Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network

Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements

An integrated computational-experimental approach reveals Yersinia pestis genes essential across a narrow or a broad range of environmental conditions

Background The World Health Organization has categorized plague as a re-emerging disease and the potential for Yersinia pestis to also be used as a bioweapon makes the identification of new drug targets against this pathogen a priority. Environmental temperature is a key signal which regulates virulence of the bacterium. The bacterium normally grows outside the human host at 28 °C. Therefore, understanding the mechanisms that the bacterium used to adapt to a mammalian host at 37 °C is central to the development of vaccines or drugs for the prevention or treatment of human disease. Results Using a library of over 1 million Y. pestis CO92 random mutants and transposon-directed insertion site sequencing, we identified 530 essential genes when the bacteria were cultured at 28 °C. When the library of mutants was subsequently cultured at 37 °C we identified 19 genes that were essential at 37 °C but not at 28 °C, including genes which encode proteins that play a role in enabling functioning of the type III secretion and in DNA replication and maintenance. Using genome-scale metabolic network reconstruction we showed that growth conditions profoundly influence the physiology of the bacterium, and by combining computational and experimental approaches we were able to identify 54 genes that are essential under a broad range of conditions. Conclusions Using an integrated computational-experimental approach we identify genes which are required for growth at 37 °C and under a broad range of environments may be the best targets for the development of new interventions to prevent or treat plague in humans

A yeast metabolite extraction protocol optimised for time-series analyses.

There is an increasing call for the absolute quantification of time-resolved metabolite data. However, a number of technical issues exist, such as metabolites being modified/degraded either chemically or enzymatically during the extraction process. Additionally, capillary electrophoresis mass spectrometry (CE-MS) is incompatible with high salt concentrations often used in extraction protocols. In microbial systems, metabolite yield is influenced by the extraction protocol used and the cell disruption rate. Here we present a method that rapidly quenches metabolism using dry-ice ethanol bath and methanol N-ethylmaleimide solution (thus stabilising thiols), disrupts cells efficiently using bead-beating and avoids artefacts created by live-cell pelleting. Rapid sample processing minimised metabolite leaching. Cell weight, number and size distribution was used to calculate metabolites to an attomol/cell level. We apply this method to samples obtained from the respiratory oscillation that occurs when yeast are grown continuously

Time-Structure of the yeast metabolism in vivo

All previous studies on the yeast metabolome have yielded a plethora of information on the components, function and organisation of low molecular mass and macromolecular components involved in the cellular metabolic network. Here we emphasise that an understanding of the global dynamics of the metabolome in vivo requires elucidation of the temporal dynamics of metabolic processes on many time-scales. We illustrate this using the 40 min oscillation in respiratory activity displayed in auto-synchronous continuously grown cultures of Saccharomyces cerevisiae, where respiration cycles between a phase of increased respiration (oxidative phase) and decreased respiration (reductive phase). Thereby an ultradian clock, i.e. a timekeeping device that runs through many cycles during one day, is involved in the co-ordination of the vast majority of events and processes in yeast. Through continuous online measurements, we first show that mitochondrial and redox physiology are intertwined to produce the temporal landscape on which cellular events occur. Next we look at the higher order processes of DNA duplication and mitochondrial structure to reveal that both events are choreographed during the respiratory cycles. Furthermore, spectral analysis using the discrete Fourier transformation of high-resolution (10 Hz) time-series of NAD(P)H confirms the existence of higher frequency components of biological origin and that these follow a scale-free architecture even in stable oscillating modes. A different signal-processing approach using discrete wavelet transformations (DWT) indicates that there is a significant contribution to the overall signal from ∼ 5, ∼ 10 and ∼ 20-minutes cycles and the amplitudes of these cycles are phase-dependent. Further investigation (derivative of Gaussian continuous wavelet transformation) reveals that the observed 20-minutes cycles are actually confined to the reductive phase and consist of two ∼ 15-minutes cycles. Moreover, the 5 and 10-minutes cycles are restricted to the oxidative phase of the cycle. The mitochondrial origin of these signals was confirmed by pulse-injection of the cytochrome c oxidase inhibitor H2S. We next discuss how these multi-oscillatory states can impinge on the apparently complex reactome (represented as a phase diagram of 1,650 chemical species that show oscillatory behaviour). We conclude that biological processes can be considerably more comprehensible when dynamic in vivo time-structure is taken into account

Metabolites (attomol/cell) extracted from samples taken during respiratory oscillation (minimum dissolved oxygen; figure 4), detected by CE-MS and the methods used to extract them. nd – not detected.

1<p>-Q not quenched.</p>2<p>BB bead-beating.</p>3<p>All other samples were performed in triplicate, except this one.</p>4<p>NEM <i>N</i>-ethylmaleimide.</p>5<p>NEM added pre bead-beating.</p>6<p>NEM added post bead-beating.</p

The general reaction scheme for <i>N</i>-ethylmaleimide on biological thiols.

<p>The product formed is an <i>N</i>-ethylsuccinimido conjugate and the monoisotopic mass of the reactant is increased by 125.048. For example the glutathione (m/z 308.0911) conjugate is <i>N</i>-ethylsuccinimido-S-glutathione (m/z 433.1391).</p

Electrophoretograms of glutathione (GSH) and homocysteine (Hcys) and their NEM derivatives (ESG and ESHcys) in representative samples with (red line) and without (green line) the addition of 2 mM NEM.

<p>Electrophoretograms of glutathione (GSH) and homocysteine (Hcys) and their NEM derivatives (ESG and ESHcys) in representative samples with (red line) and without (green line) the addition of 2 mM NEM.</p

A heatmap of the calibrated intracellular metabolite time-series during the respiratory oscillation (A) and time-series for ATP (B), glutathione (C), and aspartate (D) in <i>S. cerevisiae</i>.

<p>Cationic and anionic data are shown in red and blue (lines or shaded areas), respectively. The corresponding oscillation in dissolved oxygen (DO) is represented by a grey line.</p