Chicken LMH (ATCC^® CRL-2117™) were obtained from the American Type Culture Collection (Manassas, VA, United States) and cultured as described in a previous study (Niu et al., 2018a). The FAdV-4 strain (SDDM-4/15) used in the study was prepared as described previously (Niu et al., 2016). Self-prepared rabbit polyclonal antibodies against the FAdV-4 hexon protein were used for indirect immunofluorescence (Niu et al., 2018a).

LMH cells at 80% confluence were seeded in the six-well culture plates and infected with FAdV-4 at a multiplicity of infection (MOI) of 1, while mock-infected cells were treated with an equal volume of Dulbecco’s modified Eagle’s medium (DMEM). After 2 h, the inoculum was aspirated and the cells were washed twice with phosphate-buffered saline (PBS). Subsequently, the cells were collected at 12, 24, 36, and 48 h post-infection (hpi). A virus growth curve was constructed based on the data acquired by quantitative real-time polymerase chain reaction (qRT-PCR).

At 24 h before FAdV-4 infection, ∼8.0 × 10⁶ LMH cells were seeded in 100-mm culture plates and cultured to 80–90% confluence. Twelve plates were prepared, six of which were infected with FAdV-4 at an MOI of 1, while the others were used as the mock group. All cells (about 1.2 × 10⁷) were harvested at 24 hpi. Briefly, after the medium was discarded, the cells were washed twice with cold PBS followed by cold 0.9% sodium chloride solution, then quenched with 1 mL of methanol:acetonitrile: water (2:2:1, v/v), and stored at −80°C until metabolomics analysis.

After thawing, the cell suspensions were sonicated in an ice bath for 30 min and cooled at −20°C for 10 min before centrifugation at 14,000 × g for 20 min at 4°C. Then, the supernatant was evaporated under a stream of nitrogen gas in a vacuum concentrator. Prior to LC-MS/MS analysis, 100 μL of acetonitrile:water solution (1:1, v/v) was added to each tube for resolution and the tubes were swirled and centrifuged at 14,000 × g at 4°C for 15 min.

The samples were assayed using an Agilent 1290 Infinity UHPLC system (Agilent Technologies, Inc., Santa Clara, CA, United States) and a Triple TOF 6600 mass spectrometer (AB SCIEX, Concord, ON, Canada). Electrospray ionization (ESI) in positive and negative ion modes was used to detect the samples with the following parameters: atomization auxiliary heating gas 1, 60 Psi; auxiliary heating gas 2, 60 Psi; curtain gas, 30 Psi; ion source temperature, 600°C; spray voltage, ± 5,500 V (positive and negative modes); first-grade mass charge ratio detection range, 60–1,000 Da; second grade ion mass charge ratio detection range, 25–1,000 Da; first grade mass spectrum scanning accumulation time, 0.20 s/spectra; and second grade mass spectrum scanning accumulation time, 0.05 s/spectra. The secondary mass spectrum was obtained in data-dependent acquisition mode with a dynamic exclusion of isotopic ion range of 4 Da with scanning and collecting of 10 debris at a time.

The raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert software before importing into freely available XCMS software. For peak picking, the following parameters were used: centWave m/z, 25 ppm; peakwidth, c (10, 60); and prefilter, c (10, 100). For peak grouping, the following parameters were used: bw, 5; mzwid, 0.025; and minfrac, 0.5. CAMERA (Collection of Algorithms of MEtabolite pRofile Annotation) was used for annotation of isotopes and adducts. In the extracted ion features, only variables with more than 50% of non-zero measurement values in at least one group were retained. Compound identification of metabolites was performed by comparing the accuracies of the m/z values (<25 ppm) and MS/MS spectra with an in-house database established with available authentic standards.

After sum-normalization, the processed data were subjected to multivariate data analysis with the R package “ropls,” including pareto-scaled principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS-DA). Sevenfold cross-validation and response permutation testing was used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the PLS-DA and OPLS-DA models was calculated to indicate potential contribution to the classification. The quality of the models is described by the R²X or R²Y and Q² values. The Student’s t-test was applied to determine the significance of differences between two groups of independent samples. VIP > 1 and a probability (p) value < 0.05 were used to screen for significant changes in metabolites. Pearson’s correlation analysis was performed to identify potential correlations between two variables.

To investigate the impact of host cell metabolism of glucose and glutamine on FAdV-4 replication, LMH cells were cultured in DMEM without glucose or glutamine. As drug treatment samples, FAdV-4-infected LMH cells were treated with 10 or 40 μM 2-deoxy-D-glucose (2dGlc) (a commonly used inhibitor of glycolysis), 1 or 10 μM CB-839, or dimethyl sulfoxide (DMSO) for 48 h. Cells in the DMSO group were infected with FAdV-4 and cultured in DMEM supplemented with glucose and glutamine. Cells were lysed for western blot analysis.

FAdV-4 is known to cause cytopathologic effects in LMH cells. Firstly, the replication characteristics of the virus in LMH cells were determined by qRT-PCR. As shown in Figure 1A, virus titers in LMH cells reached 10⁴ at 24 hpi. Indirect immunofluorescence showed that about 50% of the cells were infected with FAdV-4 at 24 hpi (Figure 1B), and hematoxylin and eosin staining confirmed the formation of viral inclusion bodies (Figure 1C). Combined with previous research results (Niu et al., 2018a), FAdV-4 could cause significant apoptosis, autophagy, and a severe inflammatory response in LMH cells at 24 hpi. Therefore, mock and FAdV-4-infected LMH cells were prepared at 24 hpi for early metabolomics analysis.

The metabolomic profiles of LMH cells infected with FAdV-4 were characterized using LC-MS/MS techniques, and PCA, PLS-DA, and OPS-DA were applied to the data. Analysis of data quality, including total ion chromatogram, PCA, Pearson’s correlation, Hotelling’s t² statistic, multivariate control chart, and relative standard deviation, indicated that the quality of data was acceptable for the following metabolomics analysis. PCA score plots revealed clear separation between the control and FAdV-4-infected groups, under both positive and negative modes (Figures 2A,B). The LC-MS/MS data were further subjected to PLS-DA and OPLS-DA. The quality of the PLS-DA and OPLS-DA models was evaluated based on the parameters R²Y and Q², which are measures of fitness and prediction ability, respectively. Generally, Q² is greater than 0.5, indicating that the model is stable and reliable, where 0.3 < Q2 ≤ 0.5 indicates that the model is stable and Q2 < 0.3 indicates low reliability of the model. PLS-DA and OPLA-DA score plots revealed excellent separation of the control and FAdV-4-infected groups (Figures 2C–F).

OPLS-DA based on a VIP > 1 in and P < 0.05 (Student’s t-test) revealed significant differences in metabolite screening criteria. The metabolites and associated fold changes in response to FAdV-4 infection of LMH cells are shown in Figure 3 and Tables 1, 2. The differential metabolites, which included 49 amino acids and related derivatives, of LMH cells caused by FAdV-4 infection, included 78 that were increased and five that were decreased in positive ion mode, while 34 were increased and four were decreased in negative ion mode.

Differential metabolites were further analyzed to explore the effects of FAdV-4 infection on the metabolic pathways of LMH cells. The relationships among the differential metabolites were investigated using heatmap analysis (Figure 4) and Pearson’s correlation (Figure 5). The results indicated that metabolites in the same or related metabolic pathways were closely connected. When the significance level of metabolite enrichment in each pathway was analyzed and calculated with the Fisher’s exact test, 60 pathways were potentially changed in LMH cells infected with FAdV-4 (Table 3). The top 20 metabolic pathways with the highest significance were selected based on P values and presented in the form of a histogram (Figure 6). The top 20 metabolic pathways mainly involved pyrimidine metabolism, purine metabolism, amino acid metabolism, carbohydrate metabolism, glutathione metabolism, and cofactor and vitamin metabolism, in addition to some signal transduction pathways. Next, some metabolites with significant differences were further screened to establish connections among the related metabolic pathways (Figure 7). The results showed that the tricarboxylic acid (TCA) cycle, purine metabolism, pyrimidine metabolism, alanine, aspartate, and glutamate metabolism, amino sugar and nucleotide sugar metabolism, and glycolysis were significantly up-regulated and closely connected.

As shown in Figure 8, treatment of LMH cells with either glutamine withdrawal or 1 or 10 μM CB-839 significantly reduced FAdV-4 replication and lowered viral yields relative to DMSO treatment in the presence of 4 mM glutamine. Moreover, the inhibitory effect of CB-839 on FAdV-4 was dose-dependent. FAdV-4 production was significantly reduced in LMH cells cultured in glucose-deficient medium. Treatment of FAdV-4-infected cells with 20 or 40 μM 2dGlc also effectively impaired the production of infectious particles.