RESULTS AND
DISCUSSION
To simulate the production of SARS-CoV-2 virus particles in a human cell
we utilised an existing, community-developed human genome scale model
known as RECON 2.2 (Thiele 2013; Swainston 2016) (see Methods ).
This model represents the most comprehensive catalogue of metabolic
reactions found in human cells, with many of its reactions associated
with known genes (Swainston 2016). Within this model, we implement a
pseudo reaction representing the production of SARS-CoV-2 viral
particles from biosynthetic precursors (see Figure 1A andMethods ). The construction of this pseudo reaction is based on
available structural information on the virus including its use of
proteins for viral packaging (Bar-On 2020; Mahmoudabadi 2017; Bárcena
2009; Neuman 2006; 2008; 2011) and its genome sequence. As such, this
pseudo biomass reaction accounts for the stoichiometry of nucleic and
amino acids required to make a complete virus and associated energetic
costs. This analysis highlights that leucine and alanine are the most
utilised amino acids in SARS-CoV-2 proteins and adenosine- and
uridine-triphosphate are the more common nucleotides in its RNA (Figure
1B).
Metabolic fluxes supporting SARS-CoV-2 production in a human
cell are primarily in central carbon metabolism. By incorporating the
SARS-CoV-2 viral biomass function into the human metabolic model and
assuming a minimal media composition (see Methods ) we predict a
metabolic flux distribution for optimal virus production in a human cell
(Figure 1C). We then evaluated the flux variability allowed in each
reaction of the model, while maintaining an optimal viral production
level (Supplementary File 1). These analyses have shown that
reactions which must carry flux for optimal viral biomass production
include glycolysis, oxidative phosphorylation, fatty acid oxidation, and
specific amino and nucleic acid biosynthesis pathways (Figure 1C andSupplementary File 1). As the optimal flux distributions are
related also to the flux limits imposed on uptake reaction fluxes, we
also repeated the flux variability analysis with minimal media but using
an increased uptake limit and with a rich media that allows all uptake
reactions of the model to be active. Increasing uptake limits did not
alter the general conclusions about key active pathways sustaining
optimal virus production, but resulted in higher uptake fluxes causing
additional pathways relating to overflow metabolism to be active
(Supplementary File 1). Simulating a rich media resulted in a
much lower number of flux-carrying reactions as the cell can obtain
several key compounds such as uridine-triphosphate from the media under
this scenario (Supplementary File 1). Since, such a rich media is
physiologically less realistic, we focus the remaining analysis on the
results from simulations using the minimal media.
Inhibiting specific metabolic enzymes and enzyme combinations
inhibit SARS-CoV-2 production in human cells. To identify reaction
perturbations which, when inhibited, can halt or reduce virus
production, we systematically simulated a knock-out of each
flux-carrying reaction. Excluding reactions involved in uptake from
media, this analysis highlighted 35 reaction knock-outs that can stop
virus production and an additional 8 reactions that can reduce it below
80% of the original (Figure 1C). The former group of reactions tended
to be involved in nucleotide and amino acid biosynthesis pathways, while
the latter group included reactions primarily in glycolysis and
oxidative phosphorylation (Table 1 and Supplementary File 2). Key
ones among these reactions are further discussed below.
Considering that it is possible for the effects of single perturbations
to be circumvented by re-directing of fluxes, we also explored combined
perturbations. We created all possible pairs of flux-carrying reactions
according to the flux variability analysis (over 5000 pairs) and
simulated the effect of setting their reaction fluxes to zero. This has
identified over 400 reaction pairs, co-inhibition of which results in
the reduction of virus optima to 80% or less of the original (Table 1
and Supplementary File 2). Most of these reaction pairs involved
one of the 10 single perturbations that were found to reduce virus
production to less than 80% on their own, but pairing them with
additional reaction increased their impact. For example, inhibition of
glyceraldehyde-3-phosphate dehydrogenase (GAPD) and cytochrome c oxidase
(CYOR) individually caused reduction to 62% and 60% of original virus
production respectively, but combined inhibition of these reactions
results in 25% of original production (Supplementary File 2).
Some of these cases of increased effect arises due to co-inhibition of
reactions more effectively blocking fluxes into virus biomass
precursors. For example, combined blocking of GAPD and CYOR, reactions
involved in respiration and glycolysis respectively, results in reduced
fluxes into pyruvate and alpha-ketoglutarate (akg), a key intermediary
of the tricarboxylic acid (TCA) cycle. Akg is further linked into valine
production through the valine:3-methyl-2-oxobutanoate shuttle across the
mitochondrial membrane (Figure 2). In the optimal flux distribution for
SARS-CoV-2 production, this “valine shuttle” has a high flux and
contributes to the production of both valine and multiple other amino
acids via mitochondrial glutamate (Figure 2b). Perturbations to both
CYOR and GAPD lead to a new flux distribution where the glutamate
production in the mitochondrial matrix is sustained through a different
metabolic route. The “valine shuttle” that was active in the optimal
solution is now non-functional and is instead replaced by a
leucine:4-methyl-2-oxopentanoate shuttle carrying a lesser flux. This in
turn decreases the production of the amino acids from glutamate, and
thus causing a significant decrease in virus biomass production flux
(Figure 2).
In the case of simulating the minimal media with higher uptake fluxes,
we have also identified pairs of completely new enzyme inhibitions, that
were not causing any effect on their own (Supplementary File 2).
Some of these pairs exert their effects by blocking multiple pathways
from a given compound and thereby causing disruption in steady state
balances in the model. For example, co-inhibition of citrate synthase
(CSm) and several other enzymes such as histidase (HISD) totally
prevents flux in SARS-CoV-2 biosynthesis reaction by making impossible
the mass balance of protons in the cytosol (see Figure S1).
Metabolic requirements of viral production are different to
those arising from host cellular maintenance. In the above discussed
analyses, we considered host metabolism as optimised for viral
production and evaluated impact of perturbations only on this process.
Such metabolic perturbations should also be evaluated for their impact
on the normal metabolic functions of uninfected host cells. In previous
studies, normal state of metabolism in human cell lines are either
represented through a pseudo reaction for cellular maintenance (Bordbar
2010; Thiele 2013) or through consideration of specific metabolic
functions such as ATP or lipid production (Gille 2010; Mardinoglu 2014).
In the former case, cell maintenance is captured by a generic account of
cellular constituents such as lipids, carbohydrates and DNA and a more
specific accounting of amino acid usage in protein expression (Thiele
2013; Bordbar 2010;). In RECON2.2, the protein-based component of the
maintenance function is calculated from a large collection of human
genes’ open reading frames (Thiele 2013).
Here, we expanded from this approach and calculated the biomass protein
component using available protein expression data for lung cells from
the Human Protein Atlas project (Uhlén 2015) (see Methods ).
Comparing the resulting human lung cell maintenance function to the
SARS-CoV-2 biomass, in terms of the building block stoichiometries,
revealed differences in relative amino and nucleic acid usage (Figure 3
and Supplementary File 3). Compared to the host, there was
particularly higher relative usage of phenylalanine, isoleucine,
asparagine, threonine, tryptophan, and tyrosine in the virus and
particularly lower relative usage of glutamate, histidine, methionine,
and proline (Figure 3). Accordingly, the optimisation of the model using
the host metabolic maintenance results in a different metabolic flux
distribution compared to viral production (Supplementary File 4).
The differences, however, were rather limited from the perspective of
fluxes supporting SARS-CoV-2 production; out of all reactions that must
carry a flux to sustain a virus optimal state, almost all were also
required to carry flux to sustain a host optimal state
(Supplementary File 4).
Flux control can ensure selective reduction in viral
production. Given the above finding that the same reactions carrying
flux for SARS-CoV-2 production also carry flux for host metabolic
maintenance, we re-analysed the effects of enzyme perturbations on both
virus and the host. We found that many of the previously identified
single perturbations limiting virus production also limited
significantly the host metabolic maintenance, with only one single
perturbation - that involving CYOR - showing more than 5% difference in
its impact on virus vs. the host (Supplementary File 5). The same
finding prevailed for double perturbations. The only pairs that
displayed 5% or more difference in their effects on virus vs. host are
those involving CYOR paired with other enzymes (Supplementary
File 5).
The limited differential impact of full inhibition of enzymes made us
postulate that more refined perturbations could provide a better
strategy to just impact SARS-CoV-2 production without affecting the
host. In particular, given the differences in optimal metabolic fluxes
between virus production and host maintenance states, we argued that
there might be flux values for some reaction that are compatible with
only one of these states. To explore this possibility, we systematically
analysed the flux variability of each reaction given either the
optimisation of host maintenance or virus production. This has allowed
us to see if any of the reactions would have flux regimes that are only
compatible with the optimal host maintenance but not with optimal virus
production and then ‘enforce’ such flux regimes on the model. This
approach allowed us to identify few double reaction perturbations that
are fully selective on their effect and solely reduce virus production
without causing any impact on the host (see Table 1). All of these
involved threonine deaminase (THRD) and caused up to 17% reduction in
SARS-CoV-2 production (Table 1).
The flux enforcement approach creates further constraints on how the
metabolic fluxes in the system can be balanced at steady state. In the
case of flux enforcements involving THRD this reduced set seems to leave
only certain flux distributions to be possible that are specifically
less optimal for the virus’ biomass biosynthesis, but still optimal for
host maintenance function to be fulfilled. Expected from this, we found
that the effective flux enforcements relate to compositional differences
between the viral biomass and host maintenance functions, as illustrated
in Figure 4 for the case of the enforcement of THRD and succinate
dehydrogenase (SUCD1m). In the optimal flux distribution for SARS-CoV-2
production in the unmodified model, threonine is obtained from the
medium through a threonine:leucine shuttle, with both of these amino
acids being in relatively similar demand between host and virus
requirements. In the case of THRD and SUCD1m fluxes enforced to specific
ranges, threonine is instead obtained through a threonine:isoleucine
shuttle and is now further intertwined with the arginine:isoleucine
shuttle (Figure 4). This shift in fluxes happens because the flux
enforcement of SUCD1m, a reaction of the TCA cycle, decreases the flux
of the previously observed valine shuttle (see Figure 2) and increases
the flux of alternative mitochondrial shuttle reactions including one
involving leucine (see Figure 4). The reduced availability of leucine
causes the utilisation of isoleucine and arginine for threonine, thereby
creating a trade-off among these amino acids. Crucially, arginine is the
highest and isoleucine the third highest differentially demanded amino
acid when comparing virus vs. host requirements (Figure 3). This is why
the described trade-off situation among these amino acids and threonine,
caused by the flux enforcement, differentially impacts the SARS-CoV-2
biomass production more than it effects host maintenance.
Current metabolic drugs exist that could target predicted
reactions to inhibit production of SARS-CoV-2. The metabolic approach
employed here allowed prediction of several reactions and reaction
combinations that could limit SARS-CoV-2 production in human cells in
general and differentially in human lung cells. The most significant of
these are re-summarised in Table 1 as those reactions, the perturbation
of which, can reduce virus production below 80% of the original. For
these reactions, we evaluated their associated enzymes in the light of
existing, approved drugs using the available small molecule inhibitor
and drug database DrugBank (Wishart 2018). We found several existing
drugs that could inhibit some of the predicted reactions including those
targeting enolase (ENO), phosphoglycerate mutase (PGM), and SUCD1m
(Table 2). These drugs could be used as a starting point to
experimentally test the predictions made here, using in vitroassays. In addition to these identified small molecule inhibitors, we
note that it might also be possible to achieve development of de
novo metabolic gene knock-out approaches using recent CRISPR and RNA
silencing approaches.