MATERIALS AND
METHODS
Human genome scale metabolic model and its adaptation to human
lung cells. To identify specific host metabolic reactions that can
alter viral production, we make use of a generic human cell genome-scale
metabolic model that has been previously developed (Thiele 2013) and
that has been subsequently curated and improved by the systems biology
community (Swainston 2016). This model, referred as RECON2.2, reflects
the state-of-the-art in genome scale metabolic model construction
applied to human cells and contains over 8000 thousand reactions, many
of which have associated gene and protein information (Swainston 2016).
This model also contains a pseudo reaction representing generic
maintenance costs of a human cell, including ATP and precursor
stoichiometries for proteins, DNA, RNA, lipids, and carbohydrates. This
pseudo biomass maintenance reaction is primarily derived using
information from human leukaemia cell lines (Thiele 2013). This generic
human cell model represents a consensus human metabolic capacity, and as
such, its use in this study allows identification of widest range of
possible metabolic reactions which can then be further scrutinized and
sifted in a cell specific context.
As discussed in the main text, when identifying metabolic perturbations
that can selectively affect viral production without much affecting the
host, the specifics of the used host maintenance representation must be
cell-specific. To align this representation to a lung cell we have made
use of the gene expression data available from the Human Protein Atlas
project (Robinson 2020). In particular, we have used the gene expression
profile for lung cells available from this project and available protein
sequences from ENSEMBL database (Yates 2020) to create a lung-cell
specific stoichiometry for amino acids required for protein synthesis.
The proportion of each amino acid in the composition of the new
maintenance function was determined by converting the protein coding
RNA’s codons to amino acids, counting their frequency and weighing these
with the normalised expression coefficient provided by the Human Protein
Atlas project. The energy cost associated with amino acid polymerisation
is also accounted for based on the length of the protein sequences, and
assuming 4.3 molecules of ATP hydrolysed to ADP per amino acid
polymerisation (Quek 2014). The stoichiometric coefficient for each
amino acid in the new maintenance function is then scaled so as to
represent the same weight as the original protein synthesis reaction in
the RECON2.2 model (not accounting for ATP, ADP, Pi nor
H2O in the scaling) (see Code Availability ). The
remaining elements of the maintenance function was retained as in the
RECON2.2 model. The final, lung-cell specific maintenance function is
provided in Supplementary File 3.
Creation of SARS-CoV-2 virus biomass function. The biomass
function for the SARS-CoV-2 is done as in a previous study (Aller 2018)
and by accounting for the composition and stoichiometry of proteins and
genomic material in the virus. The protein composition and stoichiometry
of the virus particle is obtained from electron microscopy and mass
spectrometry studies on other coronaviruses, including SARS-CoV
(Mahmoudabadi 2017; Barcena 2009; Neuman 2006; Neuman 2008; Neuman
2011). The resulting protein stoichiometry is further checked against
recent estimates specific to SARS-CoV-2 (Bar-On 2020). Composition of
protein and virus genome sequences are obtained from the National Centre
for Biotechnology Information (NCBI) nucleotide database (accession
number NC_045512). Energetic costs in form of ATP stoichiometry is
calculated as above. The final SARS-CoV-2 biomass function is provided
in Supplementary File 3 , while the computer code used to
calculate is made available (see Code Availability ).
Simulation of the metabolic model. The integrated genome scale
metabolic model was simulated using the flux balance analysis (FBA)
approach (Bordbar 2014). FBA assumes steady state of metabolic fluxes
and implements linear optimisation to find one particular flux
distribution across all reactions that can satisfy this assumption and
that is optimal under given flux constraints and a certain optimality
criterion. Here, we used the standard mathematical implementation of
flux balance analysis as described before (Bordbar 2014) and used
maximisation of flux through the host maintenance or viral biomass
pseudo reactions. All reaction flux constraints are kept as in the
original RECON2.2 model except for extracellular transport reactions.
The extracellular transport reactions are normally set to carry negative
flux to represent uptake of metabolites from the media. In the RECON2.2
model all extracellular transport reactions’ minimum flux values are set
to -1000 mmol∙gDW-1∙h-1 (where DW
stands for grams of dry weight) to represent a rich media (all exchange
reactions allowed to carry flux). We have used here both this approach
and additionally implemented a minimal media containing only essential
metabolites, carbon and nitrogen source, and oxygen. The identification
of the minimal media was achieved using a linear optimisation based
algorithmic approach (Senior 2017), where a pseudo currency metabolite
is added to all exchange reactions of the model and the flux for the
extracellular transport reaction of this pseudo metabolite is
systematically altered to identify a minimal set of exchange reactions
that can still result in model optimisation. To implement the minimal
media the identified extracellular transport reactions’ minimum flux
values were set to -1000 or to -10
mmol∙gDW-1∙h-1, with all other
extracellular transport reactions’ minimum flux set to zero. The
identified media composition is provided as Supplementary File 6
and a computational implementation of the described minimal media
identification approach is provided in Python (see Code
Availability )