Genetic markers are used for in modern population genetics/genomics to uncover the past neutral and selective history of population and species. Besides Single Nucleotide Polymorphisms (SNPs) obtained from whole genome data, microsatellites (or Short Tandem Repeats, SSR) have been common markers of choice in numerous population genetics studies of non-model species with large sample sizes [1]. Microsatellites can be used to uncover and draw inference of the past population demography (e.g. expansion, decline, bottlenecks…), population split, population structure and gene flow, but also life history traits and modes of reproduction (e.g. [2,3]). These markers are widely used in conservation genetics [4] or to study parasites or disease vectors [5]. Microsatellites do show higher mutation rate than SNPs increasing, on the one hand, the statistical power to infer recent events (for example crop domestication, [2,3]), while, on the other hand, decreasing their statistical power over longer time scales due to homoplasy [6].
To perform such analyses, however, an excellent and reliable quality of data is required. As emphasized in the article by De Meeûs et al. [7] three main issues do bias the observed heterozygosity at microsatellites: null alleles, short allele dominance (SAD) and stuttering. These originates from poor PCR amplification. As a result, an excess of homozygosity is observed at the microsatellite loci leading to overestimation of the variation statistics FIS and FST as well as increased linage disequilibrium (LD). For null alleles, several methods and software do help to reduce the bias, and in the present study, De Meeûs et al. [7] propose a way to tackle issues with SAD and stuttering.
The authors study a dataset consisting of 387 samples from 61 subsamples genotyped at nine loci of the species Ixodes scapularis, i.e. ticks transmitting the Lyme disease. Based on correlation methods and FST, FIS they can uncover null alleles and SAD. Stuttering is detected by evaluating the heterozygote deficit between alleles displaying a single repeat difference. Without correction, six loci are affected by one of these amplification problems generating a large deficit of heterozygotes (measured by significant FIS and FST) remaining so after correction for the false discovery rate (FDR). These results would be classically interpreted as a strong Wahlund effect and/or selection at several loci.
After correcting for null alleles, the authors apply two novel corrections: 1) a re-examination of the chromatograms reveals previously disregarded larger alleles thus decreasing SAD, and 2) pooling alleles close in size decreasing stuttering. The corrected dataset shows then a significant excess of heterozygotes as could be expected in a dioecious species with strong population structure. The FDR correction removes then the significant excess of homozygotes and LD between pairs of loci. FST on the cured dataset is used to demonstrate the strong population structure and small effective subpopulation sizes. This is confirmed by a clustering analysis using discriminant analysis of principal components (DAPC).
While based on a specific dataset of ticks from different populations sampled across the USA, the generality of the authors’ approach is presented in Figure 6 in which they provide a step by step flowchart to cure microsatellite datasets from null alleles, SAD and stuttering. Several criteria based on FIS, FST and LD between loci are used as decision keys in the flowchart. An excel file is also provided as help for the curation steps. This study and the proposed methodology are thus extremely useful for all population geneticists working on non-model species with large number of samples genotyped at microsatellite markers. The method not only allows more accurate estimates of heterozygosity but also prevents the thinning of datasets due to the removal of problematic loci. As a follow-up and extension of this work, an exhaustive simulation study could investigate the influence of these data quality issues on past demographic and population structure inference under a wide range of scenarios. This would allow to quantify the current biases in the literature and the robustness of the methodology devised by De Meeûs et al. [7].

References

[1] Jarne, P., and Lagoda, P. J. (1996). Microsatellites, from molecules to populations and back. Trends in ecology & evolution, 11(10), 424-429. doi: 10.1016/0169-5347(96)10049-5
[2] Cornille, A., Giraud, T., Bellard, C., Tellier, A., Le Cam, B., Smulders, M. J. M., Kleinschmit, J., Roldan-Ruiz, I. and Gladieux, P. (2013). Postglacial recolonization history of the E uropean crabapple (Malus sylvestris M ill.), a wild contributor to the domesticated apple. Molecular Ecology, 22(8), 2249-2263. doi: 10.1111/mec.12231
[3] Parat, F., Schwertfirm, G., Rudolph, U., Miedaner, T., Korzun, V., Bauer, E., Schön C.-C. and Tellier, A. (2016). Geography and end use drive the diversification of worldwide winter rye populations. Molecular ecology, 25(2), 500-514. doi: 10.1111/mec.13495
[4] Broquet, T., Ménard, N., & Petit, E. (2007). Noninvasive population genetics: a review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conservation Genetics, 8(1), 249-260. doi: 10.1007/s10592-006-9146-5
[5] Koffi, M., De Meeûs, T., Séré, M., Bucheton, B., Simo, G., Njiokou, F., Salim, B., Kaboré, J., MacLeod, A., Camara, M., Solano, P., Belem, A. M. G. and Jamonneau, V. (2015). Population genetics and reproductive strategies of African trypanosomes: revisiting available published data. PLoS neglected tropical diseases, 9(10), e0003985. doi: 10.1371/journal.pntd.0003985
[6] Estoup, A., Jarne, P., & Cornuet, J. M. (2002). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology, 11(9), 1591-1604. doi: 10.1046/j.1365-294X.2002.01576.x
[7] De Meeûs, T., Chan, C. T., Ludwig, J. M., Tsao, J. I., Patel, J., Bhagatwala, J., and Beati, L. (2019). Deceptive combined effects of short allele dominance and stuttering: an example with Ixodes scapularis, the main vector of Lyme disease in the USA. bioRxiv, 622373, ver. 4 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/622373

In partially clonal organisms, genetic markers are often used to characterize the genotypic diversity of populations and infer thereof the relative importance of clonal versus sexual reproduction. Most studies report a measure of genotypic diversity based on a ratio, R, of the number of distinct multilocus genotypes over the sample size, and qualitatively interpret high / low R as indicating the prevalence of sexual / clonal reproduction. However, a theoretical framework allowing to quantify the relative rates of clonal versus sexual reproduction from genotypic diversity is still lacking, except using temporal sampling. Moreover, R is intrinsically highly dependent on sample size and sample design, while alternative measures of genotypic diversity are more robust to sample size, like D*, which is equivalent to the Gini-Simpson diversity index applied to multilocus genotypes. Another potential indicator of reproductive strategies is the inbreeding coefficient, Fis, because population genetics theory predicts that clonal reproduction should lead to negative Fis, at least when the sexual reproduction component occurs through random mating. Taking advantage of this prediction, Arnaud-Haond et al. [1] reanalysed genetic data from 165 populations of four partially clonal seagrass species sampled in a standardized way. They found positive correlations between Fis and both R and D* within each species, reflecting variation in the relative rates of sexual versus clonal reproduction among populations. Moreover, the differences of mean genotypic diversity and Fis values among species were also consistent with their known differences in reproductive strategies. Arnaud-Haond et al. [1] also conclude that previous works based on the interpretation of R generally lead to underestimate the prevalence of clonality in seagrasses. Arnaud-Haond et al. [1] confirm experimentally that Fis merits to be interpreted more properly than usually done when inferring rates of clonal reproduction from population genetics data of species reproducing both sexually and clonally. An advantage of Fis is that it is much less affected by sample size than R, and thus should be more reliable when comparing studies differing in sample design. Hence, when the rate of clonal reproduction becomes significant, we expect Fis < 0 and D* < 1. I expect these two indicators of clonality to be complementary because they rely on different consequences of clonality on pattern of genetic variation. Nevertheless, both measures can be affected by other factors. For example, null alleles, selfing or biparental inbreeding can pull Fis upwards, potentially eliminating the signature of clonal reproduction. Similarly, D* (and other measures of genotypic diversity) can be low because the polymorphism of the genetic markers used is too limited or because sexual reproduction often occurs through selfing, eventually resulting in highly similar homozygous genotypes.
The work of Arnaud-Haond et al. [1] shows that the populations genetics of partially clonal organisms should be better studied, an endeavour encompassed in a companion paper using numerical simulations [2]. A further step that remains to be accomplished is to build a mathematical framework for developing estimators of rates of clonal versus sexual reproduction based on genotypic diversity.

References

[1] Arnaud-Haond, S., Stoeckel, S., and Bailleul, D. (2019). New insights into the population genetics of partially clonal organisms: when seagrass data meet theoretical expectations. ArXiv:1902.10240 [q-Bio], v6 peer-reviewed and recommended by Peer Community in Evolutionary Biology. Retrieved from http://arxiv.org/abs/1902.10240
[2] Stoeckel, S., Porro, B., and Arnaud-Haond, S. (2019). The discernible and hidden effects of clonality on the genotypic and genetic states of populations: improving our estimation of clonal rates. ArXiv:1902.09365 [q-Bio], v4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. Retrieved from http://arxiv.org/abs/1902.09365

The evolution of mutualistic symbiosis is a puzzle that has fascinated evolutionary ecologist for quite a while. Data on transitions between symbiotic bacterial ways of life has evidenced shifts from mutualism towards parasitism and vice versa (Sachs et al., 2011), so there does not seem to be a strong determinism on those transitions. From the host’s perspective, mutualistic symbiosis implies at the very least some form of immune tolerance, which can be costly (e.g. Sorci, 2013). Empirical approaches thus raise very important questions: How can symbiosis turn from parasitism into mutualism when it seemingly needs such a strong alignment of selective pressures on both the host and the symbiont? And yet why is mutualistic symbiosis so widespread and so important to the evolution of macro-organisms (Margulis, 1998)?

While much of the theoretical literature on the evolution of symbiosis and mutualism has focused on either the stability of such relationships when non-mutualists can invade the host-symbiont system (e.g. Ferrière et al., 2007) or the effect of the mode of symbiont transmission on the evolutionary dynamics of mutualism (e.g. Genkai-Kato and Yamamura, 1999), the question remains whether and under which conditions parasitic symbiosis can turn into mutualism in the first place. Earlier results suggested that spatial demographic heterogeneity between host populations could be the leading determinant of evolution towards mutualism or parasitism (Hochberg et al., 2000). Here, Ledru et al. (2022) investigate this question in an innovative way by simulating host-symbiont evolutionary dynamics in a spatially explicit context. Their hypothesis is intuitive but its plausibility is difficult to gauge without a model: Does the evolution towards mutualism depend on the ability of the host and symbiont to evolve towards close-range dispersal in order to maintain clusters of efficient host-symbiont associations, thus outcompeting non-mutualists?

I strongly recommend reading this paper as the results obtained by the authors are very clear: competition strength and the cost of dispersal both affect the likelihood of the transition from parasitism to mutualism, and once mutualism has set in, symbiont trait values clearly segregate between highly dispersive parasites and philopatric mutualists. The demonstration of the plausibility of their hypothesis is accomplished with brio and thoroughness as the authors also examine the conditions under which the transition can be reversed, the impact of the spatial range of competition and the effect of mortality. Since high dispersal cost and strong, long-range competition appear to be the main factors driving the evolutionary transition towards mutualistic symbiosis, now is the time for empiricists to start investigating this question with spatial structure in mind.

References

Ferrière, R., Gauduchon, M. and Bronstein, J. L. (2007) Evolution and persistence of obligate mutualists and exploiters: competition for partners and evolutionary immunization. Ecology Letters, 10, 115-126. https://doi.org/10.1111/j.1461-0248.2006.01008.x

Genkai-Kato, M. and Yamamura, N. (1999) Evolution of mutualistic symbiosis without vertical transmission. Theoretical Population Biology, 55, 309-323. https://doi.org/10.1006/tpbi.1998.1407

Hochberg, M. E., Gomulkiewicz, R., Holt, R. D. and Thompson, J. N. (2000) Weak sinks could cradle mutualistic symbioses - strong sources should harbour parasitic symbioses. Journal of Evolutionary Biology, 13, 213-222. https://doi.org/10.1046/j.1420-9101.2000.00157.x

Ledru L, Garnier J, Rohr M, Noûs C and Ibanez S (2022) Mutualists construct the ecological conditions that trigger the transition from parasitism. bioRxiv, 2021.08.18.456759, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.08.18.456759

Margulis, L. (1998) Symbiotic planet: a new look at evolution, Basic Books, Amherst.

Sachs, J. L., Skophammer, R. G. and Regus, J. U. (2011) Evolutionary transitions in bacterial symbiosis. Proceedings of the National Academy of Sciences, 108, 10800-10807. https://doi.org/10.1073/pnas.1100304108

Sorci, G. (2013) Immunity, resistance and tolerance in bird–parasite interactions. Parasite Immunology, 35, 350-361. https://doi.org/10.1111/pim.12047

Understanding the factors that shape the virome of a host is key to understanding virus ecology and evolution (Obbard, 2018; French & Holmes, 2020). There is still much to learn about the diversity and distribution of viruses in a host community (Wille et al., 2019; Chen et al., 2023). The viruses of parasitoid wasps are well studied, and their viruses, or integrated viral genes, are known to suppress their insect host’s immune response to enhance parasitoid survival (Herniou et al., 2013; Coffman et al., 2022). Likewise, the insect virome is being increasingly well studied (Shi et al., 2016), with the virome of Drosophila species being particularly well characterised over the best part of the last century (L'Heritier & Teissier, 1937; L'Heritier, 1970; Brun & Plus, 1980; Longdon et al., 2010; Longdon et al., 2011; Longdon et al., 2012; Webster et al., 2015; Webster et al., 2016; Medd et al., 2018; Wallace et al., 2021). However, the viromes of parasitoids and their insect host communities have been less well studied (Leigh et al., 2018; Caldas-Garcia et al., 2023), and the inherent connectivity between parasitoids and their hosts provides an interesting system to study virus host range and cross-species transmission.

Here, Varaldi et al (Varaldi et al., 2024) have examined the viruses associated with a community of nine Drosophilidae hosts and six parasitoids. Using both RNA and DNA sequencing of insects reared for two generations, they selected viruses that are maintained in the lab either via vertical transmission or contamination of rearing medium. From 55 pools of insects they found 53 virus-like sequences, 37 of which were novel. Parasitoids were host to nearly twice as many viruses as their Drosophila hosts, although they note this could be due to differences in the rearing temperatures of the hosts.  

They next quantified if species, year, season, or location played a role in structuring the virome, finding only a significant effect of host species, which explained just over 50% of the variation in virus distribution. No evidence was found of related species sharing more similar virus communities. Although looking at a limited number of species, this suggests that these viruses are not co-speciating or preferentially host switching between closely related species.

Finally, they carried out crosses between lines of the parasitoid Leptopilina heterotoma that were infected and uninfected for a novel Iflavirus found in their sequencing data.  They found evidence of high levels of maternal transmission and lower level horizontal transmission between wasp larvae parasitising the same host. No evidence of changes in parasitoid-induced mortality, developmental success or the sex ratio was found in iflavirus-infected parasitoids. Interestingly individuals infected with this RNA virus also contained viral DNA, but this did not appear to be integrated into the wasp genome.

Overall, this work has taken the first steps in examining the community structure of the virome of parasitoids together with their Drosophilidae hosts. This work will not doubt stimulate follow-up studies to explore the evolution and ecology of these novel virus communities.

References

Brun G, Plus N (1980) The viruses of Drosophila. In: The genetics and biology of Drosophila eds Ashburner M & Wright TRF), pp. 625-702. Academic Press, New York.
 
Caldas-Garcia GB, Santos VC, Fonseca PLC, de Almeida JPP, Costa MA, Aguiar ERGR (2023) The Viromes of Six Ecosystem Service Provider Parasitoid Wasps. Viruses, 15. https://doi.org/10.3390/v15122448
 
Chen YM, Hu SJ, Lin XD, Tian JH, Lv JX, Wang MR, Luo XQ, Pei YY, Hu RX, Song ZG, Holmes EC, Zhang YZ (2023) Host traits shape virome composition and virus transmission in wild small mammals. Cell, 186, 4662-4675 e4612. https://doi.org/10.1016/j.cell.2023.08.029
 
Coffman KA, Hankinson QM, Burke GR (2022) A viral mutualist employs posthatch transmission for vertical and horizontal spread among parasitoid wasps. Proceedings of the National Academy of Sciences of the United States of America, 119. https://doi.org/10.1073/pnas.2120048119
 
French RK, Holmes EC (2020) An Ecosystems Perspective on Virus Evolution and Emergence. Trends in Microbiology, 28, 165-175. https://doi.org/10.1016/j.tim.2019.10.010
 
Herniou EA, Huguet E, Thézé J, Bézier A, Periquet G, Drezen JM (2013) When parasitic wasps hijacked viruses: genomic and functional evolution of polydnaviruses. Philosophical Transactions of the Royal Society B-Biological Sciences, 368. https://doi.org/10.1098/rstb.2013.0051
 
L'Heritier PH (1970) Drosophila viruses and their role as evolutionary factors. Evolutionary Biology, 4, 185-209
 
L'Heritier PH, Teissier G (1937) Une anomalie physiologique héréditaire chez la Drosophile. C.R. Acad. Sci. Paris, 231, 192-194
 
Leigh BA, Bordenstein SR, Brooks AW, Mikaelyan A, Bordenstein SR (2018) Finer-Scale Phylosymbiosis: Insights from Insect Viromes. Msystems, 3. https://doi.org/10.1128/mSystems.00131-18
 
Longdon B, Obbard DJ, Jiggins FM (2010) Sigma viruses from three species of Drosophila form a major new clade in the rhabdovirus phylogeny. Proceedings of the Royal Society B, 277, 35-44. 
https://doi.org/10.1098/rspb.2009.1472
 
Longdon B, Wilfert L, Jiggins FM (2012) The Sigma Viruses of Drosophila. Caister Academic Press, Norfolk, UK.
 
Longdon B, Wilfert L, Osei-Poku J, Cagney H, Obbard DJ, Jiggins FM (2011) Host switching by a vertically-transmitted rhabdovirus in Drosophila. Biology Letters, 7, 747-750. 
https://doi.org/10.1098/rsbl.2011.0160
 
Medd NC, Fellous S, Waldron FM, Xuereb A, Nakai M, Cross JV, Obbard DJ (2018) The virome of Drosophila suzukii, an invasive pest of soft fruit. Virus Evol, 4, vey009. 
https://doi.org/10.1093/ve/vey009
 
Obbard DJ (2018) Expansion of the metazoan virosphere: progress, pitfalls, and prospects. Curr Opin Virol, 31, 17-23. https://doi.org/10.1016/j.coviro.2018.08.008
 
Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, Qin XC, Li J, Cao JP, Eden JS, Buchmann J, Wang W, Xu J, Holmes EC, Zhang YZ (2016) Redefining the invertebrate RNA virosphere. Nature. https://doi.org/10.1038/nature20167
 
Varaldi J, Lepetit D, Burlet N, Faber C, Baretje B, Allemand R (2024) Community structure of heritable viruses in a Drosophila-parasitoids complex. bioRxiv, 2023.2007.2029.551099, ver.3, peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.07.29.551099
 
Wallace MA, Coffman KA, Gilbert C, Ravindran S, Albery GF, Abbott J, Argyridou E, Bellosta P, Betancourt AJ, Colinet H, Eric K, Glaser-Schmitt A, Grath S, Jelic M, Kankare M, Kozeretska I, Loeschcke V, Montchamp-Moreau C, Ometto L, Onder BS, Orengo DJ, Parsch J, Pascual M, Patenkovic A, Puerma E, Ritchie MG, Rota-Stabelli O, Schou MF, Serga SV, Stamenkovic-Radak M, Tanaskovic M, Veselinovic MS, Vieira J, Vieira CP, Kapun M, Flatt T, Gonzalez J, Staubach F, Obbard DJ (2021) The discovery, distribution, and diversity of DNA viruses associated with Drosophila melanogaster in Europe. Virus Evol, 7, veab031. https://doi.org/10.1093/ve/veab031
 
Webster CL, Longdon B, Lewis SH, Obbard DJ (2016) Twenty-Five New Viruses Associated with the Drosophilidae (Diptera). Evol Bioinform Online, 12, 13-25. https://doi.org/10.4137/EBO.S39454
 
Webster CL, Waldron FM, Robertson S, Crowson D, Ferrai G, Quintana JF, Brouqui JM, Bayne EH, Longdon B, Buck AH, Lazzaro BP, Akorli J, Haddrill PR, Obbard DJ (2015) The discovery, distribution and evolution of viruses associated with Drosophila melanogaster. Plos Biology, 13(7): e1002210. https://doi.org/10.1371/journal.pbio.1002210
 
Wille M, Shi M, Klaassen M, Hurt AC, Holmes EC (2019) Virome heterogeneity and connectivity in waterfowl and shorebird communities. ISME J, 13, 2603-2616. https://doi.org/10.1038/s41396-019-0458-0

Sequencing and analyzing SARS-Cov-2 genomes in nearly real time has the potential to quickly confirm (and inform) our knowledge of, and response to, the current pandemic [1,2]. In this manuscript [3], Danesh and colleagues use the earliest set of available SARS-Cov-2 genome sequences available from France to make inferences about the timing of the major epidemic wave, the duration of infections, and the efficacy of lockdown measures. Their phylodynamic estimates -- based on fitting genomic data to molecular clock and transmission models -- are reassuringly close to estimates based on 'traditional' epidemiological methods: the French epidemic likely began in mid-January or early February 2020, and spread relatively rapidly (doubling every 3-5 days), with people remaining infectious for a median of 5 days [4,5]. These transmission parameters are broadly in line with estimates from China [6,7], but are currently unknown in France (in the absence of contact tracing data). By estimating the temporal reproductive number (Rt), the authors detected a slowing down of the epidemic in the most recent period of the study, after mid-March, supporting the efficacy of lockdown measures.
Along with the three other reviewers of this manuscript, I was impressed with the careful and exhaustive phylodynamic analyses reported by Danesh et al. [3]. Notably, they take care to show that the major results are robust to the choice of priors and to sampling. The authors are also careful to note that the results are based on a limited sample size of SARS-Cov-2 genomes, which may not be representative of all regions in France. Their analysis also focused on the dominant SARS-Cov-2 lineage circulating in France, which is also circulating in other countries. The variations they inferred in epidemic growth in France could therefore be reflective on broader control policies in Europe, not only those in France. Clearly more work is needed to fully unravel which control policies (and where) were most effective in slowing the spread of SARS-Cov-2, but Danesh et al. [3] set a solid foundation to build upon with more data. Overall this is an exemplary study, enabled by rapid and open sharing of sequencing data, which provides a template to be replicated and expanded in other countries and regions as they deal with their own localized instances of this pandemic.

References

[1] Grubaugh, N. D., Ladner, J. T., Lemey, P., Pybus, O. G., Rambaut, A., Holmes, E. C., & Andersen, K. G. (2019). Tracking virus outbreaks in the twenty-first century. Nature microbiology, 4(1), 10-19. doi: 10.1038/s41564-018-0296-2
[2] Fauver et al. (2020) Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States. Cell, 181(5), 990-996.e5. doi: 10.1016/j.cell.2020.04.021
[3] Danesh, G., Elie, B., Michalakis, Y., Sofonea, M. T., Bal, A., Behillil, S., Destras, G., Boutolleau, D., Burrel, S., Marcelin, A.-G., Plantier, J.-C., Thibault, V., Simon-Loriere, E., van der Werf, S., Lina, B., Josset, L., Enouf, V. and Alizon, S. and the COVID SMIT PSL group (2020) Early phylodynamics analysis of the COVID-19 epidemic in France. medRxiv, 2020.06.03.20119925, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/2020.06.03.20119925
[4] Salje et al. (2020) Estimating the burden of SARS-CoV-2 in France. hal-pasteur.archives-ouvertes.fr/pasteur-02548181
[5] Sofonea, M. T., Reyné, B., Elie, B., Djidjou-Demasse, R., Selinger, C., Michalakis, Y. and Samuel Alizon, S. (2020) Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France. medRxiv, 2020.05.22.20110593. doi: 10.1101/2020.05.22.20110593
[6] Rambaut, A. (2020) Phylogenetic analysis of nCoV-2019 genomes. virological.org/t/phylodynamic-analysis-176-genomes-6-mar-2020/356
[7] Li et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med, 382: 1199-1207. doi: 10.1056/NEJMoa2001316