APE event, which hinders the initiation of early intervention before the establishment of substantial immune response. These drawbacks, together with a lack of in-depth information on the pathophysiology of these two diseases may prevent clinicians from making the best possible therapeutic interventions and treatment decisions to improve patient healthcare. Consequently, there has been a constant drive to discover novel biomarkers to improve PCa diagnosis and prediction of APE onset in CF patients via non-targeted metabolomics strategy. Mass spectrometry (MS) has been increasingly applied in metabolomics studies due to its high sensitivity. MS methods often include chromatography separation prior to ion detection, which helps to increase metabolite coverage and resolution, decrease spectral congestion and ion suppression (or enhancement) effects. As current metabolomics research focuses more on large scale studies with hundreds to thousands of samples, high-throughput metabolic profiling techniques with fast sample analysis speed become a pivotal necessity. Flow injection (FI) and direct infusion (DI) MS are alternative approaches involving direct introduction of biological samples into MS systems without prior chromatography separation, increasing sample analysis speed. The combination of FI or DI methods with ion mobility (IM) MS is generally appealing for its ability to simplify spectra, raise signal to noise ratio by eliminating chemical noise, produce cleaner MS/MS spectra and provide rapid separation of closely related compounds. Therefore this strategy has great potential in non-targeted metabolomics research demanding high sample throughput. In this thesis work, liquid chromatography (LC) MS method and LC-free FI-IM-MS and DI-IM-MS methods were employed for metabolic profiling of biological samples to find potential biomarkers for PCa and CF APEs and study the associated metabolic perturbations. An introduction to MS-based non-targeted metabolic profiling for human disease studies is provided in Chapter 1, with recent developments in disease biomarker discovery reviewed. Sample preparation, MS platforms utilized, metabolite identification, innovations in data analysis and pathway mapping were discussed. Part I of the dissertation consists of Chapters 2 and 3, which present LC-MS based non-targeted metabolomics studies of PCa and CF APE diseases. In Chapter 2, a metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict PCa in serum samples with a panel of 40 metabolic features, yielding 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was demonstrated to be higher than the prevalent PSA test. The identification of amino acids, fatty acids, lysophospholipids, and bile acids provided insights into the metabolic alterations associated with the disease. In addition, several metabolites were mapped to the steroid hormone biosynthesis pathway, indicating its association with PCa. Chapter 3 discusses the feasibility of predicting APE in CF patients using EBC metabolites. In a pilot study, LC-MS was used to profile metabolites in exhaled breath condensate (EBC) samples in negative ion mode from 17 clinically stable CF patients, 9 CF patients with an APE severe enough to require hospitalization (termed APE), 5 CF patients during recovery from a severe APE (termed post-APE), and 4 CF patients who were clinically stable at the time of collection but in the subsequent 1 to 3 months developed a severe APE (termed pre-APE). Using multivariate analysis, a panel containing 2 metabolic discriminant features identified as 4-hydroxycyclohexylcarboxylic acid and pyroglutamic acid differentiated the APE from the stable CF samples with 84.6% accuracy. In addition, the pre-APE samples were distinguished from the stable CF samples with 90.5% accuracy using a panel of two discriminant features including lactic acid and pyroglutamic acid. In a larger EBC sample cohort (n=210) study, negative ion mode data and the combination of negative and positive ion mode data showed that classification was possible for age and gender-matched samples grouped into adult and pediatric patients. Negative ion mode data yielded acceptable sensitivities (83.3% and 76.2%), specificities (91.7% and 83.7%), and accuracies (88.9% and 81.3%) for discriminating APE from stable CF EBC samples, from pediatric and adult patients, respectively. For the pre-APE vs. stable CF comparison, good sensitivities (85.7% and 89.5%), specificities (88.4% and 84.1%), and accuracies (87.7% and 85.7%) were obtained for EBC samples from pediatric and adult patients, respectively. By combining positive with negative ion mode data, improved classification performance was achieved for most binary comparisons with accuracies enhanced between 3 and 9.6%. The discriminant metabolites identified in the pilot study were also selected in some of the discriminant metabolite panels. Some of the identified discriminant metabolites had microbial relevance, indicating a possible central role of bacterial metabolism in APE development. Part II of the dissertation includes Chapters 4 and 5, describing non-targeted metabolomics studies on PCa and CF APE disease using LC-free FI-IM-MS and DI-IM-MS. Chapter 4 presents the application of FI-IM-MS to the non-targeted metabolic profiling of serum extracts from 61 PCa patients and 42 controls from the same cohort in Chapter 2. Comprehensive data mining of the mobility-mass domain was used to discriminate compounds with various charges and filter matrix salt cluster ions. Specific criteria were set to ensure correct grouping of adducts, in-source fragments, and impurities in the dataset. Endogenous metabolites were identified with high confidence using tandem MS experiments and collision cross-section (CCS) matching with chemical standards or CCS databases. PCa patient samples were distinguished from control samples with good accuracies (88.3-89.3%), sensitivities (88.5-90.2%), and specificities (88.1%) using supervised multivariate classification methods. Results from this study show the potential of FI-IM-MS as a high throughput metabolic profiling tool for large scale metabolomics studies. In Chapter 5, transmission-mode direct analysis in real time (TM-DART) coupled to IM-MS was tested as a high-throughput alternative to conventional DI electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) methods, and the performances of the three ionization methods were compared. When using pooled EBC collected from a healthy control, ESI detected the most metabolites, and TM-DART the least. TM-DART-TWIM-TOF-MS was used to profile metabolites in the EBC samples from 5 healthy individuals and 4 CF patients, and a panel of 3 discriminant EBC metabolites was found to differentiate these two classes with excellent cross-validated accuracy. Appendix A presents a collaborative work that combined results from surface enhanced Raman spectroscopy (SERS), metabolomics and proteomics experiments, to study the molecular mechanisms of the cellular processes during the plasmonic photothermal therapy (PPTT) process. Our metabolomics results showed increased levels of phenylalanine and metabolites tentatively identified as its derivatives and phenylalanine-containing peptides, aiding in assignments of SERS bands with observed changes during PPTT. To better understand the mechanism of phenylalanine increase upon PPTT, we combined metabolomics and proteomics results using network analysis, which demonstrated that phenylalanine metabolism was perturbed. In addition, several apoptosis pathways were activated via key proteins (e.g. HADHA and ACAT1), which are consistent with the proposed role of altered phenylalanine metabolism in inducing apoptosis. At last, Chapter 6 summarizes the conclusions drawn from the thesis work, and also presents the outlook and possible future work.  Ph.D

Flow Injection-Traveling-Wave Ion Mobility-Mass Spectrometry for Prostate-Cancer Metabolomics

Flow injection-traveling-wave ion mobility-mass spectrometry (FITWIM-MS) was applied to the nontargeted metabolic profiling of serum extracts from 61 prostate-cancer (PCa) patients and 42 controls with an analysis speed of 6 min per sample, including a 3 min wash run. Comprehensive data mining of the mobility-mass domain was used to discriminate species with various charge states and filter matrix saltcluster ions. Specific criteria were developed to ensure correct grouping of adducts, insource fragments, and impurities in the data set. Endogenous metabolites were identified with high confidence using FI-TWIM-MS/MS and collision-cross-section (CCS) matching with chemical standards or CCS databases. PCa patient samples were distinguished from control samples with good accuracies (88.3-89.3%), sensitivities (88.5-90.2%), and specificity (88.1%) using supervised multivariate classification methods. Although largely underutilized in metabolomics studies, FI-TWIM-MS proved advantageous in terms of analysis speed, separation of ions in complex mixtures, improved signal-to-noise ratio, and reduction of spectral congestion. Results from this study showcase the potential of FITWIM-MS as a high-throughput metabolic-profiling tool for large-scale metabolomics studies.Fil: Zang, Xiaoling. Georgia Institute of Techology; Estados UnidosFil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: Gaul, David A.. Georgia Institute of Techology; Estados UnidosFil: Fernandez, Facundo M.. Georgia Institute of Techology; Estados Unido

Changes in Lipidomics, Metabolomics, and the Gut Microbiota in CDAA-Induced NAFLD Mice after Polyene Phosphatidylcholine Treatment

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in most parts of the world. Although there is no first-line drug approved for the treatment of NAFLD, polyene phosphatidylcholine (PPC) is used by clinicians to treat NAFLD patients. This study aimed to evaluate the efficacy of PPC on a mice model of NAFLD, and to study the PPC’s mechanism of action. The mice were fed a choline-deficient, L-amino acid-defined (CDAA) diet to induce NAFLD and were subsequently treated with PPC. The treatment effects were evaluated by the liver index, histopathological examination, and routine blood chemistry analyses. Lipidomics and metabolomics analyses of 54 samples were carried out using ultraperformance liquid chromatography (UPLC) coupled to a mass spectrometer to select for changes in metabolites associated with CDAA diet-induced NAFLD and the effects of PPC treatment. The intestinal flora of mice were extracted for gene sequencing to find differences before and after the induction of NAFLD and PPC treatment. PPC significantly improved the CDAA diet-induced NAFLD condition in mice. A total of 19 metabolites including 5 polar metabolites and 14 lipids showed marked changes. In addition, significant differences in the abundance of Lactobacillus were associated with NAFLD. We inferred that the protective therapeutic effect of PPC on the liver was related to the supplement of phosphatidylcholine, lysophosphatidylcholine, and sphingomyelin (PC, LPC, and SM, resectively) and acylcarnitine metabolism. This study developed a methodology for exploring the pathogenesis of NAFLD and can be extended to other therapeutic agents for treating NAFLD

Feasibility of Early Detection of Cystic Fibrosis Acute Pulmonary Exacerbations by Exhaled Breath Condensate Metabolomics: A Pilot Study

Progressive lung function decline and, ultimately, respiratory failure are the most common cause of death in patients with cystic fibrosis (CF). This decline is punctuated by acute pulmonary exacerbations (APEs), and in many cases, there is a failure to return to baseline lung function. Ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry was used to profile metabolites in exhaled breath condensate (EBC) samples from 17 clinically stable CF patients, 9 CF patients with an APE severe enough to require hospitalization (termed APE), 5 CF patients during recovery from a severe APE (termed post-APE), and 4 CF patients who were clinically stable at the time of collection but in the subsequent 1-3 months developed a severe APE (termed pre-APE). A panel containing two metabolic discriminant features, 4-hydroxycyclohexylcarboxylic acid and pyroglutamic acid, differentiated the APE samples from the stable CF samples with 84.6% accuracy. Pre-APE samples were distinguished from stable CF samples by lactic acid and pyroglutamic acid with 90.5% accuracy and in general matched the APE signature when projected onto the APE vs stable CF model. Post-APE samples were on average more similar to stable CF samples in terms of their metabolomic signature. These results show the feasibility of detecting and predicting an oncoming APE or monitoring APE treatment using EBC metabolites.Fil: Zang, Xiaoling. Georgia Institute of Techology; Estados UnidosFil: Monge, Maria Eugenia. Georgia Institute of Techology; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; ArgentinaFil: McCarty, Nael A.. University of Emory; Estados UnidosFil: Stecenko, Arlene A.. University of Emory; Estados UnidosFil: Fernández, Facundo M.. Georgia Institute of Techology; Estados Unido

Dual Effect of Tryptamine on Prostate Cancer Cell Growth Regulation: A Pilot Study

Abnormal tryptophan metabolism is linked to cancer and neurodegenerative diseases, and tryptophan metabolites have been reported as potential prostate cancer (PCa) biomarkers. However, little is known about the bioactivities of tryptophan metabolites on PCa cell growth. In this study, MTT and transwell assays were used to study the cytotoxicities of 13 major tryptophan metabolites on PCa and normal prostate epithelial cell lines. Ultraperformance liquid chromatography–high resolution mass spectrometry (UPLC–HRMS) was used to analyze metabolic changes in cells treated with tryptamine. Flow cytometry, confocal imaging, and Western blot were used to test the apoptosis induced by tryptamine. It was shown that tryptamine had obvious inhibitory effects on PCa cell lines PC-3 and LNCaP, stronger than those on the normal prostate cell line RWPE-1. Tryptamine was further shown to induce apoptosis and inhibit PC-3 cell migration. Metabolic changes including amino acid metabolism related to cell proliferation and metastasis were found in PC-3 cells treated with tryptamine. Furthermore, a PC-3 xenograft mouse model was used to study the effect of tryptamine in vivo. The intratumoral injection of tryptamine was demonstrated to significantly reduce the tumor growth and tumor sizes in vivo; however, intraperitoneal treatment resulted in increased tumor growth. Such dual effects in vivo advanced our understanding of the bioactivity of tryptamine in regulating prostate tumor development, in addition to its major role as a neuromodulator

Arterial stiffness acute changes following aerobic exercise in males with and without hypertension

Abstract While regular exercise exposure is considered the most effective therapy to reduce arterial stiffness, the effect of acute exercise training on arterial stiffness in adults with different blood pressure (BP) levels remains unclear. The authors aimed to investigate the effects of acute aerobic exercise on arterial stiffness in male with different BP levels. This cross‐sectional study utilized data for 1200 males aged 20–49 years from the Kailuan study cohort who participated in the fifth National Fitness Monitoring project. A total of 940 participants (621 in the non‐hypertensive group and 319 in the hypertensive group) aged 36.82 ± 7.76 who completed a twice‐quantitative cycle ergometer exercise and measure of brachial‐ankle pulse wave velocity (baPWV) at both the baseline and immediately after exercise were included in this study. The baPWV was decreased after acute aerobic exercise in the non‐hypertension and hypertension groups (Δ 40.29 [95% confidence interval [CI], −47.72 to −32.86] vs. Δ20.45 [95% CI, −31.32 to −9.58] cm/s). Participants without hypertension showed a greater decrease in baPWV (Δ 19.84 [95% CI, −33.83 to −5.84] cm/s) than participants with hypertension. Aerobic exercise had an acute positive effect on arterial stiffness. This study provides evidence of a greater reduction in arterial stiffness in individuals without hypertension than in those with hypertension

An Adaptive Spatial Filtering Method for Multi-Channel EMG Artifact Removal During Functional Electrical Stimulation With Time-Variant Parameters

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts