Wolbachia is a maternally transmitted bacterial symbiont that is estimated to infect approximately half of arthropod species. In the laboratory it can increase the resistance of insects to viral infection, but its effect on viruses in nature is unknown. Here we report that in a natural population of Drosophila melanogaster, individuals that are infected with Wolbachia are less likely to be infected by viruses. By characterising the virome by metagenomic sequencing and then testing individual flies for infection, we found the protective effect of Wolbachia was virus-specific, with the prevalence of infection being up to 15% greater in Wolbachia-free flies. The antiviral effects of Wolbachia may contribute to its extraordinary ecological success, and in nature the symbiont may be an important component of the antiviral defences of insects. Wolbachia on viral infection of a natural Drosophila population through metagenomics sequencing of the virome. This study provides new evidence showing that Wolbachia confers viral resistance in natural population of flies.Cogni et al. report a protective effect of  Wolbachia is an alphaproteobacterium that lives within the cytoplasm of arthropod cells and is maternally transmitted. It infects approximately half of arthropod species1, and many strains manipulate host reproduction, most commonly by inducing cytoplasmic incompatibility (CI)2. CI allows Wolbachia to invade populations by causing embryonic mortality when uninfected females mate with infected males, hence conferring a selective advantage to infected females4. Wolbachia can also protect Drosophila species against RNA viruses6. Combined with Wolbachia’s ability to invade populations due to CI, this provides a way to modify natural insect populations to make them resistant to viral infections. Wolbachia has been transferred from Drosophila to the mosquito Aedes aegypti, where it limits the replication of the dengue virus as well as chikungunya, yellow fever, Zika and West Nile viruses10. When Wolbachia-infected mosquitoes were released into the wild, the bacterium spread through the mosquito populations by CI11, and large field trials have shown substantial reductions in dengue incidence in the human population13.Wolbachia have great value in the field of public health, their ecological importance is far from clear. As Wolbachia is estimated to infect 52% of arthropod species1, it may be a major component of antiviral defences in nature. However, studies on the antiviral effects of Wolbachia have largely been performed under laboratory conditions and frequently with artificial routes of infection. Wolbachia protects wild mosquitoes against the dengue virus, but here Wolbachia has been artificially transferred into the mosquito, resulting in an activation of immune defences that is not typical of natural host-Wolbachia associations14. Furthermore, two studies of natural populations of Drosophila melanogaster have failed to find evidence of Wolbachia protecting infected insects against viral infection16, so there is currently no evidence that Wolbachia is a natural antiviral defence of insects17.While the antiviral effects of Wolbachia may frequently be a mutualist that defends its host against infection. This may explain why Wolbachia strains that do not cause CI and have no obvious phenotypic effect can invade and be maintained in populations. For example, the Wolbachia strain wAu spread through Australian populations of Drosophila simulans despite not causing CI18. In the same host species, the wRi Wolbachia strain has evolved to become a mutualist, but the cause of the fitness benefit is unknown19. The benefits provided by antiviral protection could also allow CI-inducing strains of Wolbachia to invade new populations and species. Theory predicts that CI can only invade when local infection frequencies become sufficiently high to offset imperfect maternal transmission and infection costs20. However, recent data suggested that Wolbachia can spread from arbitrarily low frequencies18. This can be explained if there is a fitness advantage for the host caused by Wolbachia, which may be its antiviral effects.If antiviral protection is present in nature, D. melanogaster from an orchard in Connecticut, USA and extracted RNA from single flies. To characterize the diversity of viruses in this population in an unbiased way, we pooled RNA from groups of 23 flies to generate 40 RNAseq libraries. These were mapped to the published genome sequences of D. melanogaster, the Wolbachia strain wMel and known Drosophila viruses. The unmapped reads were then assembled to identify novel Drosophila-associated viruses (see methods for inclusion criteria).We collected 1014 male D. melanogaster in this population , an arthropod (Serbia narna-like virus 4-like) and a trypanosome (Leptomonas seymouri RNA virus-like). As viruses will be present in the food, environment and pathogens of flies, we would caution that the presence of these viruses in our samples does not mean they infected D. melanogaster, although the close relationship of many of them to other arthropod viruses suggests that some do . This infection rate was driven by the high prevalence of Galbut virus and Vera virus, which infect 68% and 75% of flies respectively , two dsRNA viruses  and six positive-sense RNA viruses .We used our RNAseq data to design PCR primers that matched the eleven viruses present in all our libraries, and tested the panel of 1014 individual flies for infection by quantitative PCR. Viral infection is common, with 93% of flies infected with at least one virus (Wolbachia (N = 1014), and these flies were infected with fewer viruses. Wolbachia-free flies were infected with a mean of 2.85 viruses, which is 15% more than the number of viruses detected in Wolbachia-infected flies , suggesting the Wolbachia is protecting flies against infection in nature.Seventy-one percent of the flies carried Wolbachia-free and Wolbachia-infected flies, and found there are no cases where the symbiont completely blocks viral infection . These were a positive-sense RNA virus—Motts Mill virus—where the Wolbachia-free flies were 2.73 times more likely to be infected, and the dsRNA partitivirus Vera virus, where Wolbachia-free flies were 1.19 times more likely to be infected . However, Galbut virus loads were significantly lower in the presence of Wolbachia . Comparing the distribution of viral loads, it is clear that this is caused by a minority of flies with strongly reduced viral loads in the Wolbachia-infected flies, while most individuals have similar loads .As well as reducing the likelihood that flies are infected, ads Fig. . FurtherWolbachia protects wild Drosophila against viral infection, with Wolbachia-infected flies carrying on average 0.37 fewer viruses. As viruses are common in natural insect populations, this phenotype may benefit many Wolbachia-infected insects and partly explain the extraordinary ecological success of Wolbachia. If the magnitude of this benefit is sufficient to outweigh the fitness cost of carrying Wolbachia, the symbiont will become a mutualist that can invade populations in the absence of other phenotypes. Establishing whether this is the case is particularly important as the Wolbachia strains that provide the greatest anti-viral protection tend to be associated with the highest fitness costs, as both traits depend on the density of Wolbachia in insect cells31. However, even if the benefits of antiviral protection are insufficient to make Wolbachia a mutualist and there remains a net fitness cost, then the antiviral phenotype can still reduce this cost, making it more likely that Wolbachia can invade populations as a reproductive parasite20.We have found that Wolbachia on host fitness will depend not only on the reduction in viral prevalence and titre, but also on how harmful virus infection is to the fly. Of the three viruses affected by Wolbachia, only the phenotypic effects of Galbut virus infection have been reported. Under laboratory conditions this virus had only very modest effects on lifespan and fecundity32. If we speculate that these results hold for other viruses affected, and given that Wolbachia-infected flies carrying ~0.37 fewer viruses, the magnitude of any fitness benefit might be so small as to have minimal impact on Wolbachia dynamics. However, harsh competitive conditions can increase the cost of infection, and these may be common in the field. For example, flies infected with the Drosophila melanogaster sigmavirus appear healthy in the laboratory. However, in the field or under competitive laboratory conditions it is estimated to reduce fitness by 20–30%34. If this was the case for the viruses affected by Wolbachia, then the benefits of antiviral protection could be as high as 10%. This is comparable to the fitness benefit of wAu that allowed it to invade populations of Drosophila simulans in the absence of CI18.The effect of D. melanogaster to the species level. However, because Wolbachia is maternally transmitted, it is antiviral protection in females that will have the greatest effect on the symbiont’s fitness and population dynamics. Therefore, an important question for the future is whether similar levels of antiviral protection are seen in female hosts.An important caveat to this study is that we only investigated males, as we could not reliably morphologically identify female Wolbachia on the natural viral community of Drosophila. The first of these was a study designed to characterize the diversity of viruses infecting D. melanogaster and D. simulans, and the authors suggest their sampling design means they have low power to detect the effects of Wolbachia16. The second study investigated D. melanogaster, but used considerably smaller sample sizes than us and reared the flies for one or more generations in the laboratory at 19 °C before testing them15. It was later discovered that the antiviral effect of wMel is greatly reduced at this temperature35. Finally, another study investigated D. simulans but used comparatively small sample sizes that are unlikely to detect effects of the size we observed36.Our results contrast with three previous that failed to find any effect of 37. For the first time, our results demonstrate Wolbachia naturally protects wild insects against infection and should therefore be regarded as a defensive symbiont. Because Wolbachia is so common in terrestrial arthropods1 it may be an important component of antiviral defence in many species. This has the potential to affect the population biology of beneficial and pest insects, disease transmission by vector species, and the evolution of insect immune defences38.The microbiome plays a key role in protecting animals against infection, and in insects, this role is frequently played by specialized heritable endosymbionts that function alongside the immune system as an integral component of the animal’s defences against infectionDrosophila melanogaster populations40. From 4 to 6 September 2018, we collected a total of 1014 D. melanogaster males by aspirating and netting over fermenting dropped peaches. We collected males as they can be identified to species level morphologically and individually preserved them in RNAlaterTM reagent a few hours after field collecting.Flies were collected at Lyman Orchards in Middlefield, CT, USA, a common field site to collect natural TM  extraction as previously described41. RNA pellets were re-suspended in 10 µl nuclease-free water  and stored at −80 °C. Half of the RNA from each fly was saved for library preparation and half was reverse transcribed with Promega GoScript reverse transcriptase and random hexamer primers. cDNA was diluted 1:10 with nuclease-free water. RT-qPCR was performed on an Applied Biosystems StepOnePlus system using Sensifast Hi-Rox Sybr kit (Bioline) with the following PCR cycle: 95 °C for 2 min followed by 40 cycles of: 95 °C for 5 s followed by 60 °C for 30 s. Each sample was tested for Wolbachia infection by amplification of a segment of the gene atpD by RT-qPCR using primers CCTTATCTTAAAGGAGGAAA and AATCCTTTATGAGCTTTTGC31. To normalise estimates of Wolbachia and virus loads we also amplified the fly gene RpL32 using primers TGCTAAGCTGTCGCACAAATGG and TGCGCTTGTTCGATCCGTAAC42.RNA was isolated from single flies using TRIzolSingle fly RNA samples were combined into 40 different pools, each pool contained samples from 23 individual flies to give a total volume of 69 µl per pool. The RNA from each pool was quantified using Qubit RNA HS assay kit . RNAseq libraries were prepared from each RNA pool as follows: Ribosomal RNA was depleted using a Ribo-Zero Gold rRNA Removal Kit (Human/Mouse/Rat) . Between 620 ng of RNA for the lowest and 1800ng of RNA for the highest sample in a total volume of 28 µl was used. To this was added 8 µl of Ribo-zero removal solution and 4 µl of Ribo-Zero reaction buffer. The protocol was followed according to the manufacturer’s recommendation. The rRNA-depleted RNA was cleaned up using ethanol precipitation and the resulting pellet was re-suspended in 5 µl of nuclease-free water.43. Libraries were normalized to the concentration of the lowest in the pool by diluting in 0.1x TE buffer and combined into three separate pools of 13 or 14 libraries. The multiplexed library pools were quantified by Qubit HS DNA as above and assessed for quality and average fragment length using a Bioanalyzer HS DNA kit as above. The concentration of each pool was calculated and then diluted to 20 nM by adding the appropriate quantity of 0.1x TE buffer before sequencing. Paired-end RNA sequencing reads from 40 libraries were obtained. Libraries were sequenced on three lanes of the Illumina HiSeq4000 with paired end 150 bp reads. Quality control of the raw RNA sequencing reads was implemented with TrimGalore-0.6.0 .RNAseq libraries were prepared using the NEB Next Ultra II Directional RNA Library Prep kit for Illumina  according to the manufacturer’s recommendations. All 5 µl of the rRNA-depleted sample was used for each library. Adapters used were from KAPA Single-Indexed Adapter kits KK8701 and KK8702, the 30 µM stock was diluted to 1.87 µM before use. Eight cycles of PCR were used to amplify the libraries. Libraries were quantified using Qubit HS DNA quantification kit . The final concentration of libraries was 11–29 ng/µl in a volume of 20 µl. The quality of the libraries was assessed using Bioanalyzer HS DNA kit  according to the manufacturer’s instructions. The relative quantity of the libraries was ascertained using qPCR: 3 × 1:1000 dilutions were made from each library by adding 1 µl of library to 1 ml of 10 mM Tris-HCl pH8.0 with 0.05% Tween 20. Two microlitres of each dilution was used in a qPCR reaction using primers, IS5.reamp.P5: AATGATACGGCGACCACCGA and IS6.reamp.P7: CAAGCAGAAGACGGCATACGADrosophila melanogaster, Wolbachia strain wMel and viruses isolated from or associated with flies in the genus Drosophila  were collected and subsequently blasted to NCBI non-redundant protein database and viral non-redundant protein database using DIAMOND blastx48. Contigs with blast top results corresponding to viral origins in both databases were identified as candidate viral contigs, and were selected to be assembled into longer contigs using Sequencher 4.5 (http://www.genecodes.com), followed by manual curation (Sequencher contigs).To reduce the size of the dataset, unmapped reads from all libraries were pooled and mapped to Ribosomal RNAs (rRNA) database downloaded from SILVA49. Phylogenies were estimated using PhyML50 with LG substitution model and nearest neighbour interchanging during the tree search. We identified numerous novel viruses that clustered within the Mitoviridae in the phylogenetic tree, and these were excluded as they may have been infecting other organisms such as yeasts and were mostly uncommon.These candidate viral contigs were once more queried against the NCBI non-redundant protein database using DIAMOND blastx to identify closely related viruses for inclusion in phylogenetic analyses. Novel viruses where the top blast hit in GenBank did not infect eukaryotes were excluded from downstream analyses. Where available, RNA-dependent RNA-polymerase protein sequences of these related viruses were then used to construct phylogenetic trees. Multiple sequence alignment was done using the M-Coffee mode in T-CoffeeDrosophila melanogaster, Wolbachia and Drosophila related viruses, with all published sequences included) combined with the new viral contigs assembled from this population. Again, mapping was performed using STAR-2.6.0 with default settings  and Machany virus (D. obscura) read counts were positively correlated with that of their close relatives, respectively Motts Mill virus (D. melanogaster) and Kilifi virus (D. melanogaster)22, suggesting miss-mapping , which infects ~38% of D. immigrans flies52, was excluded as there was evidence to suggest low levels of D. immigrans contamination in the RNAseq libraries, and the count of D. immigrans mitochondrial COI reads was positively correlated with the count of DimmSV reads  , but in this case many flies were infected with Galbut but not Chaq. This agrees with previous data suggesting Chaq virus is either a satellite virus of Galbut virus or an ‘optional’ segment of the Galbut virus genome21. We, therefore, refer to this sequence as Galbut (Chaq) virus.We used our PCR data (see below) to identify pairs of contigs that were likely segments of the same viral genome. First, there was a strong correlation between the abundance of a new viral contig we identified and Vera virus . Because primers for the viruses and the endogenous genes have approximate similar efficiencies, we calculated viral titer from the cycle thresholds (Ct) as 2CtΔ, where ΔCt = CtRpL32 − Ctvirus.Quantitative PCR (qPCR) was used to determine the presence and load of each virus in each sampled fly. Primers were designed in Primer-BLAST, which uses the Primer3 and BLAST, setting Wolbachia on the probability that flies were infected by viruses was estimated using a generalised linear mixed model implemented using the R package MCMCglmm56, which uses Bayesian Markov chain Monte Carlo (MCMC) techniques. The binary response variable was whether or not a single fly tested positive for a given virus, which was treated as a binomial response with a logit link function. The model included a single fixed effect—whether or not a fly was infected with Wolbachia. The first random effect in the model was the identity of the individual fly being tested. The second random effect was the identity of the virus being tested for. For this random effect, separate variances were estimated for Wolbachia-infected and Wolbachia-free flies, and the covariance was set to zero (specified as ‘idh(wolbachia):virus’ in MCMCglmm). We used inverse Wishart priors . We estimated the prevalence of viruses in Wolbachia-infected and Wolbachia-free flies from the random effects of the model, and these estimates were transformed from the logit scale back into proportions. Credible intervals were obtained as the 95% highest posterior density of these random effects. To investigate if there was an effect of Wolbachia on flies being infected with a given virus, we calculated the proportion of samples from the MCMC chain where the viral prevalence in Wolbachia-infected samples is less than the prevalence in Wolbachia-free samples. The risk ratio was estimated by dividing the random-effects estimate of the prevalence in Wolbachia-infected flies by the estimate in Wolbachia-free flies for each sample from the MCMC chain, and then calculating the mean (posterior mean) and 95% highest posterior density  of these numbers.The effect of Further information on research design is available in the Transparent Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Direct at line monitoring of live virus particles in commercial manufacturing of vaccines is challenging due to their small size. Detection of malformed or damaged virions with reduced potency is rate-limited by release potency assays with long turnaround times. Thus, preempting batch failures caused by out of specification potency results is almost impossible. Much needed are in-process tools that can monitor and detect compromised viral particles in live-virus vaccines (LVVs) manufacturing based on changes in their biophysical properties to provide timely measures to rectify process stresses leading to such damage. Using ERVEBO, MSD’s Ebola virus vaccine as an example, here we describe a flow virometry assay that can quickly detect damaged virus particles and provide mechanistic insight into process parameters contributing to the damage. Furthermore, we describe a 24-h high throughput infectivity assay that can be used to correlate damaged particles directly to loss in viral infectivity (potency) in-process. Collectively, we provide a set of innovative tools to enable rapid process development, process monitoring, and control strategy implementation in large scale LVV manufacturing. The resulting virus harvest then undergoes a series of purification and concentration steps to generate the bulk drug substance which is typically frozen. Weeks or months later the bulk drug substance will be thawed, formulated and filled into vials which may then be lyophilized to generate the stable drug product (DP) for patient use. For a typical large-scale LVV manufacturing process, the time from initial virus infection of cells to vial filling is several months. Across all of these processing steps, it is important to preserve the critical quality attributes (CQA) of the virus particles, which, for most LVVs, are infectivity or antigenicity, or both. Potency is determined by infectivity of the virus particles in a known host cell and is often measured using a plaque forming assay by counting the number of plaque forming units (pfu) or by a TCID50 assay. Antigenicity, if required, is usually measured using an ELISA. Both plaque or ELISA are used for release testing of LVV batches at the end of manufacturing a vaccine batch for drug substance (DS) or DP9. Both of these release assays are time and resource-intensive, resulting in extended lead times of about two weeks or more to obtain results. However, these assays are not designed to detect and respond to any at line variabilities that can affect the individual virus particles being manufactured. Control strategies currently in place for most commercial LVV manufacturing include in line probes to obtain measurements of cellular metabolites like glucose and lactate and monitoring pH and gas profiles of oxygen and CO2. While these measurements provide an indication of changes in the infected cell culture and thereby offer an indirect way to monitor virus particles, they do not provide sufficient direct control or monitoring of virus quality or quantity. Development of process analytical tools (PAT) that can offer innovative ways to directly monitor virus particles at-line are much needed. The benefits such a PAT could offer include developing a strong correlation between total virus particles manufactured in-process and their predicted infectivity and antigen levels in DS. Release assays could then only be used for a final commercial batch release and not for intermediate testing of DS. A second benefit is detection and response to any in-process virus alterations in real time. Conceivably, a suitable PAT intervention for direct virus monitoring can offset some unique vulnerabilities of existing LVV manufacturing, like inadvertent generation of damaged virus particles resulting in out of specification (OOS) DS and costly batch failures.Traditional LVV manufacturing uses mammalian cellular substrates for virus productionIn addition to the final manufacturing process, scale-down process models are used to establish process parameter ranges for the commercial-scale manufacturing process during process characterization and development . Experiments using scale-down models investigate the relationship between variability in-process parameters and quality attributes. Key process parameters (KPPs) evaluated during these studies include cell seeding density and virus multiplicity of infection (MOI) as well as temperature and media conditions for the process. These studies, known as “range-finding”, require rapid response to multiple conditions during process characterization and produce far more samples than that is typical of a final manufacturing process. A PAT for high-throughput and immediate, at-line results for viral particle quality and quantity are valuable for rapid response and further iterations of these studies.22. Violet lasers are often utilized as they have the shortest wavelength and thus are scattered with more frequency by sample streams with particles less than or equal to the wavelength. The more scatter produced by running the sample, the easier it is to differentiate from incidental background noise, producing a clean signal. While these standard, commercially available units are advertised as flow cytometers, they can effectively enumerate smaller particles such as viruses and are referred to as flow virometers in this study.However, direct monitoring of virus particles has remained challenging due to their sub-micron sizes. Dynamic light scattering (DLS) as well as flow cytometry have been explored as PAT for direct at-line monitoring of virus particles. Modifications have been made to conventional flow cytometers to enable direct measurement of submicron particles. A number of laboratories have recently reported success in utilizing such customized “flow virometers” as well as newer standard commercial flow cytometers with high-powered violet lasers to detect biological particles smaller than 300 nm, including extracellular vesicles, exosomes, and larger viruses24. The VSV virions are roughly bullet-shaped and 70 nm by 200 nm in size based on cryo-EM images , the recently approved rVSV-ZEBOV-GP LVV. We then evaluated the effects of relevant process parameters on this product using lab scale studies and the utility of flow virometry in detecting damaged virions early in a scale down model of the manufacturing process. ERVEBO is a live, vesicular stomatitis virus (VSV), genetically engineered to express glycoprotein from Zaire Ebolavirus, to elicit a protective immune response in vaccinated individuals  LVV candidate from MSD Research Laboratories. This is a conditionally replication-defective HCMV virus with restored expression of the pentameric complex and a characteristic particle size distribution28. The clarified material is transferred to a vessel for treatment with recombinant Trypsin followed by purification and concentration to produce the bulk DS. The protease treatment creates a distinct conformational change in the surface proteins by clipping the Ebola GP1, removing parts of the surface proteins and increasing viral infectivity similar to the effects of natural cathepsin-mediated cleavage (discussed in28). Per manufacturer specifications, the activity of Trypsin is dependent on maintaining storage conditions, most notably to protect from light and extreme temperature (> 37 °C).The current manufacture of ERVEBO consists of a cell growth phase, followed by viral infection and replication phase, harvest of viral fluids , and clarification via depth filtration Fig. A as descCurrent in-process analytics used for manufacturing ERVEBO include initial cell counts and MOI calculations for virus seeding and a plaque assay potency test on HVF harvest and samples taken before and after the enzyme reaction step Fig. A. A chanThe current ERVEBO manufacturing process involves transfer of the CH material to be heated at 37 °C in presence of recombinant Trypsin to generate RVH. To understand the impact of variability in key process parameters or raw materials on virus quality, we designed the following experimental arms : (i) extended hold time at room temperature of CH before enzyme reaction, (ii) increased heat (> 37 °C) during enzyme reaction, (iii) use of light exposed Trypsin  during enzyme reaction and (iv) performing heated reaction step without adding Trypsin to the reaction mix. For each arm of study, pre and post enzyme reaction material was measured by Simple Western  to detect GP1 cleaving and Apogee and Cytoflex flow virometers were used to determine particle concentrations and assess size distributions. All experimental conditions were compared to baseline  representative control material.For control samples, we detected a > 230 kDa protein band in HVF and CH samples which was replaced by a ~ 100 kDa band in RVH samples , we hypothesized that the appearance of the right shoulder could be due to distortion of individual virus particles and/or virus aggregation upon damage from heat. Since the right shoulder indicated particles of larger size causing increased light scatter, a key hypothesis was heat caused loss of viral membrane integrity resulting in virus particles sticking to each other to form aggregates. To test this hypothesis, we stained control and heated virus particles with an anti-VSV polyclonal antibody with no additional permeabilization of the virus particles. We reasoned that if the virus envelope was intact, the anti-VSV antibody, specific to intra-virus M and N proteins, will not be able to access these proteins and we would get a predominantly unstained, negative population in the flow profile. However, if the virus envelope was damaged the antibody would gain access to the internal virus proteins, resulting in an increase in the positively stained population. Using the previously described arm (ii) experimental samples, we stained and ran RVH samples generated at 45, 50, and 60 °C enzyme reaction on the Cytoflex. We found that control RVH samples had about 8% of anti-VSV antibody positively stained virus particles Fig. .Figure 4To further demonstrate that anti-VSV antibody staining of rVSV-ZEBOV virions is directly related to virus envelope damage, we took control RVH samples and treated them with Tween 20, a known membrane damaging agent. We found that control RVH samples had a negligible population (0.52%) of virus particles that stained positively for anti-VSV Fig. E. The tr7 for control samples at the 1:100 dilution and 8.2 x 106 at 1:1000 dilution was observed, with corresponding average intensity value of 6 x 106 for heat treated samples at similar dilutions. A range of total intensity from 3x108−1 x 104 was seen for control samples at the 1:100 dilution. The corresponding range for heat treated samples diluted at 1:100 was 3 x 106−200. For centerpoint samples at 1:1000 dilution the range of total intensity was 6 x 108−2500 and the corresponding range for heat treated samples at the same dilution was 2 x 106−250  and cell death in the wells infected with control RVH at 1:10 dilution. At the 1:100 dilution and 1:1000 dilutions of the control virus, greater than 80% of Vero cells were infected and stained positively for anti-VSV antibody  in the sample, whereas the shoulders contain dense bodies (particles without capsids) of various sizes formed during HCMV replication. We applied heat and Tween 20 at out of range specifications to detect effects on the virus population. We hypothesized that significant changes in profile shape would occur which can be easily detected with an in-process monitoring tool, similar to the changes seen in ERBEVO. For this, control HVF HCMV samples derived from upstream processing were either treated with 0.05% Tween 20 for 30 min at room temperature or heated to 50 °C for 10 and 30 min respectively. Tween treatment showed a significant shift of the main center peak to the left  and Raman probes. In addition, these instruments can function as standalone plug and play machines and do not require additional infrastructure, that can significantly increase cost. Some of the logistical pros and cons of using these newer process monitoring tools compared to gold standard potency assays is outlined in Supplementary Table The power of PAT to directly monitor virus particles at crucial stages of large-scale manufacturing is its ability to provide enhanced process control and increase process robustness. In addition to providing improved control strategy, such tools, if applied correctly, can be used to avert expensive batch failures. However, flow virometry as a PAT to monitor LVV manufacturing in real time has thus far been an under-utilized application in industry. This is presumably because these tools are fairly new and significant characterization work still remains to be done to understand or establish their full potential. Unlike other more well-established PAT like Raman probes widely used for bio-process monitoringOur study represents a successful application of high throughput flow virometry as a potential PAT to detect conformational changes and subtle virus particle degradation in two unrelated LVV products: the recently approved ERVEBO and the HCMV LVV candidate. In ERVEBO, we focus on the effects of temperature on virus particle quality. Heating is a critical step required in the manufacturing of this product as it results in clipping of the Ebola GP1 protein by protease (here recombinant Trypsin). However, heating also introduces a point of vulnerability in large scale manufacturing and requires precise temperature control over extended time. Here we provide a control strategy around evaluating the effect of variability in heat directly on virus particles. We show that heating at or around 45 °C or higher during the enzyme reaction step can start modifying morphology of a fraction of virions and appears as a small but measurable right shoulder in the virus profile. It is possible that the conformational change in protein upon GP1 clipping also alters refractivity or optical density of the particles so that the light path through the virions is collectively altered and detected by flow cytometry as an altered profile shape. If applied as a PAT on a manufacturing floor, the flow virometry assay used here to detect initial morphological changes in-process can then possibly trigger a corrective measures and save the batch or future batches. The assay is quick and easy and can be done at-line with only microliters of sample, no additional processing and within ten minutes. Samples were run fresh and frozen and no significant differences in counts or profiles were observed between these two, underscoring the ease of use and flexibility of this method. For ERVEBO, a slightly advanced version of the same method was used to further probe the cause for unusual RVH profile in heated samples by staining the virions with virus specific antibodies. The hypothesis is that an intact virion will be impermeable to staining with antibodies against intra-virus proteins without any additional permeabilization steps. Our results showed selective staining of damaged particles with increased heating. Cryo-EM imaging at high resolution showed profound morphological differences between normal and heated virus particles. Though not a high-throughput method, such imaging tools are invaluable in the direct visualization of otherwise “invisible” particles. Traditional plaque potency assays confirm the loss in potency seen using both flow virometry and quantitative imaging assay. It is perhaps not surprising to see an aggravated loss in plaque potency for particles heated at 50 °C, though the percent population of damaged virus is less than a hundred percent at this temperature. This could be a combination of plaque assay sensitivity not being able to detect differences that are within assay noise as well as flow virometry being unable to discriminate between individual “damaged” infectious particles contributing to potency loss versus aggregates of particles that may represent a higher percent damaged population than is currently shown.In addition to developing a potential PAT for virus quality measurement, we have also performed a surrogate, imaging-based infectivity assay that can be used for in-process assessment of virus potency. The high content and high throughput quantitative imaging platform used here offers the distinct advantage of being able to automatically count and quantify thousands of cells within minutes as well as can be multiplexed with additional cellular markers for virus infection. Combined with the PAT for direct virus monitoring, these two technologies can provide virus potency measurements much before release assays and be utilized for further control over continuous bioprocess monitoring.Using HCMV as a model enveloped virus particle, here we explore the effect of two stressors, heat and Tween 20, on virus quality. Tween 20 is a detergent commonly added to buffers and reagents for ELISA chemistry to act as a surfactant and emulsifier. If the concentration of Tween is not carefully monitored, it can destroy lipid bilayer membranes such as those on enveloped viral particles. In our study, this property is used to simulate sample contamination with chemical residues or constituents which could potentially degrade the enveloped particles and result in low potency. We find that heating and Tween 20 treatments, both done for thirty minutes, have separate and distinct effects on the characteristics of enveloped virus particles, demonstrated by alterations in virus profile. The effects seen on upstream and downstream samples are also distinct, potentially due to differences in matrices, where the upstream samples have more cellular material and debris. Both Tween and heat cause the HCMV profile to shift to the left, but the shift is much more pronounced with Tween. With heat, an increase in height of the already existing “left shoulder” in HCMV upstream samples is seen over time, but a complete shift of the center peak of virions and NIEPs is not observed. Presumably, these differences indicate both the extent and kinetics of virus particle damage.As treatment with Tween 20 is harsh, the membrane damage is more severe. Heating causes the same damage, but the kinetics is slower. The difference in profile for same treatments on downstream samples is intriguing. Here, both Tween and heating for 30 min seem to have a more severe effect on virions with the appearance of multiple peaks previously not seen, potentially suggesting sample degradation. As downstream samples are much more concentrated than upstream material and also exist in a very different matrix that is devoid of protection from cell culture media and serum, this result is not unexpected. These studies demonstrate the sensitivity of flow virometry that can differentiate not only between samples based on the specific process stress, but also between the various stages of manufacturing process (upstream or downstream) and can be simply and effectively integrated into existing workflows. No additional sample processing, small sample volumes and quick sample running time remain key enablers to this method.36. To mitigate concerns around a specific platform, we were able to show proof of concept studies using two independent flow virometers that both show the same effect on virus particles, and can both be suitable PAT.LVVs are manufactured worldwide and manufacturing programs exist over many decades. Evaluation of the longevity and product life cycle management of the tool, is therefore, an important aspect in the selection of a new PAT. Currently, multiple flow virometry platforms are being tested for application to small particle measurement including exosomes and virus particlesFinally, and in conclusion, it is critical that fit for purpose analytics are designed to provide near real time monitoring of LVVs in large-scale manufacturing. The goal in doing this is twofold. One, it is significantly cost saving to be able to characterize product quality as early in the process as possible in order to intervene with corrective measures. Second, it is important to be able to have robust process monitoring tools to evaluate the impact of large-scale changes in raw materials on virus seed stocks and production. For many LVV processes it is challenging to assess the effects of a change in poorly characterized raw materials such as serum on product quality and studies implemented to characterize such process and raw material changes are lengthy and may be difficult to interpret. The integrated tools represented in this study provide an advancement opportunity for the rapid and robust monitoring and control of large molecule therapeutics.All virus samples used in this study were derived in MSD laboratories and used in lab scale studies.All virus samples used in this study were generated using scaled down models of the commercial-scale process. Samples were stored at < − 60 °C after generation and thawed at room temperature for testing or further manipulation. Sample treatment conditions with Tween 20 and heat are listed in sample names and were performed immediately prior to testing. The Tween was added in the PBS dilution buffer to specified concentrations and sample thermal treatments were performed in a temperature-controlled heating block.28.The Simple Western assay was run on an automated capillary western blotting instrument produced by ProteinSimple. The instrument emulates the typical western blot workflow in a capillary tube, including separation by molecular weight, treatment with primary and secondary antibodies, and detection by chemiluminescence. This method has been described inPrior to analysis by capillary western blot, ERVEBO samples were diluted up to 5 × depending on anticipated concentration and then added to a mixture containing sodium dodecyl sulfate (SDS), dithiothreitol (DTT), and fluorescent molecular weight standards. After a 10-min incubation at 70 °C, samples are added to a 12–230 kDa sample plate along with 1000 × anti-ZEBOV GP-rabbit primary antibody, anti-rabbit-HRP secondary antibody, and luminol-peroxide.Separation time was set to 30 min, primary antibody incubation was set to 45 min, and secondary incubation time was set to 45 min. Detection performed on chemiluminescent channel. All analysis performed at 2 s exposure time.The Apogee A60 is configured with 405 nm violet laser and forward, side, and green florescence detectors. Apogee refers to traditional forward and side scatter as small-angle light scatter (SALS) and large-angle light scatter (LALS) respectively. The Cytoflex S is in standard configuration with three color lasers and assorted detectors; for the purposes of this study we are concerned primarily with violet side scatter . No customization was required of the equipment after initial set up.Highly filtered sheath fluid; either 0.1 µm deionized water, 0.12 µm Milli-Q water, or purchased Beckman Coulter sheath fluid, were used for all of these studies. Both cytometers were confirmed to be capable of visualizing and enumerating control beads and ERVEBO viral particles with control samples prior to using for experimental conditions.HVF, CH and RVH were diluted in 0.1 µm filtered PBS and run on Apogee and Cytoflex Flow Virometers, dilutions are noted in the sample names on Figures. For Apogee, a flow rate of 1.5 ul/minute was selected and for Cytoflex a flow rate of 10ul/minute was used. Total particle count for samples was measured by multiplying events/second recorded with dilution of the sample and total volume collected during each run. The profile of each sample was recorded and exported as fcs files to plot using FlowJo software.Control polystyrene beads for confirming operation of flow cytometers were sourced through Apogee and utilized per manufacturer recommendations. The Apogee A60 utilizes a traditional photomultiplier tube (PMT) with variable voltage, settings were identical for all profiles which we compared to one another. The laser wattage was set to 270mW to increase resolution for ERVEBO viral particles and set to 70mW for the larger, heterogenous population of HCMV. The Cytoflex S does not utilize a PMT based system in favor of avalanche photodiodes, and laser wattage is not user-variable; our unit was set to 92mW on initial install.CH and RVH samples from control and heated runs were diluted 100X in 0.1 micron filtered PBS and stained with anti-VSV rabbit polyclonal antibody for 30 min at room temperature. The samples were then clarified by passing through CaptoCore column once and then stained with anti-rabbit AF488 secondary antibody for 30 min at room temperature. After this, the samples were once more clarified by CaptoCore column purification and run directly on Cytoflex. To detect membrane damage, positive signal from AF488 was detected on y axis from virus particles gated on violet laser side scatter (VSSC) on x axis.Control and experimental RVH samples were used to infect Vero cells plated overnight in 96 well plates. A serial dilution of viruses in Vero cell media was performed to obtain a dose response infectivity curve. 24 h post infection, virus containing media was removed and cells were permeabilized and fixed using BD Perm Fix solution directly in 96 well plate. Staining of virus in cells was then performed using anti-VSV rabbit polyclonal antibody (Imanis) at a 1:1000 dilution for one hour at 37degC. Secondary staining was performed with anti-rabbit AF568 secondary at 1:200 dilution for 40 min at 37degC. A DAPI counterstain was applied and plates were imaged using Perkin Elmer Operetta CLS high content imager. Image analysis was performed using the Harmony software (PE). Statistical analysis was done on log transformed data using Graph Pad Prism software. A P value of < 0.05 was considered highly significant. P values between 0.1 and 0.05 were considered moderately significant.Supplementary Information


Non-communicable diseases (NCDs) consistently pose a huge economic burden to health systems and countries in general. The aim of this study was to quantify inpatient costs associated with chronic obstructive pulmonary disease, stroke and ischemic heart disease stratified by type of referral pathway, and to investigate key factors that drive these costs.A registry-based data analysis was performed using national public hospital inpatient records from 2016 to 2018 for 117,600 unique patients and linking patient-level inpatient health care use with hospital-specific unit cost per bed-day. These were combined to calculate the annual inpatient costs for each of the three disorders per person and per year. Generalized linear modeling was used to assess the association of inpatient costs with age, gender, location, comorbidity, treatment referral pathways and years.Across three diagnoses, the majority of patients were female. Most were over 50–60 years old, with more than half being a pensioner, typically with at least one comorbidity. About 25% of patients followed what might be considered inappropriate  inpatient referral pathways. Mean annual inpatient costs were int$ 721. These costs rose to int$ 849 for unofficial pathways and dropped to int$677 for official pathways. Further covariates significantly associated with high inpatient costs were location, age, gender, and comorbidity.Our findings provide background information essential to develop evidence-based and cost-effective interventions aimed at health promotion, prevention and service delivery. Reducing the unofficial use of inpatient care can improve efficient resource allocation in health care and prevent further escalation of inpatient costs in the future.The online version contains supplementary material available at 10.1186/s12913-021-07281-8. Mean inpatient costs associated with chronic obstructive pulmonary disease, stroke and ischemic heart disease were significantly higher for unofficial referral pathways compared to official referral pathways.Key drivers of high inpatient costs next to referral pathway were location, age, gender and comorbidity.Factual costs of hospital care for the NCDs considered provide insight in healthcare delivery, and the financial burden of disease for Mongolia and comparable developing countries.The costs associated with non-communicable diseases (NCDs) consistently pose a high economic burden on health systems and countries in general , 2. WithOne of the factors might be hospital referral pathways. Lack, either real or perceived, of quality access to or the capacity of community level hospitals may lead to inefficient treatment pathways, with direct entry into secondary or tertiary levels of care. Higher level facilities are often better equipped and employ more specialized personnel, hence self-referral tends to occur . HoweverIn response, the length of stay for stroke patients has been reduced to about 6 days, while some hospitals transfer patients to lower-level hospitals . PreviouMongolia is a LMIC with a population of 3.3 million , 12. ThePrevious evidence showed that inappropriate hospital admissions accounted for 21.7% for public hospital admissions in the department of internal medicine, while this was even higher for private hospitals (27.66%) in Mongolia .n = 57 participants). Generalizability to countrywide costs may thus be limited for studies performed in LMICs [Inpatient costs for NCDs and specifically for Stroke, Chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IHD) have been shown to vary with several factors. The most studied factors that contribute to high inpatient costs in LMICs were age, gender, comorbidities, length of stay, and type of health care providers –25. Alsoin LMICs , 26. On in LMICs .In Mongolia, no study so far addressed disease-specific inpatient cost, and studied key factors that contribute to high inpatient costs. Stroke, COPD, and IHD are 3 NCDs with large burden of disease in Mongolia. Moreover, their future burden maybe expected to rise, when current trends in lifestyle risk factors like overweight and smoking are not bent towards more healthy pathways.Information on the inpatient costs for NCDs can be used to prioritize prevention policy focusing on risk factors for NCDs. Evidence on key factors that impact on high inpatient costs can help to control the rise of costs due to inefficient service delivery in practice. With lack of disease-specific costing information and key factors influencing these costs, inefficiency in resources allocation may continue to exist. Furthermore, lack of cost estimates may lead to over or under estimated true intervention costs in applied studies and to ignorance of costs drivers.The current study therefore adds to this literature. Using a large patient-level dataset from the national Mongolian hospital inpatient registry, in combination with data from hospital financial accounts, information beyond what has previously been published may be retrieved . The datA ‘real world data’ registry-based cost analysis was performed using national public hospital inpatient records for the years 2016 through 2018 in Mongolia. Two data sources were combined to calculate the population mean costs associated with hospitalization for patients with stroke, chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IHD). First, micro-level hospital registry data was used to analyze the characteristics of inpatient care use across different diagnosis-related groups (DRGs). Second, hospital-specific funding source data were collected from hospital financial reports to calculate the unit cost per bed-day. Public funding sources, that is, direct government funding and health care insurance for each type of hospital, were tracked over time. Inpatient health care use was linked with cost per bed-day in order to quantify the costs per patient per year (PPPY). This was repeated for each of the three conditions. Generalized linear modeling analysis (GLM) was used to relate the PPPY to several covariates reflecting patient characteristics, type of treatment referral pathways and diagnosis.The national inpatient records are managed by the Health Development Center, which is the public authority responsible for collecting, inspecting and annual reporting of national health indicators in Mongolia. The registry uses the international classification of diseases version 10 (ICD-10). We selected the three most prevalent DRGs among the NCDs: COPD (J40-J44), IHD (I20-I25), and stroke (I60-I69).Inpatient record data contains information on demographics , socioeconomic characteristics , medical condition  and type of hospitals . An anonymized patient identification number was used to record any comorbidity as present in a given year. Inpatient care use was estimated at the patient-level rather than per admission only, meaning that for a patient who was admitted to the hospital multiple times or to multiple hospitals, the different episodes of inpatient care costs were summed, to total annual costs for this individual.Laws on transparency in Mongolia, effective as of 2015, all state authorities, including public hospitals, are obliged to report financial information online and make it freely accessible to the public. Hospital financial reports contain information on total funding and funding sources. The extracted data were further validated through expert consultations.Our dataset consisted of all inpatient admissions in Mongolia in the years 2016 to 2018  hospitals, 39 inter-community hospitals , 16 provincial (aimag) general hospitals, 5 regional diagnostic centers, 12 district general hospitals in Ulaanbaatar , and 3 general clinic hospitals (tertiary level care).Patients living in rural and urban areas follow different inpatient care pathways which reflect geographical circumstances Fig. . GeneralThe outcome variable in the analysis was total hospitalization cost per patient per year. Since price levels varied over time, they were adjusted to the price level of 2018, based on the consumer price index for all 3 years in the final analyses. The primary covariates included in the model were gender, age, comorbidities, location of the patient, social status, treatment referral pathways and year. Sex was dichotomized ; age was categorized into ten-year age groups: 0–40, 40–50, 50–60, 60–70, 70–80 and 80 and older; patient location was dichotomized ; and patient comorbidities were identified within the three ICD codes recorded during each hospitalization. Social status was categorized into 5 categories: formal sector salaried employee, private sector employee, herders, pensioners and others, including full-time students and children aged under 15 years. Patient referral pathways were assessed and coded as dummy variables based on each individual patient’s admissions. The dummy variable indicated whether the treatment path was an official or unofficial referral by merging information on referral paths with patient location information over the one-year period.Costs per patient per year were estimated by type of hospitals and diagnosis, using patient-level data for information on number of admissions and number of bed-days, and hospital financial records for hospital-level information on total budgets. To arrive at costs per patient per year, the number of bed-days for each patient and diagnosis were multiplied by the relevant unit costs per bed-day, depending on hospital type and diagnosis. An overview of hospital funding information and share of funding sources are presented in Table SStatistical analyses were performed in R software version 1.2.5033 by RStudio Inc., Boston US, while data preprocessing of hospital accounting information was performed in MS Excel. More detailed information related to the statistical analysis is reported in the supplementary material. Costs were estimated in Mongolian National Tugrik (2472 MNT = 1 USD in 2018) in real terms at base year in 2018. Findings are reported in international dollars (int$) by using purchasing power parity (PPP$) exchange rates for the period 2016 to 2018 .Table SA total of 117.6 thousand unique patients received inpatient health care. , varying from int$ 677 (sd = 372) for official referral pathways to int$ 849 (sd = 502) for unofficial referral pathways. Covariates significantly associated with high inpatient costs were location, age, gender and comorbidity. Our results show that costs per admission over three successive years went down in all hospital types when converting to the real price level in 2018. Although hospital funding increased in real terms, this growth was lower than the inflation rate, and explains the trends in int$ term. Regarding disease-specific inpatient costs, part of the variation in inpatient costs may be explained by variation in the mean length of stay for all three diagnoses. Having comorbidity was associated with high inpatient costs for COPD patients, and less so for stroke and IHD patients. For all three diseases, the main inpatient cost drivers were location, inappropriate referral pathways, older age, and male gender, while social status had only a small effect on the inpatient costs.Our study shows that referral pathways significantly affect inpatient costs and hence highlights the need for more efficient inpatient care delivery by stimulating the role of primary and secondary level hospital care, and enhancing official referral pathways. Similar pathways have been observed in other low- and middle-income countries, where unnecessary referrals lead to 1–2 month delayed diagnosis, ineffective treatment, and poor provider-patient communication for continued care support , 32. In Comparing the costs currently estimated with previous findings in the literature was challenging due to the different perspectives used , different cost components included (direct or indirect) and different phases of disease conditions studied. Keeping this heterogeneity of study designs in mind, internationally, a substantial variation in the mean inpatient costs was observed. For stroke patients, the mean annual inpatient costs per patient in China was int$ 5264 for 27 bed-days in 2013, including out-of-pocket payment (24.2%) . AnotherOur finding that age is positively associated with high inpatient costs is consistent with a study in eight Asian countries,  whereas n = 3) in the capital city, deliver 9500 to 25,000 specialized admissions annually. Ensuring equitable and accessible quality health care for all is the main challenge in the Mongolian health system’s efforts to attain UHC, given the extremely low population density in a large part of its territory. Any unofficial policy decision and improper financial incentive can increase overall health expenditure and inefficient use of available resources in health, but also causes serious financial burdens on patients and families. Recent study demonstrated that patients admitted in public hospitals in Mongolia were more likely to suffer from catastrophic health expenditure, due to high out-of-pocket payments and pushing them into poverty [In rural areas, a total of 277 primary level hospitals were included in our study. Each have approximately 5–15 beds, realizing a total of 150 to 1300 admissions annually. In contrast, tertiary level general hospitals . To progress towards UHC, efficient use of available resources is crucial to controlling constantly increasing costs and growing demand. Previous local evidence found that public spending on NCDs in Mongolia was similar to NCDs spending observed in high-income countries and spending is dominated by inpatient care instead of preventive care . This apAlso, Mongolia has massive opportunities to benefit from proactive prevention and health promoting activities. According to the national survey, one in every three Mongolians has three or more common modifiable risks factors for developing NCDs . In abseThe current study supports policy makers that have to find ways to deal with this rising financial burden in two ways. First, the regression analyses support further action to reduce unofficial referral pathways, by showing that the latter are more expensive than official referral pathways. A caveat here is that a systematic difference might exist between patients using official and unofficial pathways. However, effective measures should be addressed to control escalation of costs associated with inpatient care in Mongolia.Second, the estimated unit costs and costs per disease per year could be applied in cost-effectiveness studies to support prevention policy to reduce the incidence of NCDs or their complications. In that case, it should be kept in mind that our estimates are a lower boundary for actual costs, since out-of-pocket costs and costs made in private hospitals were not included.Our findings thus may inform policy makers on the publicly funded costs for three major NCDs, and could provide useful information to inform future cost-effectiveness studies of disease prevention and health promotion programs related to the three major NCDs in Mongolia and comparable LMICs.The inpatient costs for major NCDs were substantially lower when an official referral pathway was followed and a substantial proportion of the inpatient care use consisted of pathways that may indicate unofficial self-referrals in Mongolia. Reducing the unofficial use of inpatient care can improve the resource allocation in health care and prevent further escalation of inpatient costs in the future.Additionally, this study supports further research into the cost-effectiveness of prevention policies aiming at reducing the burden of NCDs in Mongolia as well as other developing countries.Additional file 1: Supplementary file. Figure S2 Cost per bed-day by types of hospital and years for COPD, in int$ 2016–2018. Figure S3 Cost per bed-day by types of hospital and years for IHD, in int$ 2016–2018. Table S1 Overview of hospital funding mechanisms and share of funding sources. Table S2 Cost weight used to calculate case-based inpatient costs by DRGs. Table S3 Distribution of admissions according to the hospital types and according to diagnosis by year. Table S4 Mean costs per bed-day across different hospital types, in INT$ 2016–2018. Table S5 Characteristics of patients by type of diagnosis, inpatient data 2016–2018. Table S6 Patient referral pathways by diagnosis and year. Table S7 Patient characteristics associated with inpatient costs by age groups, (int$) in 2018. Table S8 Generalized liner model for COPD patients. Table S9 Generalized linear model for stroke patients. Table S10 Generalized linear model for IHD patients.


Biological, physical and chemical interaction between one (or more) microorganisms and a host organism, causing host cell damage, represents an infection. Infection of a plant, animal or microorganism with a virus can prevent infection with another virus. This phenomenon is known as viral interference. Viral interference is shown to result from two types of interactions, one taking place at the cell surface and the other intracellularly. Various viruses use different receptors to enter the same host cell, but various strains of one virus use the same receptor. The rate of virus–receptor binding can vary between different viruses attacking the same host, allowing interference or coinfection. The outcome of the virus–virus–host competition is determined by the Gibbs energies of binding and growth of the competing viruses and host. The virus with a more negative Gibbs energy of binding to the host cell receptor will enter the host first, while the virus characterized by a more negative Gibbs energy of growth will overtake the host metabolic machine and dominate. Once in the host cell, the multiplication machinery is shared by the competing viruses. Their potential to utilize it depends on the Gibbs energy of growth. Thus, the virus with a more negative Gibbs energy of growth will dominate. Therefore, the outcome can be interference or coinfection, depending on both the attachment kinetics (susceptibility) and the intracellular multiplication machinery (permittivity). The ratios of the Gibbs energies of binding and growth of the competing viruses determine the outcome of the competition. Based on this, a predictive model of virus–virus competition is proposed. In 2020, during the COVID-19 pandemic, a surprisingly low number of influenza cases was reported worldwide . Many arViral interference is a phenomenon where a virus can prevent or partially inhibit infection with another virus within the same host ,5,6, impvirus–host competitive interaction, there can also be virus–virus competitive interactions [Various living species surround and interact with each other. Infection represents a biological, physical and chemical interaction between microorganisms and a host organism, leading to host cell damage ,9,10. Viractions . When mu(a) cooperative forms (co-infections), or(b) competitive forms (interference) of virus–virus–host interactions .Co-infections can occur when two or more pathogens are in circulation and have the same seasonal pattern or overlap [Competitive forms of virus–virus–host interactions (interference) occur when two or more viruses are in circulation but one virus interacts more efficiently with the host and, thus, prevents the other virus from infecting the same host [Mixed infections of viruses are not uncommon, and complex pathophysiological changes can result from such infections . Co-infe overlap . Competiame host , or whenInteraction between two viruses in one host organism can be considered at two levels:(1) Macroscopic—considering virus–virus–host interaction within a population as an epidemiological phenomenon.(2) Microscopic—considering virus–virus–host interaction within a cell/tissue/organism as a chemical, physical and biological phenomenon.Both approaches are necessary to analyze virus–virus–host interactions. Epidemiological data on the spreading of two or more viruses during a season can indicate that there is interference between viruses. The microscopic approach can offer an explanation of the mechanism that leads to interference. Thus, the microscopic and macroscopic approaches are complementary.Interference has been known for a long time on a macroscopic, epidemiological level. Epidemiological data are readily available and have been collected for many years, making them more accessible than microscopic data that have to be collected in expensive experiments. Except for the high price, experiments are also hindered by difficulties in obtaining samples that are pure enough in amounts that are sufficient for analysis.Spreading of a virus can be interrupted by an epidemic of another virus . Dietz  discusseThe evidence from recent surveillance data in Hong Kong suggests that viral interference during the epidemics of influenza viruses and other common respiratory viruses might affect the timing and duration of subsequent epidemics of certain or several viruses . Three rViral interference represents, at the microscopic level, the inhibition of viral reproduction (and host damage) caused by previous exposure of susceptible cells to another virus . InfectiViruses compete not only for host metabolic machinery, but also to enter the cell first. Spike proteins of both SARS-CoV-2 and SARS-CoV bind to ACE2 before entering the cell for replication. Wrapp et al.  suggesteEven though much information has been gathered on interference, many things remain unknown. It is unknown whether the outcome of interference is determined by virus attachment and entry, or intracellular multiplication ,28,29,30Virus competition occurs at the cell surface and in the cytoplasm, as summarized in In mixed infection, two different viruses use different receptors to enter the host cell, but they compete for one metabolic machinery for their multiplication, as well as for resources. The two viruses must also compete with the host cell for its own metabolic pathways, but its Gibbs energy of growth is much less negative than that of the viruses ,10, implThe aim of this paper is to explore interference at the microscopic level and try to shed light on the molecular mechanism of interference. The biothermodynamic approach will be used to analyze the pathogen–pathogen–host interactions (interference and coinfection) between various virus species that potentially cause infections and epidemics in humans. Viral interference may be not one phenomenon, but many . Thus, iThis study analyzes the virus–virus interactions of human respiratory viruses. The considered viruses include adenovirus, influenza, parainfluenza, rhinovirus and SARS-CoV-2. The analysis was conducted, taking into account virus pairs, by comparing the thermodynamic properties of binding and growth.Gibbs energies of virus–receptor binding were calculated from binding constants, which, in turn, were calculated from dissociation constants reported in the literature. The strength of virus attachment to host cell receptors is often expressed in terms of dissociation constants , modelinbK, using the equationdK is the dissociation constant, while onk and offk are kinetic rate constants that account for the forward binding and reverse unbinding (or dissociation) reaction, respectively [bG0, through the equationR is the universal gas constant and T is the temperature. The ΔbG0 values were calculated at the physiological temperature of 37 °C (310.15 K).The dissociation constants were used to find binding constants, ectively . FinallyiM is the molar mass in the empirical formula of molecular component i, and ix and iw are the mole fraction and mass fraction of molecular component i, respectively. The summation is over all molecular components. The mole fractions of molecular components were then used to find the empirical formula of viruses using the equationJn is the number of moles of element J in the empirical formula of the biomass, and i,Jn is the number of moles of element J in the empirical formula of molecular component i. The information about molecular components of the virus, including i,Jn and iM, is given in the The elemental compositions of all viruses, except SARS-CoV-2, were calculated based on molecular composition, as described in . MoleculnHOnONnNPnPSnS. The elemental compositions of the analyzed viruses are given in The elemental composition of SARS-CoV-2 was calculated through the atom counting method. The atom counting method gives the elemental composition of any virus based on its nucleic acid sequence, protein sequences and morphology, as described in ,10. The The elemental compositions of the viruses were used to determine their thermodynamic properties, as described in ,10,54. TE, using Equation (5) [CH0, through the Patel–Erickson equation (Equation (6)) [fH0(Bio), using Hess’ law (Equation (7)) and the standard thermodynamic properties of oxides [m0S(Bio), was calculated using the Battley equation (Equation (8)) [fS0(Bio), (Equation (9)) [fH0(Bio) and ΔfS0(Bio) were combined to find the standard Gibbs energy of formation of virus live matter, ΔfG0(Bio), through Equation (10). Thermodynamic properties of virus live matter are given in the The enthalpy of the viruses was determined using the Patel–Erickson equation and Hess’ law. First, the elemental compositions of the viruses were used to find the number of electrons transferred to oxygen during complete combustion, tion (5) ,56. The ion (6)) ,56. The f oxides ,58,59. Sion (8)) . The Bation (9)) . Finally1.7978O0.4831N0.2247S0.0225) as the carbon, nitrogen and sulfur source, O2 as the electron acceptor, HPO42- as the phosphorus source, and HCO3-/H2CO3 buffer the as the pH regulator. More information about the nutrients can be found in the The thermodynamic properties of the growth of viruses were determined as described in ,10,54. TThe determined thermodynamic properties had an uncertainty that originated from the model used for their estimation. The Pate–Erickson equation gave the standard enthalpy of combustion of with an accuracy of 5.36% . The samThe interaction between a virus (system A) and its host cell (system C) is a simple two-system interaction. However, if a single cell (system C) is in contact with virus A (system A) and virus B (system B), the interaction becomes complex, consisting of two simultaneous simple interactions: A–C and B–C. Thus, virus A and virus B interact through a mediator—the host cell—with which they interact simultaneously, making the interaction transitive. The interaction can lead to three outcomes: co-infection, asymmetrical co-infection and interference, as shown in Two viruses infecting the same host interact at two sites, the cell membrane and the cytoplasm, as summarized in T is the temperature, pr is the rate of process p, pL is a phenomenological coefficient for process p, and ΔpG is the Gibbs energy change for process p [rr, is proportional to Gibbs energy of growth, ΔrG, of the virus [br, can be quantified by the Gibbs energy of binding, ΔbG [The rates of both entrance and multiplication of viruses are proportional to their driving forces—Gibbs energy dissipation. The rate of a process depends on both thermodynamic and kinetic factors, which are summarized by the linear phenomenological equationrocess p ,64. The rocess p ,64,65,66rocess p ,10. The he virus ,10. Thusing, ΔbG ,40,46,62ing, ΔbG ,64. At ting, ΔbG ,40,46. TbH, and entropies, ΔbS, of binding reported in the literature, using the equation ΔbG = ΔbH − T ΔbS; (C) directly from the literature. Gibbs energies of binding are of the same order of magnitude, but slightly differ between various virus species. If two viruses simultaneously infect the same host, the virus with a more negative Gibbs energy of binding attaches and enters the host cell more efficiently. This head start allows one virus to hijack the host cell metabolic machinery more quickly, giving it an advantage in terms of multiplication.Viruses are obligate intracellular parasites . Before Except for receptor binding, interference also depends on the ability of the two viruses to multiply inside the cell—permittivity. Permittivity is characterized by the Gibbs energy of growth ,10. The Gibbs energies of growth determine multiplication rates of various viruses, according to the linear equation, because viral multiplication consists of several chemical processes. Thus, they influence virus–virus–host interactions (interference). A virus with a lower Gibbs energy of growth (multiplication) is able to suppress the multiplication of another virus. Similarly to gladiators in the arena, viruses interact competitively inside cells, fighting for survival. The virus with lower Gibbs energies of binding and growth dominates. Whether the interaction of two viruses will result in co-infection or interference depends on the Gibbs energies of binding and growth, which characterize both viruses. The discussion below is about the outcomes of interactions of various virus pairs on a single host. The thermodynamic properties and outcomes on which the analysis is based are summarized in Rhinovirus vs. SARS-CoV-2: The rhinovirus interacts with SARS-CoV-2 at the cell surface receptor and at the intracellular level. In simultaneous infection by both viruses, the rhinovirus enters the host cell at a lower rate, since the Gibbs energy of binding of the rhinovirus is −28 to −39 kJ/mol, while that of SARS-CoV-2 is −42.2 to −51.4 kJ/mol. However, the rhinovirus multiplies at a higher rate, since the Gibbs energy of growth of the rhinovirus is −197 kJ/mol, while that of SARS-CoV-2 is −165 kJ/mol. Since the SARS-CoV-2 has a more negative Gibbs energy of binding, it will enter host cells faster than the rhinovirus. Even though SARS-CoV-2 enters host cells faster due to its more negative Gibbs energy of binding, the rhinovirus will be able to hijack cell metabolism more effectively, due to its more negative Gibbs energy of growth. This implies that during infection, both viruses will be present in multicellular organisms. This conclusion is supported by the observation that, in 2020, rhinovirus was able to constitute an epidemic in parallel with the SARS-CoV-2 pandemic [Influenza vs. SARS-CoV-2: SARS-CoV-2 dominates over influenza, due to its having more negative Gibbs energies of binding and growth. In simultaneous infection by both viruses, the influenza virus enters the host cell at a lower rate, since the Gibbs energy of binding of the influenza virus is −32.6 to −33.7 kJ/mol, while that of SARS-CoV-2 is −42.2 to −51.4 kJ/mol. The influenza virus multiplies at a lower rate because its Gibbs energy of growth is −83 to −87 kJ/mol, while that of SARS-CoV-2 is −165 kJ/mol. As a result, the influenza virus enters host cells more slowly and multiplies more slowly. Therefore, SARS-CoV-2 dominates in the population and the outcome is interference. Indeed, the incidence of influenza reported to the WHO was low in 2020 [Influenza and SARS-CoV-2 use different receptors for cell entry: sialic acid and ACE2, respectively. Sialic acid is more prevalent than ACE2, but the binding affinity of the S-protein of SARS-CoV-2 to ACE2 (−42.2 to −51.4 kJ/mol) is stronger than that of influenza for sialic acid (−32.6 to −33.7 kJ/mol). Thus, even though sialic acid is more prevalent than ACE2, SARS-CoV-2 has an advantage over influenza due to its more negative Gibbs energy of binding. Moreover, since SARS-CoV-2 and the influenza virus both infect cells of the respiratory system, they compete for resources . Having Adenovirus vs. SARS-CoV-2: In infection by both viruses, the adenovirus enters the host cell at approximately the same rate as SARS-CoV-2 since their Gibbs energies of binding are very similar . Adenovirus performs multiplication at a lower rate, since its Gibbs energy of growth is −144 kJ/mol, while that of SARS-CoV-2 is −165 kJ/mol. In case of a simultaneous infection, both viruses should circulate, but SARS-CoV-2 should dominate. This conclusion is also supported by observation. In New South Wales, the detection of adenoviruses held relatively steady throughout the southern winter, rather than crashing, as was the case for flu, or surging, as was observed for the rhinovirus [inovirus .Parainfluenza vs. SARS-CoV-2: In this case, the influenza virus enters the host cell at a lower rate, since the Gibbs energy of binding of the parainfluenza virus is −24.0 to −27.8 kJ/mol, while that of SARS-CoV-2 is −42.2 to −51.4 kJ/mol. The parainfluenza virus multiplies at a lower rate, because its Gibbs energy of growth is −86 kJ/mol, while that of SARS-CoV-2 is −165 kJ/mol. In that case, the parainfluenza virus enters host cells more slowly and multiplies more slowly. Therefore, SARS-CoV-2 dominates in the population and the outcome is interference. Cases of COVID-19 and parainfluenza-4 virus co-infection are very rare [ery rare .Influenza vs. adenovirus: The influenza virus enters the host cell at a lower rate because the Gibbs energy of binding of the influenza virus is −32.6 to −33.7 kJ/mol, while that of the adenovirus is −45.1 to −45.7 kJ/mol. The influenza virus performs multiplication at a lower rate, since the Gibbs energy of growth of the influenza virus is −83 to −87 kJ/mol, while that of the adenovirus is −165 kJ/mol. Thus, the adenovirus should dominate in case both viruses are present and the outcome is interference. This conclusion is supported by the results of Nickbakhsh et al. [Influenza vs. parainfluenza: This pair is very interesting, since both viruses have similar thermodynamic properties. In case of infection with both viruses, the parainfluenza virus enters the host cell at a lower rate because the Gibbs energy of binding of the influenza virus is −32.6 to −33.7 kJ/mol, while that of parainfluenza is −24.0 to −27.8 kJ/mol. Both viruses multiply at the same rate, since the Gibbs energy of growth of the influenza virus is −83 to −87 kJ/mol, while that of parainfluenza is −86 kJ/mol. Thus, since the rate of entrance of the influenza virus is slightly greater, a small domination of influenza is expected if both viruses are present, and the expected outcome is co-infection. This result is supported by the observation of Goto et al., [ et al., , who cla et al.,  reportedParainfluenza 1 vs. parainfluenza 2: The parainfluenza 1 and parainfluenza 2 viruses perform co-infection, due to their similar Gibbs energies of binding and growth. In case of simultaneous infection by both viruses, both of the parainfluenza viruses enter host cells at approximately the same rate, because the Gibbs energy of binding of the parainfluenza 1 virus is −24.0 to −27.8 kJ/mol and that of parainfluenza 2 is −21.7 to −30.4 kJ/mol. Both viruses multiply at the same rate, because the Gibbs energy of growth of both parainfluenza viruses is −86 kJ/mol. Thus, the expected outcome is co-infection. This result is in agreement with that of Nickbakhsh et al. [Rhinovirus vs. influenza: The rhinovirus dominates over the influenza virus due to its more negative Gibbs energies of binding and growth. The influenza virus interacts with the rhinovirus. In simultaneous infection with both viruses, the influenza virus enters host cells at a lower rate, because the Gibbs energy of binding of the influenza virus is −32.6 to −33.7 kJ/mol and that of the rhinovirus is −28 to −39 kJ/mol [9 kJ/mol . The inf9 kJ/mol  and Pink9 kJ/mol . In Euro9 kJ/mol . Pinky a9 kJ/mol  found thRhinovirus vs. parainfluenza: The rhinovirus has a Gibbs energy of binding of −28 to −39 kJ/mol [9 kJ/mol , while t9 kJ/mol .Interference in respiratory virus infections was analyzed by Pinky and Dobrovolny . Five viCoronaviridae [Coronaviridae are characterized by very similar Gibbs energies of binding and growth, they are able to co-infect.Nowak et al.  found thaviridae . ObviousThe analysis above implies that if two viruses are present in circulation, the outcome depends on their Gibbs energies of binding and growth, as is summarized in If two viruses have similar Gibbs energies of binding and growth, the outcome is co-infection, since none of the viruses has an advantage. An example is parainfluenza 1 vs. parainfluenza 2. Similarly, if one virus has a more negative Gibbs energy of binding, while the other has a more negative Gibbs energy of growth, the outcome is again co-infection, since again no virus has an advantage. An example is rhinovirus vs. SARS-CoV-2. On the other hand, if both viruses have the same Gibbs energy of growth, but one virus has a more negative Gibbs energy of binding, both viruses will be present, but the one with the advantage will dominate. A situation where two viruses are present but one dominates is asymmetrical co-infection. Similarly, if both viruses have the same Gibbs energy of binding, but one has a more negative Gibbs energy of growth, the result will again be an asymmetric co-infection. Finally, if one virus has a more negative Gibbs energy of binding and a more negative Gibbs energy of growth, it will prevent the multiplication of the other virus and the result will be interference.Virus–host and virus–virus–host interactions at the population level represent an epidemiological phenomenon. At the organism level, they represent a biological phenomenon. At the microorganism level, they represent a microbiological phenomenon. However, at the molecular level, pathogen–host and pathogen–pathogen–host interactions represent a chemical phenomenon that can be explained with a biothermodynamic mechanism. The interaction of viruses on the cell surface with their appropriate receptors can be considered as a chemical process, the driving force of which is the Gibbs energy of binding. Virus multiplication, which includes transcription, translation and self-assembly, also represents a chemical process, with the Gibbs energy of growth as its driving force. Thus, biothermodynamics can describe the mechanism of appearance of infection and interference of viruses. A virus with more negative Gibbs energies can more efficiently enter host cells and multiply. The paper gives an explanation of the observation that some viruses enter cells at greater rate or multiply at greater rate. A table was given summarizing the possible outcomes of pathogen–pathogen–host interactions.Virus–host interactions are direct, while virus–virus–host interactions are indirect (transitive), occurring through the host as a mediator. The first is determined by the Gibbs energies of growth of the virus and host, while the second is determined by the Gibbs energies of binding and growth of the two viruses. The Gibbs energies of binding were calculated starting from dissociation constants, or from enthalpies and entropies of binding. The Gibbs energies of growth were calculated starting from virus molecular composition, or from capsid morphology, protein sequences and nucleic acid sequences. The Gibbs energies of binding and growth are the driving forces of virus entry into, and multiplication inside, the host cell, respectively, as can be seen from the linear phenomenological Equation (15). Thus, a virus with a more negative Gibbs energy of binding has an advantage over competing viruses when entering the host cell. However, once inside the cell, a virus with a more negative Gibbs energy of growth has an advantage over competing viruses when hijacking the host cell metabolism. Therefore, the outcome of the virus–virus competition depends on their Gibbs energies of binding and growth.The predicted virus–virus–host interaction results were validated by means of comparison with known outcomes from the literature. The predictions and literature outcomes were in good agreement. Therefore, since the model requires no direct interaction data, it is predictive.