key: cord-270077-mfl0iagr authors: Chefer, Svetlana; Thomasson, David; Seidel, Jurgen; Reba, Richard C.; Bohannon, J. Kyle; Lackemeyer, Mathew G.; Bartos, Chris; Sayre, Philip J.; Bollinger, Laura; Hensley, Lisa E.; Jahrling, Peter B.; Johnson, Reed F. title: Modeling [(18)F]-FDG lymphoid tissue kinetics to characterize nonhuman primate immune response to Middle East respiratory syndrome-coronavirus aerosol challenge date: 2015-11-16 journal: EJNMMI Res DOI: 10.1186/s13550-015-0143-x sha: doc_id: 270077 cord_uid: mfl0iagr BACKGROUND: The pathogenesis and immune response to Middle East respiratory syndrome (MERS) caused by a recently discovered coronavirus, MERS-CoV, have not been fully characterized because a suitable animal model is currently not available. (18)F-Fluorodeoxyglucose ([(18)F]-FDG)-positron emission tomography/computed tomography (PET/CT) as a longitudinal noninvasive approach can be beneficial in providing biomarkers for host immune response. [(18)F]-FDG uptake is increased in activated immune cells in response to virus entry and can be localized by PET imaging. We used [(18)F]-FDG-PET/CT to investigate the host response developing in nonhuman primates after MERS-CoV exposure and applied kinetic modeling to monitor the influx rate constant (K(i)) in responsive lymphoid tissue. METHODS: Multiple [(18)F]-FDG-PET and CT images were acquired on a PET/CT clinical scanner modified to operate in a biosafety level 4 environment prior to and up to 29 days after MERS-CoV aerosol exposure. Time activity curves of various lymphoid tissues were reconstructed to follow the [(18)F]-FDG uptake for approximately 60 min (3,600 s). Image-derived input function was used to calculate K(i) for lymphoid tissues by Patlak plot. RESULTS: Two-way repeated measures analysis of variance revealed alterations in K(i) that was associated with the time point (p < 0.001) after virus exposure and the location of lymphoid tissue (p = 0.0004). As revealed by a statistically significant interaction (p < 0.0001) between these two factors, the pattern of K(i) changes over time differed between three locations but not between subjects. A distinguished pattern of statistically significant elevation in K(i) was observed in mediastinal lymph nodes (LNs) that correlated to K(i) changes in axillary LNs. Changes in LNs K(i) were concurrent with elevations of monocytes in peripheral blood. CONCLUSIONS: [(18)F]-FDG-PET is able to detect subtle changes in host immune response to contain a subclinical virus infection. Full quantitative analysis is the preferred approach rather than semiquantitative analysis using standardized uptake value for detection of the immune response to the virus. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13550-015-0143-x) contains supplementary material, which is available to authorized users. The pathogenesis and immune response to Middle East respiratory syndrome (MERS) caused by a recently discovered coronavirus, MERS-CoV, has not been fully characterized, in part, because a suitable animal model that mimics human MERS is currently not available. Nonhuman primates (NHPs), such as rhesus monkeys (Macaca mulatta) or common marmosets (Callithrix jacchus) inoculated with MERS-CoV via combined intratracheal, intranasal, oral, and ocular routes, develop transient respiratory disease with little or no viremia although lethal disease was observed in a small number of marmosets [1] [2] [3] [4] . 18 F-Fluorodeoxyglucose ([ 18 F]-FDG) PET/CT as a real-time noninvasive approach can be beneficial in providing biomarkers for host immune response and disease progression. [ 18 F]-FDG-PET/CT has been used to track host immune response during monkeypox virus and human immunodeficiency virus-1 infections [5] [6] [7] . As [ 18 F]-FDG uptake is increased in activated macrophages, lymphocytes, and granulocytes during inflammation, the immune response can be localized by PET imaging [8] . Tracking the host response noninvasively is especially useful when animal species studied is limited and/or expensive to obtain or when animals do not develop overt clinical signs of disease. We applied [ 18 F]-FDG-PET/CT imaging to monitor infection development in rhesus macaques after MERS-CoV inhalation. Compared to standardized uptake value (SUV), we increased the accuracy of measurement of [ 18 F]-FDG uptake by applying kinetic modeling and Patlak graphical analysis. We assessed the net [ 18 F]-FDG uptake rate constant (K i ) in primary lymphoid tissues engaged in the host response to MERS-CoV exposure. This study is the first application of the methodology to an acute infectious disease process. Rhesus macaques were housed in a biosafety level 4 containment facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International. Experimental procedures were approved by the National Institute of Allergy and Infectious Diseases (NIAID), Division of Clinical Research (DCR), Animal Care and Use Committee and were in compliance with the Animal Welfare Act regulations, Public Health Service policy, and the Guide for the Care and Use of Laboratory Animals recommendations. For aerosol inhalation, MERS-CoV-Hu/Jordan-N3/2012 strain (GenBank accession no. KC776174.1) [9] was grown in Eagle's Minimum Essential Medium (Lonza, MD, USA) on Vero E6 cells. Prior to aerosol challenge, four rhesus macaques, two males and two females, 3-5 years old, weighing 3-5 kg each, were anesthetized by intramuscular ketamine (10-15 mg/kg) injection. Head-out plethysmography (Buxco-Data Sciences International, MN, USA) was used to calculate an average respiratory minute volume (mL/min) by multiplying the respiration rate by the tidal volume. Aerosol concentrations derived from a SKC biosampler (SKC Inc., PA, USA) were used to calculate the presented dose [10] . Within a negative-pressure (−24.9 Pa), head-only aerosol exposure chamber, macaques were exposed to a small-particle (0.5-3 μm aerodynamic diameter targeting lung alveoli) aerosol challenge (inhaled dose = log 10 -4.64 plaque-forming units). Imaging data were acquired with Gemini PET/CT clinical scanner (Philips Healthcare, Andover, MA, USA) [5, 11] . With an axial field-of-view (FOV) of 180 mm of the PET scanner, the entire NHP thorax is imaged in a single bed position. Use of the scanner's brain protocol resulted in a transverse field of view of 256 mm and led to cubic 2mm-wide voxels in the reconstructed images. Low-dose CT images of the thorax for PET attenuation purposes were acquired at 120 kVp, 3-mm slice thickness, and 1.5-mm spacing. No contrast was given, and the subjects were freely breathing during the scan. PET image acquisition was initiated immediately after the low-dose CT scans and 1 min prior to intravenous injection of [ 18 F]-FDG (9-10 MBq/kg) into the saphenous vein and continued for up to 60 min (3600 s). Nine imaging sessions per animal were conducted on pre-inoculation days −14 or −13 and −11 or −10 and post-inoculation days +1 or +2, +3 or +4, +7 or +8, +9 or +10, +15 or +16, +21 or +22, and +28 or +29 with MERS-CoV. SUV PET images were reconstructed iteratively using the manufacturer supplied 3D line-of-response (LOR)-based row-action maximum-likelihood algorithm [12] . Methods for scatter, decay, random, and attenuation correction were applied during the image reconstruction process. Both scatter and attenuation corrections [13] were based on the low-dose CT images acquired prior to the PET scans. The list mode data were sorted into 46 dynamic frames during creation of the histograms. To extract the early tracer dynamic distribution in the arterial blood, the initial data set (up to 720 s or 12 min) was comprised of 39 frames with the following time sequence: 15 frames × 2 s, 6 frames × 5 s, 5 frames × 10 s, 5 frames × 20 s, 4 frames × 40 s, and 4 frames × 120 s. This sequence was followed by 3 frames × 240 s and 4 frames × 480 s to capture the late slow phase of dynamic distribution of the tracer in both the blood and the tissues. PET images were reconstructed iteratively using 3D ordered-subset expectationmaximization algorithm with two iterations and nine subsets followed by 18 iterations of maximum a posteriori reconstruction [14] . Maximum a posteriori parameters were adjusted to provide a uniform spatial resolution of 4.8 mm (full-width half-maximum = 4.8 mm) in all three directions. Methods for scatter, decay, random, and attenuation correction were applied during the process of PET image reconstruction. Reconstructed SUV PET images were analyzed without any post-reconstruction smoothing using PMOD version 3.5 (PMOD Technologies, Zurich, CH). To extract an image-derived input function (IDIF), VOI (2-mm spheres) were placed on the left ventricles and arch of the aorta using frames over the first 6 min (360 s) after [ 18 F]-FDG injection (Fig. 1a, b) . Averaged data from two VOIs were used to generate the IDIF (Fig. 1c, d) . Two-ml spheres were placed on axillary and mediastinal LNs and lumbar spine bone marrow as described previously [6] , and 5-mm spheres were placed on right and left sides of the lungs to obtain the tissue time activity curves (TACs). The last time point of the TACs was used to generate the SUV data. Using the standard two-tissue compartment kinetic model with irreversible tracer metabolism (k 4 = 0, Fig. 1e ), the [15] . The blood volume fraction (V b ) was included in the modeling. Patlak linear regression method was applied for parameter estimation utilizing the IDIF, [ 18 F]-FDG tissue TACs [16] , and PMOD version 3.5 (PMOD Technologies). Tissue TACs were fitted to the models by use of the nonlinear least-squares method with the Levenberg-Marquardt algorithm, which minimizes the weighted sum of squared errors between PET measurement and model solutions. A plot of the ratio C tis (t)/C bl (t) against the ratio of cumulative to instantaneous blood activity concentration ("normalized time") became linear in the late phase after the tracer injection when the concentration of free (i.e., unmetabolized) [ 18 F]-FDG in the blood had equilibrated with that of free tracer in extravascular volume of distribution. This linear part of the plot was fitted by Eq. (1) to identify the K i as a slope of a regression line: in which C tis (t) and C bl (t) represent the radioactivity concentration in the region of interest and the arterial blood assessed from the PET images at different time points after an [ 18 F]-FDG injection, respectively, and V dist is an initial distribution volume. A criterion for maximum error was set to 5 % to derive the model parameter values. For the [ 18 F]-FDG model described in Fig. 1 , the slope equals K 1 × k 3 ÷ (k 2 + k 3 ). Complete blood cell counts were determined on PET-scan days [5] . Body temperature or body weight were monitored once daily or once every other day, respectively. Two-way repeated measures analysis of variance (ANOVA) with post hoc Bonferroni multiple comparison test used K i obtained pre-inoculation and post-inoculation with MERS-CoV and VOI location as independent variables to characterize the host immune response. For two-way repeated measures ANOVA, we used K i at different time points pre-inoculation with MERS-CoV and VOI location as within and between two factors, respectively. The correlations between K i values in mediastinal and axillary LNs and bone marrow and between monocyte fraction in the blood and mediastinal and axillary LNs were calculated using the Pearson product moment correlation coefficient (r). The D'Agostino and Pearson test [17] was applied to confirm that the data followed a Gaussian distribution. GraphPad Prizm 6.01 (GraphPad Software Inc., La Jolla, CA, USA) was used for all statistical analyses. Analysis of lung data revealed no pathology on CT images and no changes in [ 18 F]-FDG uptake up to day 30 after MERS-CoV inhalation (data not shown). No changes in body temperature, body weight, and blood glucose concentrations (69.9 ± 7.4 mg/dL prior to exposure and 63.9 ± 10.4 mg/dL after exposure) were observed. However, [ 18 F]-FDG uptake as indicated by SUV increases in mediastinal and axillary LNs post-inhalation (Fig. 2 , Additional file 1: Movie S1). Analysis of complete blood cell counts revealed a slight increase (within normal range) in circulating monocytes only that peaked on day 5 or 6 post-inhalation and remained elevated through the remainder of the study (Fig. 3a) . Images of the first 37 time frames comprised of 2-120 s each caught the fast kinetics of [ 18 F]-FDG distribution in the arterial blood (Fig. 1c, d) . The rest of six time frames, 4-8 min in duration, covered the slow distribution and accumulation of [ 18 F]-FDG in the tissues at later time points (16-60 min, slow phase) (Additional file 2: Movie S2, Fig. 4 ). SUV TACs for axillary LNs plateaued 15 min (900 s) after FDG injection and were similar throughout the study duration of 1.5 months (Fig. 4b) . Analogously, bone marrow SUV TACs did not show significant variation during the study. However, compared with the axillary LNs, the TACs for bone marrow continued to rise at 60 min (3,600 s) after [ 18 F]-FDG injection (Fig. 4b, c) suggesting a longer time after [ 18 F]-FDG injection for the tissue with high cell glycolytic activity to reach a steady state. In mediastinal LNs, TACs rise was pronounced on days 5-9 post-inhalation only, as indicated by TACs (Fig. 4a) . Representative Patlak plots for bone marrow and mediastinal and axillary LNs are shown in Fig. 5 . K i obtained 2 days prior to virus exposure remained unchanged for each lymphoid tissue (Fig. 3a, b) . The mean baseline K i prior to MERS-CoV exposure was similar in axillary and mediastinal LNs (0.0062 ± 0.002 SD and 0.008 ± 0.004 SD, respectively). Greater elevation in mean K i values (up to almost six-fold increase from pre-exposure scan) in mediastinal LNs was observed within the first week after MERS-CoV exposure compared to mean K i values (