Serial Metabolome Changes in a Prospective Cohort of Subjects with

Article

Serial metabolome changes in a prospective cohort of subjects with influenza viral infection and comparison with dengue fever Liang Cui, Jinling Fang, Eng Eong Ooi, and Yie Hou Lee J. Proteome Res., Just Accepted Manuscript • Publication Date (Web): 31 May 2017 Downloaded from http://pubs.acs.org on June 1, 2017

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Journal of Proteome Research

Serial metabolome changes in a prospective cohort of subjects with influenza viral infection and comparison with dengue fever Liang Cui1,2, Jinling Fang2, Eng Eong Ooi3, Yie Hou Lee1*

1

Translational ‘Omics and Biomarkers Group, KK Research Centre, KK Women’s and

Children’s Hospital, Singapore. 2

Infectious Diseases Interdisciplinary Group, Singapore-MIT Alliance for Research and

Technology, Singapore 3

Emerging Infectious Diseases, Duke-NUS Medical School, Singapore

*Correspondence to and request for re-prints: Yie Hou Lee Ph.D.; Email: [email protected]; Address: 100 Bukit Timah Road, Singapore 229899; Telephone: +65-6394 812

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ACS Paragon Plus Environment


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ABSTRACT Influenza virus infection (IVI) and dengue virus infection (DVI) are major public health threats. Between IVI and DVI, clinical symptoms can be overlapping yet infection-specific, but host metabolome changes are not well-described. Untargeted metabolomics and targeted oxylipinomic analyses were performed on sera serially collected at three phases of infection from a prospective cohort study of adult subjects with either H3N2 influenza infection or dengue fever. Untargeted metabolomics identified 26 differential metabolites and major perturbed pathways included purine metabolism, fatty acid biosynthesis and β-oxidation, tryptophan metabolism, phospholipid catabolism and steroid hormone pathway. Alterations in eight oxylipins were associated with the early symptomatic phase of H3N2 flu infection, were mostly arachidonic acid-derived, and enriched in the lipoxygenase pathway. There was significant overlap in metabolome profiles in both infections. However, differences specific to IVI and DVI were observed. DVI specifically attenuated metabolites including serotonin, bile acids and biliverdin. Additionally, metabolome changes were more persistent in IVI, in which metabolites such as hypoxanthine, inosine and xanthine of the purine metabolism pathway remained significantly elevated at 21-27 days after fever onset. This study revealed the dynamic metabolome changes in IVI subjects, and providing biochemical insights on host physiological similarities and differences between IVI and DVI. Keywords: metabolomics, oxylipins, infectious diseases, influenza, H3N2, dengue

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INTRODUCTION Influenza viruses (family Orthomyxoviridae) cause in an estimated 1 billion infections worldwide that result in annual epidemics and intermittent pandemics. Vaccination and antiviral drugs are the main therapeutic strategies used in controlling influenza virus infection (IVI). Despite immunization programs, influenza viruses are a major cause of morbidity. Influenza viruses undergo frequent and extensive genetic changes, and vaccines need to be updated frequently to maintain activity against with the constantly evolving circulating virus.1,2 Clinical features of influenza infection overlap with other pathogens, especially viral infections. Dengue viruses, of family flaviviridae, is the fastest growing arbovirus in the world cause an estimated 100 million symptomatic infections ever year. Influenza and dengue overlap geographically in many tropical and subtropical regions of the world.3 Several clinical symptoms such as fever, fatigue and headaches are common to both IVI and dengue virus infection (DVI), severe dengue notwithstanding, and may lead to misdiagnosis in dengueendemic areas and concurrent outbreaks.4,5 By contrast, symptoms of IVI, like runny nose, sore throat, cough, and nasal congestion, do not usually present in DVI.5 Conversely, some of the symptoms are DVI-specific, such as skin rash, and joint and muscle pain.6 The fact that IVI and DVI have both overlapping and infection-specific symptoms suggests that there are both common and distinct host responses to the two viruses. However, the differences and similarities of metabolome changes between IVI and DVI remain poorly known. Metabolomics, the analysis of the global changes of small molecule metabolites in biological systems in response to biological stimuli or perturbations, is a rapidly evolving field in systems biology.7 Metabolites are the final downstream products of gene expression and form a direct link between molecular changes and phenotypes. Thus, by studying the changing metabolite levels, metabolomics provides a functional readout of cellular activities and has played a key role in revealing metabolic pathways related to disease processes.8–10 3



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Oxylipins, a subset of the metabolome, form a major class of bioactive inflammatory signalling lipid mediators, synthesized from free omega-6 polyunsaturated fatty acids (n-6 PUFA), including arachidonic acid (AA), linoleic acid (LA), and dihomo-gamma-linolenic acid (DGLA), or omega-3 polyunsaturated fatty acids (n-3 PUFA), including eicosapentenoic acid (EPA), docosahexanoic acid (DHA), and alpha-linolenic acid (ALA)11. Upon liberation from membrane bound phospholipids by activation of phospholipases and subsequent oxidation by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 epoxygenase (CYP450) systems, oxylipins are generated. Both metabolomics and oxylipinomics analysis have been applied to infectious diseases to study host-pathogen interactions.12,13 Recently, oxylipinomics analysis has been conducted to explore their potential contributions to severe complications of IVI in mouse models.13–15 Our aim here is to profile the time-series dynamics of host responses to influenza A H3N2 infections and identify key metabolic pathways by employing both untargeted metabolomics and targeted oxylipinomics analysis on sera of IVI subjects. Serum metabolome comparisons between IVI and mild DVI (dengue fever; DF) from the same study cohort were made to characterize IVI and DVI-overlapping and specific host changes.

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MATERIALS AND METHODS Ethics statement and clinical samples The early dengue infection and outcome (EDEN) study is a multi-center longitudinal study of adult febrile infections in Singapore.16 The details of patient recruitment, sample collection and the study protocols have been described earlier.17 In brief, adult subjects (>21 years) presenting with acute onset fever (≥ 37.5°C for less than 72 h) were included in the study. Nasopharyngeal

swabs

collected

were

tested

for

viral

pathogens

by

direct

immunofluorescence assay (DFA) and virus isolation using R-MixTM cells in shell vials (Diagnostic Hybrids Inc., Athens, Ohio) according to the manufacturer’s instructions. The D3 Ultra DFA Respiratory Virus Screening and ID Kit (Diagnostic Hybrids Inc., Athens, Ohio) was used to detect for influenza A and B, RSV, human metapneumovirus, adenovirus, and parainfluenza 1, 2 and 3 in both DFA as well as shell vial cultures. Subtyping of influenza A virus was carried out by RT-PCR using previously published protocols.18,19 Venous blood samples were serially collected at enrolment (visit 1; early phase) as well as on fever day 4 to 7 (visit 2; mid phase) and weeks 3 to 4 (visit 3; recovery phase), aliquoted and frozen at 80°C until use. ‘Fever day’ here refers to number of days post onset of fever. As the main inclusion criteria in this study was fever, we leveraged on this study for dengue negative but influenza A (H3N2) positive subjects. The study cohort comprised of 20 subjects infected with influenza A virus who had their serum cytokines and markers of macrophage and neutrophil activities analyzed previously (Table 1).20 Additionally, we used serum samples from 24 asymptomatic age- and gender-matched healthy subjects as controls. Enrollment of eligible individuals was based on written informed consents and the protocols were approved by the National Healthcare Group (DSRB B/05/013). All samples were anonymized after the completion of demographic and clinical data collection.

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Serum sample preparation For untargeted metabolomics analysis, sample preparation followed a previously published report with some modifications.21,22 A volume of 50 µL from each serum sample was thawed at 4°C, and serum proteins were precipitated with 200 µL ice-cold methanol, which contained 10 µg/mL 9-fluorenylmethoxycarbonyl-glycine as an internal standard (ISTD). After vortexing, the mixture was centrifuged at 16,000 rpm for 10 min at 4°C and the supernatant was collected and evaporated to dryness in a speedvacuum evaporator. The dry extracts were then redissolved in 200 µL of water/methanol (98:2; v/v) for liquid chromatography-mass spectrometry (LC-MS) analysis. For targeted oxylipins analysis, sample preparation followed a previously published report with some modifications.21 Oxylipinomics analysis was only conducted with serum samples of influenza subjects without healthy controls because of limited sera volume from healthy controls. A volume of 50 µL from each serum sample was thawed at 4°C, and serum proteins were precipitated with 200 µL ice-cold methanol, which contained 5 ng/mL ISTDs. After vortexing, the mixture was sonicated on ice-water for 15 min, and then centrifuged at 16,000 rpm for 10 min at 4°C. The supernatant was collected and evaporated to dryness in a speedvacuum evaporator and the dried extract was subsequently reconstituted in 50 µL solution of water/acetonitrile (7:3; v/v) containing 2.8 ng/mL CUDA as a quality marker for LC-MS/MS analysis. All samples for both untargeted and targeted analysis were kept at 4°C and analyzed within 48 h. In order to prevent batch effect, the samples were randomized prior to LC-MS. Quality control (QC) samples were prepared by mixing equal amounts of serum samples from all the samples and processed as per other samples. The QC samples were interspersed throughout the run after every eight samples to monitor the stability of the system. A summary of the workflow utilized in untargeted and targeted metabolomics studies is shown in Figure S1. 6


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Untargeted metabolomics analysis by LC-MS Untargeted metabolomics was performed as previously described.22 The supernatant fraction from sample preparation step was analyzed using Agilent 1290 ultrahigh pressure liquid chromatography system (Waldbronn, Germany) equipped with a 6520 QTOF mass detector managed by a MassHunter workstation. The column used for the separation was an Agilent rapid resolution HT Zorbax SB-C18 (2.1×50 mm, 1.8 mm; Agilent Technologies, Santa Clara, CA, USA). The oven temperature was set at 45°C. The gradient elution involved a mobile phase consisting of (A) 0.1% formic acid in water and (B) 0.1% formic acid in methanol. The initial condition was set at 5% mobile B. A 7 min linear gradient to 70% B was applied, followed by a 12 min gradient to 100% B which was held for 3 min, then returned to starting conditions over 0.1 min. Flow rate was set at 0.4 mL/min, and 5 µL of samples was injected. Typical untargeted metabolomics profiles of the different groups are shown in Figure S2. The electrospray ionization mass spectra were acquired in both positive and negative ion modes. Mass data were collected between m/z 100 and 1000 at a rate of two scans per second. The ion spray voltage was set at 4,000 V, and the heated capillary temperature was maintained at 350°C. The drying gas and nebulizer nitrogen gas flow rates were 12.0 L/min and 50 psi, respectively. Two reference masses were continuously infused to the system to allow constant mass correction during the run: m/z 121.0509 (C5H4N4) and m/z 922.0098 (C18H18O6N3P3F24). Targeted oxylipins analysis by LC-MS/MS The LC-MS/MS analysis followed published reports with some modifications.23,24 The supernatant fraction from sample preparation step was analyzed using Agilent 1290 ultrapressure liquid chromatography (Waldbronn, Germany) equipped with an electrospray ionization with iFunnel technology on a triple quadrupole mass spectrometer (6490 QqQ, 7



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Agilent Technologies, US). The column used for the separation was an Agilent rapid resolution HT Zorbax SB-C18 column (2.1×100 mm, 1.8 µm; Agilent Technologies, Santa Clara, CA, USA). The oven temperature was set at 40°C. The gradient elution involved a mobile phase consisting of (A) 0.1% acetic acid in water and (B) acetonitrile/ isopropanol (50:50; v/v). The initial condition was set at 15% B. A 11 min linear gradient to 60% B was applied, followed by a 17 min gradient to 100% B which was held for 5 min, then returned to starting conditions over 0.1 min. Flow rate was set at 0.4 mL/min, and 10 µL of samples was injected. A UPLC-MS/MS chromatogram of a standards mix is shown in Figure S3. Electrospray ionization was performed in negative ion mode with the following source parameters: drying gas (N2) temperature 200°C with a flow of 14 L/min, nebulizer gas pressure 30 psi, sheath gas temperature 400°C with a flow of 11 L/min, capillary voltage 3,000 V and nozzle voltage 800 V. Compounds were quantified in multiple reaction monitoring (MRM) mode with time segments defined as Table S1. Data acquisition and processing were performed using MassHunter software (Agilent Technologies, US). The recoveries were evaluated by spiking defined amounts of deuterated ISTDs into aliquots of unprocessed serum and calculated by comparing peak areas from serum against mean peak areas of three equal amounts of unprocessed compounds in pure solvent. The recoveries generally ranged from 55.0% to 65.2%. For intra-batch and inter-batch precision and accuracy, the relative standard deviation (RSD) values ranged from 2.5% to 18.9% and 1.5% to 15.9%, respectively. Data analysis Raw spectrometric data in untargeted metabolomics were analyzed by MassHunter Qualitative Analysis software (Agilent Technologies, US) and the molecular features characterized by retention time (RT), chromatographic peak intensity and accurate mass, were obtained by using the Molecular Feature Extractor algorithm. The features were then 8


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analyzed by MassHunter Mass Profiler Professional software (Agilent Technologies, US). Only features with an intensity ≥ 20,000 counts (approximately three times the limit of detection of our LC-MS instrument), and found in at least 80% of the samples at the same sampling time point signal were kept for further processing. Next, a tolerance window of 0.15 min and 2 mDa was used for alignment of RT and m/z values, and the data normalized to spiked 9-fluorenylmethoxycarbonyl-glycine ISTD. Raw spectrometric data in targeted metabolomics were processed using MassHunter Workstation Quantitative Analysis software (Agilent Technologies, US). For statistical analysis, nonparametric Test (Wilcoxon, Mann–Whitney test) with BenjaminiHochberg Multiple Testing Correction was employed and statistical significance was set at p1 were considered to be influential for the separation of samples in OPLS-DA analysis. In addition, fold change (FC) analysis was performed to further filter the features and only those features with FC >1.5 were selected as potential significantly altered metabolites. Compound identification The structure identification of the differential metabolites was based on our published work.22 Briefly, the elemental compositions of the metabolites were first calculated based on the exact mass, the nitrogen rule and the isotope pattern by Masshunter software (Agilent). Then, the elemental composition and exact mass were used for open source database searching, including LIPIDMAPS (http://www.lipidmaps.org/), HMDB (http://www.hmdb.ca/), METLIN (http://metlin.scripps.edu/) and MassBank (http://www.massbank.jp/). Next, MS/MS 9



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experiments were performed to obtain structural information via the interpretation of the fragmentation pattern of the metabolite. The MS/MS spectra of possible metabolite candidates in the databases were also searched and compared (Figure S4). Finally, the metabolites were confirmed by comparison with the standards where commercially available, which was the case for serotonin and kynurenine. The metabolites are listed according to the minimum reporting standards for chemical analysis in metabolomics recommended by Metabolomics Standard Initiative (MSI).25,26 Briefly, MSI is a four-level system ranging from Level 1 (identified metabolites) via Levels 2 and 3 (putatively annotated compounds and compound classes) to Level 4 (unidentified or unclassified metabolites which can still be differentiated based on spectrum data).

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RESULTS Global metabolome changes in serum of IVI subjects In order to obtain reliable metabolic profiles of the samples, we evaluated the stability and reproducibility of the LC-MS method by performing PCA on all the IVI samples including all the QC samples.27 As shown in Figure 1A, the QC samples clustered in PCA scores plots, indicating good stability and reproducibility of the chromatographic separation throughout the entire sequence. Next, to obtain a snapshot of metabolome alterations and their relationships at the different stages of influenza infection, we examined our metabolomics data using PCA. The PCA scores plot revealed no distinct segregation of influenza subjects at the different visits, although all influenza subjects clearly diverged from the healthy controls (Figure 1A). Significant metabolome changes could still be observed at three to four weeks after IVI as seen at visit 3, consistent with lingering clinical symptoms seen in IVI.28,29 This was different from DF subjects whereby clear segregation of dengue subjects at each visit, and reversible metabolome changes were observed.22 Compared with healthy controls, the most predominant metabolome changes in DVI happened at visit 1, and the changes returned to the control levels at visit 3 (Figure 1B). Identification of significantly altered metabolites and pathways in H3N2 infections 130 differential metabolites identified via LC-MS/MS met the pre-determined criteria (VIP >1 in OPLS-DA analysis; p

Serial Metabolome Changes in a Prospective Cohort of Subjects with

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