Abstract

   Viral infections induce major shifts in cellular metabolism elicited by
   active viral replication and antiviral responses. For the virus,
   harnessing cellular metabolism and evading changes that limit
   replication are essential for productive viral replication. In
   contrast, the cellular response to infection disrupts metabolic
   pathways to prevent viral replication and promote an antiviral state in
   the host cell and neighboring bystander cells. This competition between
   the virus and cell results in measurable shifts in cellular metabolism
   that differ depending on the virus, cell type, and extracellular
   environment. The resulting metabolic shifts can be observed and
   analyzed using global metabolic profiling techniques to identify
   pathways that are critical for either viral replication or cellular
   defense. SARS-CoV-2 is a respiratory virus that can exhibit broad
   tissue tropism and diverse, yet inconsistent, symptomatology. While the
   factors that determine the presentation and severity of SARS-CoV-2
   infection remain unclear, metabolic syndromes are associated with more
   severe manifestations of SARS-CoV-2 disease. Despite these observations
   a critical knowledge gap remains between cellular metabolic responses
   and SARS-CoV-2 infection. Using a well-established untargeted
   metabolomics analysis workflow, we compared SARS-CoV-2 infection of
   human lung carcinoma cells. We identified significant changes in
   metabolic pathways that correlate with either productive or
   non-productive viral infection. This information is critical for
   characterizing the factors that contribute to SARS-CoV-2 replication
   that could be targeted for therapeutic interventions to limit viral
   disease.

Introduction

   As obligate intracellular parasites, viruses co-opt host cellular
   materials, machinery, and metabolism to facilitate viral replication
   [[39]1,[40]2]. Metabolic changes in response to virus infection result
   in extensive alterations to cellular physiology and often mirror
   changes seen in cancer cells [[41]2–[42]7]. Both RNA and DNA viruses
   reprogram different aspects of host metabolism including increased
   glycolysis, elevated pentose phosphate activity, amino acid generation
   and lipid synthesis [[43]1]. Viral hijacking of host metabolism and
   subversion of metabolic defenses can lead to increased viral
   replication and host damage, resulting in long-term health
   consequences, such as those seen in severe cases of COVID-19, following
   infection with the novel coronavirus, SARS-CoV-2. This is supported by
   analysis of SARS-CoV-2 positive patient serum that has shown acute and
   long-term changes in metabolites and further metabolic disorder
   [[44]8–[45]12].

   To better understand what metabolic changes occur during SARS-CoV-2
   infection and how this may relate to severe disease outcomes, we
   implemented global metabolomic profiling to analyze thousands of
   metabolites using LC-MS to detect disease-associated changes to the
   cellular environment [[46]13,[47]14]. Metabolites serve as
   intermediates for cellular physiology and include hormones,
   oligonucleotides, peptides, and other molecular products of cellular
   biochemical reactions that represent the current physiological state of
   a cell [[48]15]. Global metabolomic profiling can therefore provide an
   unbiased view of metabolic shifts induced during and in response to
   viral infection [[49]16,[50]17].

   To elucidate changes to cellular metabolism associated with SARS-CoV-2
   viral replication and those changes associated with virus exposure we
   infected and profiled A549 cells, a human lung cell line. A549 cells
   are frequently used to evaluate viral infection for many respiratory
   viruses but are not intrinsically susceptible to SARS-CoV-2 infection,
   as they lack endogenous expression of the viral receptor, ACE2
   [[51]18–[52]20]. However, expression of human ACE2 protein on A549
   (ACE2-A549) cells renders them fully susceptible to SARS-CoV-2
   [[53]21]. By comparing A549 and ACE2-A549 cells inoculated with
   SARS-CoV-2 we can identify and separate metabolic shifts induced by
   active viral replication from those induced by the host cells response
   to virus exposure.

   Here, we describe distinct metabolic changes in to both ACE2-A549 and
   A549 cells triggered by SARS-CoV-2 exposure. Amino acid metabolism,
   glutathione, and urea cycle metabolic pathways were significantly
   altered in cells that support productive SARS-CoV-2 infection
   (ACE2-A549 cells). In contrast A549 cells that are not susceptible to
   infection but were exposed to a high inoculating dose of SARS-CoV-2 had
   significant changes in fatty acid anabolic and catabolic pathways as
   well as leukotriene metabolism. These results mirror the metabolic
   shifts found in serum from patients suffering from severe COVID-19
   [[54]10,[55]11,[56]17,[57]22–[58]27]. Thus, our findings point to
   metabolite features associated with both active infection and exposure
   to virus. Understanding how cellular metabolism is reprogrammed
   following SARS-CoV-2 infection will allow identification of factors
   responsible for severe disease and aid in the development of antiviral
   therapies.

Materials and methods

Cells and viruses

   E6-Vero, A549, ACE2-A549 cells. E6 Vero cells were obtained from ATCC
   (Manassas, VA) and grown in DMEM supplemented with 10% FBS, 1%
   pen-strep. A549 cells were the obtained from Chang Lab. ACE2-A549 cells
   were obtained from BEI Resources (NR-53821). A549 cells were propagated
   in Hams F-12 (Corning) media supplemented with 10% fetal bovine serum
   (HyClone) and 1X Penicillin/Streptomycin (Fisher Scientific). ACE2-A549
   cells were supplemented with 100ug/mL Blasticidin (Gibco). SARS-CoV-2
   strain WA01 was obtained from BEI Resources (NR-52281). Viral stocks
   were propagated and titered on E6 Vero cells in DMEM supplemented with
   2% FBS and 1% pen-strep. Viral stocks were made by collecting media
   from infected cell cultures showing extensive cytopathic effect and
   centrifuged 1,000 RCF for 5 minutes to remove cellular debris. The
   clarified viral supernatant was then used for all experimental
   infection. For determination of viral infectivity by plaque assay, E6
   Vero cells were cultured then incubated with viral inoculum at limiting
   dilutions. Dilutions employed resulted in a minimum threshold of
   detection at 500 plaque forming units (PFU)/mL. Following inoculation,
   cells were over-layered with 1% methylcellulose, DMEM supplemented with
   2% FBS and 1% pen-strep and incubated for 3-4 days [[59]28,[60]29].
   Cells were then fixed and stained with 0.5% methylene blue/70% ethanol
   solution. Plaques were counted and the overall titer was calculated.

Western blot detection

   Both A549 or ACE2-A549 cells were cultured and collected with PBS
   supplemented with 0.5 mM EDTA. Cells were pelleted by centrifugation
   then resuspended in protein extraction buffer [10 mM Tris–HCl (pH 7.5),
   10 mM NaCl, 1.5 mM MgCl2, 1% NP40] supplemented with Mini-complete,
   EDTA Free Protease Inhibitor Cocktail (Roche Applied Science,
   Indianapolis, ID). Protein concentrations determined by BCA assay, and
   then 20 µg were loaded and separated on a 10% polyacrylamide gel.
   Subsequently, proteins were transferred to PVDF membranes and incubated
   with a goat anti-human ACE2 mAb (R&D Systems Cat # AF933) and secondary
   rabbit-anti goat AF488-conjugated mAb (ThermoFisher, Cat # A27012).
   After detection, the blot was stripped and re-probed with mouse
   anti-beta Actin and a goat anti-mouse DyLight550 secondary
   (ThermoFisher, Cat # 84540). Membranes were imaged on a Cytiva Amersham
   Typhoon scanner (Bucks, UK) using the Cy2 and Cy3 channels.

Immunofluorescence detection of infection

   Prior to infection, cells were seeded at 2 x 10^4 per well of an
   eight-chamber coverslip (Labtek Cat. No. 155411, Nunc International,
   Rochester, NY). At indicated times post infection, cells were then
   fixed with 4% paraformaldehyde in PBS for 30 minutes, washed thoroughly
   with PBS, and blocked in 2% bovine serum albumin (BSA) prior to
   antibody incubations. Primary and secondary antibodies were diluted in
   a PBS supplemented with 0.5% saponin, 0.125% BSA as described [[61]30],
   and incubated for one hour at room temperature. Primary mouse
   anti-nucleocapsid (Thermofisher, Cat # MA1-7403) was diluted 1:500,
   followed by goat anti-mouse IgG labeled with Dylight 550 ThermoFisher,
   Cat # 84540) at 1:500. DNA was counterstained with Hoescht 3342 at
   1:5000 dilution. Actin filaments stained with phalloidin-488
   (Thermofisher, Cat # A12379) at 1:500. Stained cells were imaged on a
   Nikon Ti-Eclipse inverted epifluorescent microscope (Nikon Instruments,
   Melville, NY) equipped with an iXon 896 EM-CCD (Andor Technology Ltd.,
   Belfast, Northern Ireland) camera. Fluorescence detection used a
   SpectraX LED light engine (Lumencor, Beaverton, OR) with paired
   excitation filters, dichroic mirrors, and emission filters (Prior
   Scientific, Rockland, MA). Images were acquired with either Plan Fluor
   20 phase contrast (Ph) air objective or CFI Plan Apochromat Lambda 60x
   Oil immersion objective. All imaging experiments were performed a
   minimum of two times.

Metabolite extractions

   ACE2-A549 and A549 cells were cultured in 6-well plates to
   approximately 90% confluency. Cells were then inoculated with
   SARS-CoV-2 for one hour, washed with PBS, then fed with fresh media.
   Cells were harvested at 0-, 6-, and 16-hours post-inoculum removal with
   6 replicate wells harvested separately at each time point. At each
   collection, cells were washed with PBS, suspended with trypsin-EDTA for
   5 minutes, collected and centrifuged for 5 minutes. Trypsin-EDTA was
   removed, and cell pellets were washed with an equi-volume of PBS before
   repeated centrifugation. PBS was removed and cells were resuspended in
   100% methanol. Samples were vortexed in 10 x 1 sec bursts before being
   placed in −80 °C freezer. Vortexing and freezing was repeated 3 times
   to maximize macromolecule precipitation. Subsequently, methanol
   extracts were subjected to centrifugation at 8,000 rcf for 10 minutes
   to pellet cell debris and precipitate proteins. The supernatant
   containing the metabolites was transferred to a separate tube and dried
   by vacuum concentration to remove solvents. Dried metabolites were
   resuspended in 100 μL mass spectrometry grade 50:50 (v/v) water:
   acetonitrile solution immediately prior to high performance liquid
   chromatography-mass spectrometry (HPLC-MS) analysis.

Untargeted metabolomic analysis

   Extracted metabolites were analyzed using HPLC-MS (Agilient 6538 Q-TOF
   mass spectrometer) in positive mode (resolution: ~ 20 ppm, accuracy:
   ~ 5 ppm, possible ionization adducts: H^ +, Na^+) using a Cogent
   Diamond Hydride HILIC column (150 x 2.1 mm). LC-MS data, consisting of
   mass-to-charge (m/z) values and their peak intensities, were processed
   and exported using MSConvert and XCMS (S1 Table). All data was log
   transformed and autoscaled prior to analysis using MetaboAnalyst
   [[62]31–[63]33].

   Statistical analyses performed included hierarchical cluster analysis
   (HCA), principal component analysis (PCA), partial least-squares
   discriminant analysis (PLS-DA), variable importance in projection (VIP)
   scores, volcano plot, fold change, and heatmap analysis. Pathway
   analysis was performed to map differentially expressed metabolite
   features to biological pathways using the Functional Analysis function
   in MetaboAnalyst (pathway library: KEGG, mass tolerance: 5 ppm,
   positive mode) [[64]31,[65]32]. Pathway significance was determined
   using FDR-corrected significance levels of 0.05.

   For metabolomic data and downstream pathway analyses, there were 35
   samples total (6 samples per timepoint for each cell line except for
   only 5 samples for the t6 timepoint in ACE2 cells). In total, 1,085
   metabolite features were co-detected across all samples. To examine
   differences in regulation patterns across timepoints, ANOVA analysis
   was performed. The results of this analysis are that 152 and 372
   metabolite features were differentially regulated across timepoints in
   ACE2-A549 cells and A549 cells alone, respectively. From here, we took
   these differentially-regulated features and performed pathway
   enrichment analyses. Thus 152 features were used for ACE2-A549 pathways
   and 372 features for A549 pathways. For features that are
   differentially regulated across timepoints of ACE2-A549 cells, 13
   pathways were identified. Conversely, 8 pathways were differentially
   regulated across timepoints of A549 cells.

Results

Differing susceptibility and productivity of A549 cells lines for SARS-CoV-2

   To study metabolic shifts during SARS-CoV-2 infection, we employed A549
   cells, a human lung carcinoma cell line. A549 cells are not susceptible
   to SARS-CoV2 infection and must be modified to express human ACE2 to
   allow for entry and replication [[66]18–[67]21]. To confirm human ACE2
   expression, cell extracts from A549 cells engineered to express ACE2
   (ACE2-A549) and the progenitor A549 cells were subjected to western
   blot detection ([68]Fig 1A).

Fig 1. Infection of different A549 cells with SARS-CoV-2.

   [69]Fig 1
   [70]Open in a new tab

   (A) Extracts from ACE2 expressing or parental A549 cells (20 µg protein
   per lane) were separated by SDS-PAGE and transferred for western
   blotting against ACE2 and beta-actin, respectively. (B) ACE2-A549 and
   A549 cells were infected at MOI 10 with SARS-CoV-2/WA01. Parallel
   infections were fixed at 6 and 16 hpi and stained with SARS-CoV-2
   anti-nucleocapsid antibody (white) with Dapi (blue, nuclei). Scale bar
   is 100 μm. (C) Co-staining of the SARS-CoV-2 anti nucleocapsid antibody
   (red) with phalloidin (green) with Dapi (blue, nuclei) in ACE2-A549
   cells at 16 hpi at MOI of 10. (D) Titers from ACE2-A549 and A549 cells
   infected with SARS-CoV-2 at an MOI 10. Both cell extracts (Total) and
   supernatants (Extracellular) were collected and titered at 0, 6, and 16
   hpi. All data represented as the mean ± SD of three independently
   infected samples.

   To evaluate the capacity to support SARS-CoV-2 infection, ACE2-A549 and
   A549 cells were infected and analyzed to explore differences in
   susceptibility and relative timing of viral replication. Our analysis
   focused on 6 hpi, a timepoint suggested to be prior to the onset of
   progeny virus production, and 16 hpi, when productive viral replication
   should be close to its peak [[71]34]. Infections were performed at an
   MOI of 10 to ensure homogenous infection and exposure of all cells to
   sufficient infectious inoculum. The extent of viral infection and
   replication was analyzed using indirect immunofluorescent detection of
   the SARS-CoV-2 nucleocapsid (N) protein ([72]Fig 1B). ACE2-A549 cells
   displayed extensive N protein expression at both 6 and 16 hpi. In
   contrast, unmodified A549 cells displayed no N protein staining,
   indicating a complete lack of infection and replication following
   SARS-CoV-2 inoculation. An important element to analyzing metabolic
   profiles is the relative “health” of the cell, especially at later time
   points in the viral lifecycle. To evaluate whether 16 hpi exhibits
   extensive cell deterioration, we analyzed the distribution of actin
   filaments in infected cells. Cells were counterstained with both
   SARS-CoV-2 nucleocapsid (N) protein and phalloidin to image for the
   presence of actin filament assemblies during infection ([73]Fig 1C). We
   observed a distribution of cellular morphologies, but many cells retain
   actin filament assemblies and adhesions to the cell surface similar to
   the A549 cells that do not support productive replication (S1 Fig). The
   distribution of cellular morphology and actin staining suggests that
   the cells are not undergoing extensive cytopathic effect by 16 hpi.

   To understand how the selected timepoints correlate with viral
   replication, we analyzed viral titers from supernatant with (total) and
   without (extracellular) cellular fractions, ([74]Fig 1D). We compared
   the detection of plaque forming units (PFU) from ACE2-A549 and A549
   cells at all three timepoints. In the ACE2-A549 cells, viral titer does
   not increase until 16 hours post inoculation in both the total and
   extracellular samples. A decrease at 6 hpi in the extracellular samples
   reflects the uptake of viral inoculum. The subsequent increase in titer
   at 16 hpi correlates with the release of virus. For A549 cells there
   was no increase in viral titer in either the total or extracellular
   samples beyond what is detected after inoculation of cells. These
   observations are consistent with the reported lack of susceptibility
   and permissiveness of A549 cells to SARS-CoV-2 [[75]19,[76]20]. The
   differences in infection, replication, and release of infectious virus
   supports our selection in timepoints to explore metabolic changes in
   cells that can support robust productive virus infection and in cells
   that do not allow viral entry.

Experimental design to assess metabolic differences following SARS-CoV-2
infection

   Global metabolomic profiling was performed in cells infected with or
   exposed to SARS-CoV-2. Our analysis focuses on three critical
   timepoints: 0 hours post infection (hpi), or immediately after inoculum
   removal, 6 hpi and 16 hpi [[77]34]. In our experimental approach,
   ACE2-A549 and A549 cells were inoculated at an MOI of 10 with
   SARS-CoV-2 to ensure homogeneity across the population of cells
   critical for the subsequent extraction and detection of metabolites.
   The cells were collected and processed at 0, 6, and 16 hpi, to analyze
   temporal changes in the metabolic landscape over the course of the
   viral lifecycle ([78]Fig 2A). Metabolites were extracted and processed
   for LC-MS metabolite detection ([79]Fig 2B). Samples were analyzed via
   LC-MS to identify molecules smaller than ~ 1000 Da, which can include
   hormones, oligonucleotides, peptides, and other molecular products of
   cellular biochemical reactions [[80]31,[81]32]. A total of 1085
   metabolites were detected across all samples and were included in all
   analyses. Data analyses were performed using MetaboAnalyst allowing for
   quantification of untargeted metabolites and identification of changes
   in the metabolomic phenotypes at each of the different time points
   ([82]Fig 2C) [[83]31–[84]33].

Fig 2. Metabolic profiling analysis pipeline for infected cells.

   [85]Fig 2
   [86]Open in a new tab

   (A) ACE2-A549 and A549 cells were infected with SARS-CoV-2 Isolate
   USA-WA1/2020 at an MOI of 10. Metabolites were extracted at multiple
   time points post infection. (B) Metabolic profiles are generated
   following LC-MS detection in the Mass Spectrometry Core Facility. (C)
   Data processing and analysis allows for both global metabolite
   profiling and pathway enrichment analysis. Image generated with
   Biorender.

Metabolic profiling of ACE2-A549 cells during SARS-CoV-2 infection

   We began by analyzing metabolic changes in ACE2-A549 cells that support
   productive SARS-CoV-2 infection. Changes in the global metabolomic
   profiles of infected ACE2-A549 cells were determined using unsupervised
   PCA and supervised PLS-DA ([87]Fig 3A and [88]B). From these analyses,
   the variance between each time point was greater than the variance
   between replicates within each group. We observed that the first 2
   PLS-DA components represented 42% of the overall variance, further
   demonstrating that the three time points are distinct from each other.
   This is much greater than the expected 0.03% variance that would be
   expected from a uniformly random distribution of metabolites. Taken
   together, our data suggests that greater metabolomic changes occur over
   time although some overlap between samples is observed. Both analyses
   confirm that clear, non-random, differences exist between productively
   infected ACE2-A549 cells harvested at different each time points.

Fig 3. Global metabolite profiling of SARS-CoV-2 infected ACE2-A549 cells.

   [89]Fig 3
   [90]Open in a new tab

   A total of 1085 metabolites were analyzed by (A) principal component
   analysis (PCA) and (B) supervised partial least-squares discriminant
   analysis (PLSDA). (C) Heatmap analysis of significant metabolites
   (n = 152) reveals temporal changes in metabolite phenotypes from 0 to
   16 hpi. Mean intensities of each metabolite were clustered into 3
   groups: 0 hpi (n = 6), 6 hpi (n = 5), and 16 hpi (n = 6). Normalized
   fold change of specific metabolites is relative to peak concentration
   across 0, 6, and 16 hpi.

   To further examine metabolomic patterns that significantly change
   during SARS-CoV-2 infection in ACE2-A549 cells, we performed ANOVA to
   assess changes in metabolomic between cells harvested at 0, 6, and 16
   hpi. From this analysis, 152 metabolite features with an FDR-corrected
   p-value < 0.05 were differentially regulated between timepoint groups.
   Heatmap analysis of these ANOVA metabolite features revealed temporal
   changes in metabolite phenotypes from 0 to 16 hpi ([91]Fig 3C). The
   variance between samples can also be observed when each metabolite
   feature is plotted for the individual samples (S2 Fig). Clustering
   analysis of similarly altered metabolite feature produces 4 main
   classes: reduced at 16 hpi (class 1), increased at 16 hpi (class 2),
   reduced at 6 hpi (class 3), and increased at 6 hpi (class 4). The
   majority of altered metabolite features, belonging to class 1, had the
   highest abundance at 0 hpi and progressively decreased from 6 to 16
   hpi, suggesting a trajectory of depletion during the course of
   SARS-CoV-2 replication. In contrast, class 2 metabolite features
   increased in abundance from 0 to 16 hpi. For classes 3 and 4,
   metabolite features with detected changes at 6 hpi often returned to
   baseline abundance by 16 hpi.

   To derive additional biological relevance, the 152 metabolite features
   distinguished by ANOVA were then manually searched by m/z value in
   METLIN to make putative metabolite identifications [[92]35]. Identified
   putative metabolites were found in the four main classes ([93]Fig 3C).
   Select metabolites are presented to highlight some of the metabolic
   changes detected during infection ([94]Fig 4). Each boxplot depicts the
   normalized fold-change of the putative metabolite description for each
   class with average values represented as yellow diamonds and individual
   replicates within each time point represented as black spots. These
   metabolite features may be associated with some flux or alteration in
   utilization of intermediates in metabolic pathways.

Fig 4. Putative metabolites identified from each of the four classes.

   [95]Fig 4
   [96]Open in a new tab

   The normalized fold change value for 4 separate metabolites are
   presented within box plots. Individual replicates within each time
   point are represented as black spots. Average value is the yellow
   diamond. Each plot is labeled for the individual metabolite(s) based on
   m/z value identification.

Distinct metabolic phenotypes of A549 cells during SARS-CoV-2 infection

   We next sought to separate metabolic changes identified during
   productive infection from changes that may result from responses due to
   virus exposure. To accomplish this, we analyzed A549 cells, which do
   not express ACE2 and are refractory to infection at 0, 6 and 16 hpi
   after exposure to SARS-CoV-2 inoculum [[97]19,[98]20]. As before, we
   evaluated metabolomic phenotypes using PCA and PLS-DA and observed
   larger ellipses that somewhat overlap. PLS-DA components 1 and 2
   represented 43.2% of the overall variance in the dataset. Similar to
   the ACE2-A549 cells, these findings suggest that metabolomic
   differences between A549 cells harvested at different timepoints exist,
   despite no detectable infection or viral replication. ([99]Fig 5A and
   [100]B). From the inoculated A549 cells, we identified 377 metabolite
   features that had an ANOVA FDR-corrected p-value < 0.05. To further
   visualize metabolic dysregulation across 0, 6, and 16 hpi, heatmap
   analysis was performed ([101]Fig 5C). The distribution and consistency
   of detected metabolite features across the replicate samples can be
   seen in the expanded heatmap (S3 Fig). Unlike with ACE2-A549 cells, the
   majority of metabolic changes are increasing quantities of metabolite
   features that peak at 6 hpi and remain elevated through 16 hpi
   ([102]Fig 5C). The second prominent class of metabolite features
   exhibits a transient increase in detection at 6 hpi, followed by
   reduced detection at or near levels seen at 0 hpi. The remaining
   significantly changed metabolite features vary with peak detection seen
   either at 0 or 16 hpi. Overall, the majority of changes in these cells
   likely represent changes in response to the inoculum that decrease by
   the later time points after inoculation.

Fig 5. Global metabolite profiling of SARS-CoV-2 inoculated A549 cells.

   [103]Fig 5
   [104]Open in a new tab

   Similar to ACE2-A549 cells, metabolite profiles from A549 cells were
   compared by (A) PCA and (B) PLSDA. (C) Heatmap analysis of significant
   metabolites (n = 377) reveals a different temporal phenotype in cells
   that are exposed to SARS-CoV-2 but remain uninfected. Mean intensities
   of each metabolite were clustered into 3 groups: 0 hpi (n = 6), 6 hpi
   (n = 6), and 16 hpi (n = 6). Normalized fold change of specific
   metabolites is relative to peak concentration across 0, 6, and 16 hpi.

Comparison of metabolites between ACE2 and A549 cells

   We sought to further understand the differing metabolic responses
   between A549 cells exposed to SARS-CoV-2 and ACE2-A549 cells infected
   with SARS-CoV-2. Statistically significant features distinguished by
   ANOVA analyses for both comparisons were investigated to identify
   metabolite features that were either shared or unique between the two
   cell types. Of the 152 significant metabolite features from ACE2-A549
   cells and 377 significant metabolite features from A549 cells, only 47
   were significantly changed following SARS-CoV-2 exposure in both cell
   types ([105]Fig 6A). In addition, pathway analysis of metabolite
   features that are dysregulated during SARS-CoV-2 replication was
   performed ([106]Fig 6B).

Fig 6. A549 cells have quantifiably different metabolic responses to
SARS-CoV-2.

   [107]Fig 6
   [108]Open in a new tab

   (A) Significant metabolites (FC > 1.5) from ACE2-A549 and A549 cells
   were compared to identify similar and different metabolites that change
   in response to SARS-CoV-2. (B) Pathway analysis on significant
   metabolites from ACE2-A549 and A549 cells. (C) Evaluating metabolic
   pathway changes relative to energy generation. Amino acids that were
   downregulated in ACE2-A549 are shown within the pathway highlighted in
   purple. Fatty acid metabolism was upregulated in A549 cells exposed to
   SARS-CoV-2, highlighted separately in blue. Both pathways directly or
   indirectly connect with components of the TCA cycle and glycolysis.

   In total, 13 metabolic pathways were altered in ACE2-A549 cells with
   productive SARS-CoV-2 replication. A majority, 10 pathways, were
   involved in amino acid metabolism including alanine, aspartate,
   cysteine, glutamate, glycine, histidine, lysine, methionine, and
   threonine ([109]Table 1). Additional metabolic pathways detected
   included glycerophospholipid metabolism, C5-branched dibasic acid
   metabolism, and ascorbate metabolism. Identifying if these altered
   pathways are caused by productive viral replication cannot be assessed
   from this data alone. We compared pathway changes in cells that cannot
   be productively infected by SARS-CoV-2. Performing pathway analysis on
   the 377 metabolite features distinguished by ANOVA from the A549 cells
   inoculated with SARS-CoV-2 identified 8 distinct pathways. Curiously,
   none of these pathways overlap with those identified in from ACE2-A549
   analysis ([110]Fig 6B). The 8 pathways unique to A549 cells mapped
   exclusively to lipid metabolism and included: fatty acid oxidation,
   activation, and metabolism; di-unsaturated fatty acid beta-oxidation,
   de novo fatty acid biosynthesis, omega-3 fatty acid metabolism, and
   carnitine shuttle ([111]Table 2). Interestingly, metabolite features
   associated with immunomodulatory leukotrienes were also detected.
   Overall, these extensive changes to lipids, specifically fatty acids,
   suggest a change not only towards an inflammatory state, but also a
   shift in energy source by the cells following exposure to a
   non-productive infection.

Table 1. Altered metabolic pathways during SARS-CoV-2 infection of ACE2-A549
cells.

   ACE2-A549 Pathways Pathway total Hits Total Hits Sig Gamma
   Value Cpd.Hits
   Glutamate metabolism 15 5 5 0.00558 L-Glutamic acid | Oxoglutaric acid
   | Gamma-Aminobutyric acid | Glutathione | Succinic acid semialdehyde
   Glutathione Metabolism 19 4 4 0.009278 Pyroglutamic acid | L-Glutamic
   acid | Glutathione | L-Cysteine
   Tryptophan metabolism 94 9 7 0.009861 L-Glutamic acid | Oxoglutaric
   acid | Formyl-5-hydroxykynurenamine | 2-Aminobenzoic acid | CE2095 |
   L-Kynurenine | Glutathione
   Glycine, serine, alanine and threonine metabolism 88 8 6 0.015295
   L-Glutamic acid | Oxoglutaric acid | Dimethylglycine | Creatine |
   2-Ketobutyric acid | L-Allothreonine | L-Threonine | Glutathione
   Methionine and cysteine metabolism 94 8 6 0.015295 L-Glutamic acid |
   Oxoglutaric acid | 2-Ketobutyric acid | DL-Glutamate | L-Cysteine |
   Allocystathionine | L-Cystathionine | Glutathione
   Urea cycle/amino group metabolism 85 10 7 0.018261 L-Glutamic acid |
   Creatinine | Creatine | Argininosuccinic acid | Gamma-Aminobutyric acid
   | Oxoglutaric acid | Queuine | N4-Acetylaminobutanal
   Histidine metabolism 33 3 3 0.01834 L-Glutamic acid | Oxoglutaric acid
   | Glutathione
   Beta-Alanine metabolism 20 3 3 0.01834 L-Glutamic acid |
   Gamma-Aminobutyric acid | Oxoglutaric acid
   Alanine and Aspartate Metabolism 30 4 3 0.041655 L-Glutamic acid |
   Oxoglutaric acid | N-Acetyl-L-aspartic acid | Argininosuccinic acid
   Lysine metabolism 52 4 3 0.041655 L-Glutamic acid | Oxoglutaric acid |
   Pipecolic acid
   Glycerophospholipid metabolism 156 6 4 0.042495 Alpha-Linolenic acid |
   Glycerophosphocholine | Phosphorylcholine | Linoleic acid
   C5-Branched dibasic acid metabolism 10 2 2 0.045209 Itaconic acid |
   Mesaconic acid
   Ascorbate (Vitamin C) and Aldarate Metabolism 29 2 2 0.045209
   Glutathione | L-Erythrulose
   [112]Open in a new tab

Table 2. Altered metabolic pathways following exposure of A549 cells to
SARS-CoV-2.

   A549 Pathways Pathway total Hits Total Hits Sig Gamma
   Value Cpd.Hits
   Di-unsaturated fatty acid beta-oxidation 26 5 5 0.00594 CE2422 | CE2421
   | CE2434 | CE0849 | Linoleic acid
   Fatty acid activation 74 9 6 0.011166 Palmitoyl-CoA |
   Alpha-Linolenoyl-CoA | Alpha-Linolenic acid | Gamma-Linolenic acid |
   Linoleic acid | Heptadecanoyl-CoA
   Fatty Acid Metabolism 63 4 3 0.024423 3-Oxohexadecanoyl-CoA |
   Palmitoyl-CoA | Linoleic acid
   Fatty acid oxidation 35 4 3 0.024423 Palmitoyl-CoA |
   Alpha-Linolenoyl-CoA | Heptadecanoyl-CoA
   De novo fatty acid biosynthesis 106 7 4 0.031172 Palmitoyl-CoA |
   Alpha-Linolenic acid | Gamma-Linolenic acid | Linoleic acid
   Leukotriene metabolism 92 7 4 0.031172 20-Hydroxy-leukotriene B4 |
   CE6473 | CE6228 | CE6182 | CE6187
   Omega-3 fatty acid metabolism 39 2 2 0.034719 trans-2-Enoyl-OPC8-CoA |
   Alpha-Linolenic acid
   Carnitine shuttle 72 5 3 0.040388 Alpha-Linolenoyl-CoA | Palmitoyl-CoA
   | Heptadecanoyl-CoA
   [113]Open in a new tab

   Taken together, changes in metabolism between two cell lines over a
   16-hour period of exposure to SARS-CoV-2 demonstrate metabolomic
   differences in a range of individual metabolites and pathways.
   Specifically, amino acid related-pathways were dysregulated in
   ACE2-A540 while lipid-related pathways were dysregulated in A549 cells
   exposed to SARS-COV2 ([114]Fig 6C).

Discussion

   In this study, we sought to understand the nature of cellular metabolic
   shifts in response to SARS-CoV-2 infection. To distinguish changes
   associated with viral replication from exposure to infectious virus, we
   compared ACE2-expressing A549 cells that are susceptible and support
   productive viral replication with A549 cells that are not susceptible
   to infection. We chose time points that represent early, intermediate,
   and late stages of viral replication to evaluate the temporal changes
   in metabolites following inoculation. Our metabolic pathway analysis
   found 152 and 377 significantly changed metabolites in ACE2-A549 and
   A549 cells, respectively. Surprisingly, there was limited overlap in
   altered metabolites or altered metabolic pathways between cells
   undergoing productive infection and those exposed to infectious virus.
   Critically, we identified alterations in pathways that are potentially
   involved in either productive viral infection or in cellular anti-viral
   responses to SARS-CoV-2.

Consequences of productive viral infection

   The initial focus of our analysis was the changes to cellular
   metabolism induced by active viral replication. The ACE2-A549 cells are
   a widely used model that we observed to be both susceptible to
   SARS-CoV-2 infection and permissive for productive viral replication
   [[115]19,[116]20]. We analyzed metabolic shifts immediately after
   virion entry (0 hpi), a mid-point of viral replication prior to virion
   production (6 hpi), and a late timepoint when new virions are being
   released from infected cells (16 hpi) [[117]34]. Overall, we identified
   four different classes of metabolites based on the relative increase or
   decrease in detection between each time point. We used these changes to
   identify pathways that were altered by active SARS-CoV-2 replication.
   Most notably, we observed that the majority of altered metabolic
   pathways were associated with amino acid metabolism.

   Within the identified pathways, we identified L-Glutamic Acid as a
   major putative metabolite that was significantly reduced from 0 to 16
   hpi. A previous study demonstrated that SARS-CoV-2 infection rewires
   carbon entry into the TCA cycle [[118]17]. Mullen et al. showed that
   oxidative metabolism of glutamine through the TCA cycle was reduced
   during SARS-CoV-2 infection in favor of pyruvate utilization via
   pyruvate carboxylase [[119]17]. This shift increased levels of
   oxaloacetate and also maintain synthesis of aspartate, which is used to
   synthesize pyrimidine nucleotides [[120]36,[121]37]. Interestingly,
   both glutamate and aspartate metabolic pathways were considered
   significant in our analysis of SARS-CoV-2 infected ACE2-A549 cells.

   In addition to changes to L-glutamic acid and aspartate, the metabolic
   shifts observed in SARS-CoV-2 infected ACE2-A549 cells reflect similar
   changes observed in COVID-19 positive patient serum samples
   [[122]10,[123]23,[124]27,[125]38–[126]43]. It is notable that our cell
   culture model identified similar metabolic pathways being disrupted
   during SARS-CoV-2 infection, even at 6 hpi. This indicates that
   metabolic screening of different laboratory-based model systems may be
   able to accurately generate data on potential biomarkers for SARS-CoV-2
   and potentially other infectious diseases.

Metabolites responding to viral inoculation

   While metabolic changes due to active viral replication are important,
   not all cells within a tissue or organ system are equally susceptible
   to virus infection. Thus, we hypothesized that uninfected cells that do
   not support SARS-CoV-2 infection can respond to virus exposure with
   altered metabolism leading to further metabolic dysfunction. To test
   this hypothesis, we analyzed the metabolic profile of A549 cells that
   do not support viral entry following exposure to the same infectious
   virus inoculum as the ACE2-A549 cells. Our data confirmed that lack of
   ACE2 expression in A549 cells resulted in a complete lack of infection
   and replication following SARS-CoV-2 inoculation. While we have no
   evidence of viral replication in A549 cells, the resulting changes in
   the metabolic profile of these cells indicates that they are responding
   to the virus inoculum. Specifically, fatty acid catabolic (β-oxidation)
   and anabolic (de novo fatty acid synthesis) pathways were significantly
   altered following A549 cell exposure to infectious virus. Lipid
   dysregulation has been a hallmark of COVID-19 pathology in patients and
   a hallmark of disease severity and progression
   [[127]41,[128]44,[129]45]. Both pathways converge on acetyl-CoA, a
   critical molecule in the breakdown of fatty acids and the synthesis of
   other lipid types, such as cholesterol, which can be transformed into
   other steroids with pro and anti-inflammatory mechanisms
   [[130]46,[131]47]. This is further supported by the significant number
   of identified metabolites associated with leukotriene and omega-3 fatty
   acid metabolism within the A549 cells. Increased leukotriene production
   is connected to COVID-19 through transcriptional and metabolic studies
   from patient serum and infected monocytes [[132]48–[133]50]. Another
   significant metabolite in our profile of the A549 cells was
   palmitoyl-CoA, a major component in the synthesis of ceramide and
   sphingolipids [[134]51]. Previous studies of COVID-19 patient serum
   samples found distinct increases in sphingosine and ceramides
   [[135]52,[136]53]. These increases were distinct between patients with
   mild disease and those in intensive care [[137]53]. The data suggests
   that uninfected cells respond to SARS-CoV-2, altering metabolic
   profiles and possibly increasing the production of pro- or
   anti-inflammatory biomolecules and enzyme cofactors
   [[138]3,[139]4,[140]54]. Thus, even uninfected cells may be
   contributing to the overall pathology observed in COVID-19 patients.

Conclusion

   Cellular models for SARS-CoV-2 infection are incredibly important for
   the initial testing of interventions that directly target viral
   replication. Through our metabolomic profiling, we identified
   metabolites and metabolic profiles that are associated with both active
   viral infection and exposure to infectious virus. Our analysis
   identified a range of metabolites and metabolic pathways altered by
   productive viral replication. Further work will be required to
   understand if these metabolites promoting viral replication are
   specific for SARS-CoV-2 infection and the relationship to changes in
   primary cells and organs. While cellular metabolism is often thought to
   be manipulated by the virus for its own ends, it is also connected to
   antiviral responses [[141]1–[142]3]. Cells can produce antiviral
   metabolites or inhibit metabolic pathways to hinder viral replication
   [[143]3]. As with productive replication, further experiments that
   either promote shifts in leukotrienes or other inflammatory molecules
   will need to be performed to characterize their effects on SARS-CoV-2
   infection. It is also possible that the identified metabolite profiles
   can be developed as biomarkers of infection that could be used for
   surveillance testing or as a predictive tool for risk evaluation of
   severe disease. The similarities of our results to metabolic shifts
   observed in patients suggest a potential platform for methodological
   development. Discriminatory metabolites defining infection can be
   correlated to patient metabolic profiles to facilitate our
   understanding of SARS-CoV-2 induced pathologies. Through both the
   immediate findings and the development of more complex models, we hope
   to increase our understanding of how SARS-CoV-2 replication and spread
   correlates to disease. Through that understanding, we can then find
   better therapeutics to limit morbidity and mortality from COVID-19.

Supporting Information

   S1 Fig. Cellular morphology during productive and non-productive
   SARS-CoV-2 infection.

   A) Further images of ACE2-A549 and A549 cells from Fig 1B. Presented at
   three-channel merged images of the transmitted light (greyscale),
   SARS-CoV-2 anti-nucleocapsid antibody (red), and Dapi (blue, nuclei).
   Scale bar 100 µm. B) Further images of infected ACE2-A549 and A549
   cells at 16hpi as in [144]Fig 1C. The three channel merged images are
   of SARS-CoV-2 anti-nucleocapsid antibody (red), actin filaments stained
   with phalloidin (green) and Dapi (blue, nuclei). Scale bars are 10 µm.

   (EPS)
   [145]pone.0305065.s001.eps^ (22.9MB, eps)
   S2 Fig. Heatmap analysis of significant metabolites (n = 152) for all
   ACE2-A549 samples at each time point.

   (EPS)
   [146]pone.0305065.s002.eps^ (5.7MB, eps)
   S3 Fig. Heatmap analysis of significant metabolites (n = 377) for all
   A549 samples at each time point.

   (EPS)
   [147]pone.0305065.s003.eps^ (6.3MB, eps)
   S1 Table. Raw mass spec values for all samples and timepoints.

   (XLSX)
   [148]pone.0305065.s004.xlsx^ (547.5KB, xlsx)

Acknowledgments