INTRODUCTION
One of the crucial steps in the virus life cycle is the synthesis of the
virus particles within the host cell. This includes the synthesis of
viral structural proteins and new genomic material. For these processes,
all viruses are fully dependent on their host for the required energy
(Mahmoudabadi 2017) and building blocks (Berzin 1974; Waldbauer 2019).
This dependency is evidenced by experimental findings showing
significant metabolic flux alterations in host cells upon infection
(Maynard 2010a; Yu 2011). Systems level metabolic studies have
particularly highlighted changes in glucose uptake and glycolysis
(El-Bacha 2004; Munger 2006), which might be related to an increased
demand for biosynthetic precursors as viral production becomes the
dominant process within infected cells (Berzin 1974).
The observation of virus synthesis dominating the metabolism and
physiology of infected cells suggests that it might be possible to
manipulate the latter to control the former (Maynard 2010a; Ikeda 2007).
Indeed, several of the existing antivirals such as Ribavirin,
Remdesivir, and Gemcitabine are nucleoside analogs that target metabolic
enzymes in the nucleotide biosynthesis pathways and are thought to
function through their impact on free nucleotide pools in the cell (Wang
2011; Leyssen 2008). An even more specific metabolic approach to inhibit
virus production was demonstrated in the case of human cytomegalovirus.
For this virus, metabolic analyses highlighted a shifting of metabolic
fluxes within central carbon metabolism and fatty acid biosynthesis
pathways during infection (Munger 2008). It was predicted that these
flux changes could be blocked by perturbation of specific enzymes, which
were then targeted with available inhibitors and resulted in reduced
virus production (Munger 2008). Systematic analysis of gene knockout
effects on infection of bacteria with phage also identified many
metabolic genes associated with central carbon metabolism and substrate
transport (Maynard 2010b), leading to the proposition of utilising host
metabolic engineering to modulate viral production (Maynard 2010a). Such
metabolic control has been explored in virus-based bioproduction using
insect cells, where alterations in the culture media allowed alteration
of metabolic fluxes and production levels (Carinhas 2010).
These experimental findings show that viral biomass synthesis causes
significant metabolic flux changes in host cells and that metabolic
perturbations can directly alter virus reproduction. Thus, system-level
metabolic models could be utilised to predict what types of metabolic
alterations can cause what kinds of impact on virus reproduction. While
modelling of virus reproduction in host cells has mostly taken a kinetic
approach, focusing on translation and transcription processes (Endy
1997; You 2002, Yin 2018), it has been possible to combine such kinetic
models with genome-scale metabolic models to account for both host and
virus biomass (Jain 2009). This allowed predicting the effects of
metabolites available in the culture media on the dynamics of the
infection process (Birch 2012). In a human cell context, genome-scale
metabolic models were utilised to analyse the metabolic impact of
infection of macrophages with bacteria or viruses (Bordbar 2010; Aller
2018). One of these studies incorporated viral production into the
macrophage metabolic model and predicted specific reaction perturbations
that can cause a reduction in viral reproduction (Aller 2018). These
predictions correctly identified enzyme targets of the aforementioned
antiviral drugs in nucleotide pathways and highlighted new target
enzymes (Aller 2018). Such findings from the virus-host metabolic
modelling aligns with the observations that genome-scale metabolic
models can provide a comprehensive stoichiometric catalogue of possible
biochemical conversions in a cell (Thiele 2013; Swainston 2016) and that
can generate useful qualitative predictions on the impact of
environmental or genetic alterations on the cellular metabolic flux
distributions (Edwards 2000; Segré 2002; Papp 2004).
Motivated by the qualitative predictive power of stoichiometric
metabolic models and flux analysis, we apply it here to simulate the
production of SARS-CoV-2 virus particles as part of the host metabolism
and predict metabolic inhibitions against this virus. We extend the
available human genome-scale metabolic model with a viral biomass
reaction, estimated using structural information available from
SARS-CoV-2 and related viruses. By optimising flux distributions in this
model for viral reproduction, we were able to predict reactions that can
inhibit this process. We explored the possible impact of these predicted
inhibitions on the host metabolism itself, using an estimation of host
cell maintenance in human lung cells based on available protein
expression data. In addition, we have explored experimental feasibility
of implementing the predicted metabolic perturbations using available
drug and inhibitor information on metabolic enzymes. Our results
indicate that individual and double perturbation of several metabolic
reactions from central metabolic pathways can inhibit SARS-CoV-2
reproduction production in general and some of these can do so
selectively without affecting normal metabolic functions of the host. We
highlight these reactions as experimentally testable drug targets for
inhibiting SARS-CoV-2 reproduction in human lung cells and provide
details of the implemented computational approach for further
development.