Philippa Kaur

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The COVID-19 pandemic prompted calls for cessation of wild meat trade and consumption to protect public health and biodiversity. High-quality data on wild meat consumption at a global scale is limited, but in many regions wild meat forms an important component of human nutrition — complete cessation of wild meat consumption could create unforeseen shocks to wider food systems through reduced protein intake and land use change for livestock production. Therefore, Hollie Booth, from the University of Oxford, and colleagues identified wild meat-consuming regions across 83 countries and estimated the potential magnitude of halting wild meat consumption on food systems.Using available global datasets on nutrient supply and land demand, Booth and colleagues identified 15 countries where wild meat accounted for more than 5% of total animal protein — and all ranked in the bottom 50% of the global food security index. Of these 15 countries, Madagascar, Republic of Congo, Guinea, Rwanda, Central African Republic, Zimbabwe, Botswana and Côte d’Ivoire would fall below WHO protein intake recommendations — the latter two countries derive 61% and 73% (respectively) of animal protein from wild meat. Estimates indicated that globally 123,980 km2 of additional agricultural land, predominantly in South and Central America, and sub-Saharan Africa, would be needed to replace wild meat protein with livestock-derived protein — placing up to 267 species at risk of extinction. Madagascar, rural Gabon, the East Region of Cameroon, Malawi, and the Brazilian Amazon would likely struggle with food system adaptation due to a lack of viable protein alternatives, food security trade-offs and social factors such as limited ban enforcement capacities and illicit trading.Booth and colleagues note that the ability of countries’ food systems to absorb these shocks are unequally distributed, with protein shortfalls in some of the world’s most food-insecure countries and potential loss of livelihoods, rights and social values. Forest regions with high mammalian biodiversity may also suffer under increased risk of emerging infectious diseases — with epidemic or pandemic potential. Thus, the authors call for risk-based regulation preventing the use and trade of slowly reproducing, endangered species, or those with high zoonotic potential while permitting use and trade of more sustainable species. Despite the limited data available, Booth and colleagues demonstrate that political and social debates around wild meat urgently require further research — and holistic policy responses guided by food systems thinking. More

1.Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).ADS 
CAS 
Article 

Google Scholar 
2.Hicks, C. C. et al. Harnessing global fisheries to tackle micronutrient deficiencies. Nature 574, 95–98 (2019).ADS 
CAS 
Article 

Google Scholar 
3.SOFIA 2020—State of Fisheries and Aquaculture in the World 2020 (FAO, 2020).4.Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).ADS 
CAS 
Article 

Google Scholar 
5.Kawarazuka, N. & Béné, C. The potential role of small fish species in improving micronutrient deficiencies in developing countries: building evidence. Public Health Nutr. 14, 1927–1938 (2011).Article 

Google Scholar 
6.Belton, B. & Thilsted, S. H. Fisheries in transition: food and nutrition security implications for the global South. Glob. Food Sec. 3, 59–66 (2014).Article 

Google Scholar 
7.Hilborn, R., Banobi, J., Hall, S. J., Pucylowski, T. & Walsworth, T. E. The environmental cost of animal source foods. Front. Ecol. Environ. 16, 329–335 (2018).Article 

Google Scholar 
8.Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).CAS 
Article 

Google Scholar 
9.Heilpern, S. Integrating Food Webs and Food Security to Understand the Impact of Biodiversity Loss on Ecosystem Functions and Services. PhD thesis, Columbia Univ. (2020).10.Ministerio de Desarrollo Agrario y Riego (Midagri); https://www.gob.pe/midagri11.Ministerio de la Producción (Produce); https://www.gob.pe/produce12.OECD-FAO Agricultural Outlook, 2019 edn (OECD/FAO, 2020).13.Peru—National Program for Innovation in Fisheries and Aquaculture Project (World Bank, 2017).14.DeFries, R. et al. Metrics for land-scarce agriculture. Science 349, 238–240 (2015).ADS 
CAS 
Article 

Google Scholar 
15.Loreto: Resultados Definitivos de la Población Economicamnte Activa 2017 (Instituto Nacional de Estadistica e Informática, 2018).16.McIntyre, P. B., Liermann, C. A. R. & Revenga, C. Linking freshwater fishery management to global food security and biodiversity conservation. Proc. Natl Acad. Sci. USA 113, 12880–12885 (2016).CAS 
Article 

Google Scholar 
17.Youn, S.-J. et al. Inland capture fishery contributions to global food security and threats to their future. Glob. Food Sec. 3, 142–148 (2014).Article 

Google Scholar 
18.Kawarazuka, N. & Béné, C. The potential role of small fish species in improving micronutrient deficiencies in developing countries: building evidence. Public Health Nutr. 14, 1927–1938 (2011).Article 

Google Scholar 
19.Bogard, J. R. et al. Nutrient composition of important fish species in Bangladesh and potential contribution to recommended nutrient intakes. J. Food Compos. Anal. 42, 120–133 (2015).CAS 
Article 

Google Scholar 
20.Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 1–10 (2018).CAS 
Article 

Google Scholar 
21.Popkin, B. M. Nutrition, agriculture and the global food system in low and middle income countries. Food Policy 47, 91–96 (2014).Article 

Google Scholar 
22.Bogard, J. R. et al. Higher fish but lower micronutrient intakes: temporal changes in fish consumption from capture fisheries and aquaculture in Bangladesh. PLoS ONE 12, e0175098 (2017).Article 

Google Scholar 
23.Golden, C. D., Fernald, L. C. H., Brashares, J. S., Rasolofoniaina, B. J. R. & Kremen, C. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. Proc. Natl Acad. Sci. USA 108, 19653–19656 (2011).ADS 
CAS 
Article 

Google Scholar 
24.Davis, K. F. et al. Meeting future food demand with current agricultural resources. Global Environ. Change 39, 125–132 (2016).Article 

Google Scholar 
25.Parker, R. W. R. & Tyedmers, P. H. Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish Fish. 16, 684–696 (2015).Article 

Google Scholar 
26.Parker, R. W. R. et al. Fuel use and greenhouse gas emissions of world fisheries. Nat. Clim. Change 8, 333–337 (2018).ADS 
CAS 
Article 

Google Scholar 
27.Avadí, A. et al. Comparative environmental performance of artisanal and commercial feed use in Peruvian freshwater aquaculture. Aquaculture 435, 52–66 (2015).Article 

Google Scholar 
28.Fry, J. P., Mailloux, N. A., Love, D. C., Milli, M. C. & Cao, L. Feed conversion efficiency in aquaculture: do we measure it correctly? Environ. Res. Lett. 13, 024017 (2018).ADS 
Article 

Google Scholar 
29.Prudêncio da Silva, V., van der Werf, H. M. G., Soares, S. R. & Corson, M. S. Environmental impacts of French and Brazilian broiler chicken production scenarios: an LCA approach. J. Environ. Manage. 133, 222–231 (2014).Article 

Google Scholar 
30.Seto, K. & Fiorella, K. J. From sea to plate: the role of fish in a sustainable diet. Front. Mar. Sci. 4, 74 (2017).Article 

Google Scholar 
31.Lynch, A. J. et al. Inland fish and fisheries integral to achieving the Sustainable Development Goals. Nature Sustain. 3, 579–587 (2020).32.Nardoto, G. B. et al. Frozen chicken for wild fish: nutritional transition in the Brazilian Amazon region determined by carbon and nitrogen stable isotope ratios in fingernails. Am. J. Hum. Biol. 23, 642–650 (2011).Article 

Google Scholar 
33.Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).ADS 
CAS 
Article 

Google Scholar 
34.Kearney, J. Food consumption trends and drivers. Philos. Trans. R. Soc. Lond B Biol. Sci. 365, 2793–2807 (2010).Article 

Google Scholar 
35.Pinnegar, J. K., Hutton, T. P. & Placenti, V. What relative seafood prices can tell us about the status of stocks. Fish Fish. 7, 219–226 (2006).Article 

Google Scholar 
36.Wong, J. T. et al. Small-scale poultry and food security in resource-poor settings: a review. Global Food Sec. 15, 43–52 (2017).Article 

Google Scholar 
37.Tabela Brasileira de Composição de Alimentos—TACO (Núcleo de Estudos e Pesquisas em Alimentação—NEPA/UNICAMP, 2011).38.Cahu, C., Salen, P. & de Lorgeril, M. Farmed and wild fish in the prevention of cardiovascular diseases: assessing possible differences in lipid nutritional values. Nutr. Metab. Cardiovasc. Dis. 14, 34–41 (2004).CAS 
Article 

Google Scholar 
39.Vitamin and Mineral Requirements in Human Nutrition (WHO/FAO, 2004).40.Fats and Fatty Acids in Human Nutrition: Report of an Expert Consultation (FAO, 2010).41.Heilpern, S. A., Weeks, B. C. & Naeem, S. Predicting ecosystem vulnerability to biodiversity loss from community composition. Ecology 99, 1099–1107 (2018).Article 

Google Scholar 
42.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

1.Drews, C. The concept and definition of dominance in animal behaviour. Behaviour 125, 283–313. https://doi.org/10.1163/156853993X00290 (1993).Article 

Google Scholar 
2.Darwin, C.D. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life, 140 (Murray, 1859) http://darwin-online.org.uk/content/frameset?itemID=F373&viewtype=text&pageseq=1 (Accessed 10 Feb 2021).3.Wilson, E. O. Sociobiology: The New Synthesis (Harvard University Press, 1975).
Google Scholar 
4.Jennions, M. D. & Petrie, M. Variation in mate choice and mating preferences: A review of causes and consequences. Biol. Rev. 72, 283–327. https://doi.org/10.1111/j.1469-185X.1997.tb00015.x (1997).CAS 
Article 
PubMed 

Google Scholar 
5.Wong, B. B. M. & Candolin, U. How is female mate choice affected by male competition?. Biol. Rev. 80, 559–571. https://doi.org/10.1017/S1464793105006809 (2005).Article 
PubMed 

Google Scholar 
6.Johnstone, R. A. Sexual selection, honest advertisement and the handicap principle: Reviewing the evidence. Biol Rev. 70, 1–65. https://doi.org/10.1111/j.1469-185x.1995.tb01439.x (1995).CAS 
Article 
PubMed 

Google Scholar 
7.Fedorka, K. M. & Mousseau, T. A. Material and genetic benefits of female multiple mating and polyandry. Anim. Behav. 64, 361–367. https://doi.org/10.1006/anbe.2002.3052 (2002).Article 

Google Scholar 
8.Kirkpatrick, M. & Ryan, M. The evolution of mating preferences and the paradox of the lek. Nature 350, 33–38. https://doi.org/10.1038/350033a0 (1991).ADS 
Article 

Google Scholar 
9.Bachmann, G. E. et al. Mate choice confers direct benefits to females of Anastrepha fraterculus (Diptera: Tephritidae). PLoS ONE 14, e0214698. https://doi.org/10.1371/journal.pone.0214698 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Gabor, C. R. & Halliday, T. R. Sequential mate choice by multiply mating smooth newts: Females become more choosy. Behav. Ecol. 8, 162–166. https://doi.org/10.1093/beheco/8.2.162 (1977).Article 

Google Scholar 
11.Clutton-Brock, T. Sexual selection in male and females. Science 318, 1882–1885. https://doi.org/10.1126/science.1133311 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Clutton-Brock, T. Sexual selection in females. Anim. Behav. 77, 3–11. https://doi.org/10.1016/j.anbehav.2008.08.026 (2008).Article 

Google Scholar 
13.Bleu, J., Bessa-Gomes, C. & Laloi, D. Evolution of female choosiness and mating frequency: Effects of mating cost, density and sex ratio. Anim. Behav. 83, 131–136. https://doi.org/10.1016/j.anbehav.2011.10.017 (2012).Article 

Google Scholar 
14.Koyama, J. Mating pheromones: tropical dacines. In Fruit Flies: Their Biology, Natural Enemies and Control (eds Robinson, A. S. & Hooper, G.) 165–168 (Elsevier, 1989).
Google Scholar 
15.Malte, A. & Simmons, L. W. Sexual selection and mate choice. Tree. 21, 296–302. https://doi.org/10.1016/j.tree.2006.03.015 (2006).Article 

Google Scholar 
16.Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: Current knowledge and potential applications for integrated pest management. J. Pest Sci. 87, 385–405. https://doi.org/10.1007/s10340-014-0577-3 (2014).Article 

Google Scholar 
17.Prokopy, R. J. Mating behavior of frugivorous Tephritidae in nature. In Proc. Symp. Fruit Fly Problems. XVI Int. Congr. Entomol. Kyoto, Japan, 37–46 (1980).18.Sivinski, J. M. & Burk, T. Reproductive and mating behaviour. In Fruit Flies: Their Biology, Natural Enemies and Control (eds Robinson, A. S. & Hooper, G.) 343–351 (Elsevier, 1989).
Google Scholar 
19.Benelli, G., Giunti, G., Canale, A. & Messing, R. Lek dynamics and cues evoking mating behavior in tephritid flies infesting soft fruits: Implications for behavior-based control tools. Appl. Entomol. Zool. 49, 363–373. https://doi.org/10.1007/s13355-014-0276-9 (2014).Article 

Google Scholar 
20.Arita, L. H. & Kaneshiro, K. Y. Sexual selection and lek behavior in the mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). Pac. Sci. 43, 135–143 (1989).
Google Scholar 
21.Benelli, G. Aggression in Tephritidae flies: Where, when, why? Future directions for research in integrated pest management. Insects 6, 38–53. https://doi.org/10.3390/insects6010038 (2015).Article 

Google Scholar 
22.Landolt, P. J. & Hendrichs, J. Reproductive behavior of the papaya fruit fly, Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae). Ann. Entomol. Soc. Am. 76, 413–417. https://doi.org/10.1093/aesa/76.3.413 (1983).Article 

Google Scholar 
23.Robledo, N. R. & Arzuffi, R. Influence of host fruit and conspecifics on the release of sex pheromone by Toxotrypana curvicauda males (Diptera: Tephritidae). Environ. Entomol. 41, 387–391. https://doi.org/10.1603/EN11037 (2012).CAS 
Article 
PubMed 

Google Scholar 
24.Aluja, M. et al. Daily activity patterns and within-field distribution of papaya fruit flies (Diptera: Tephritidae) in Morelos and Veracruz, Mexico. Ann. Entomol. Soc. Am. 90, 505–520. https://doi.org/10.1093/aesa/90.4.505 (1997).Article 

Google Scholar 
25.Landolt, P. J. Behavior of flies in the genus Toxotrypana (Trypetinae: Toxotrypanini). In Fruit Flies (Tephritidae): Phylogeny and Evolution of Behavior (eds Aluja, M. & Norrbom, A.) 363–373 (CRC Press, 2000).
Google Scholar 
26.Jiménez-Pérez, A. & Villa-Ayala, P. Size, fecundity and gonadic maturation of Toxotrypana curvicauda (Diptera: Tephritidae). Fla. Entomol. 89, 194–198. https://doi.org/10.1653/0015-4040 (2006).Article 

Google Scholar 
27.Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1997).ADS 
Article 

Google Scholar 
28.Landolt, P. J., Heath, R. R. & King, J. R. Behavioral responses of female papaya fruit flies, Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae), to male-produced sex pheromones. Ann. Entomol. Soc. Am. 78, 751–755. https://doi.org/10.1093/aesa/78.6.751 (1985).Article 

Google Scholar 
29.Sivinski, J. M. & Webb, J. C. The form and function of acoustic courtship signals of the papaya fruit fly, Toxotrypana curvicauda (Tephritidae). Fla. Entomol. 68, 634–664 (1985).Article 

Google Scholar 
30.Landolt, P. J. Chemical ecology of papaya fruit fly. In Fruit Flies: Biology and Management 1st edn (eds Aluja, M. & Liedo, P.) 207–210 (Springer-Verlag, 1990).
Google Scholar 
31.Castrejón, A.F. Aspectos de la biología y hábitos de Toxotrypana curvicauda Gerst. (Diptera:Tephritidae) en condiciones de laboratorio y su distribución en una plantación de Carica papaya L. en Yautepec, Morelos. Bachelors’ dissertation, Instituto Politécnico Nacional. Mexico (1987).32.Robacker, C., Mangan, R. L., Moreno, D. S. & Tarshis, A. M. Mating behavior and male mating success in wild Anastrepha ludens (Diptera: Tephritidae) on a field-caged host tree. J. Insect Behav. 4, 471–487. https://doi.org/10.1007/BF01049332 (1991).Article 

Google Scholar 
33.Taylor, P. W. & Yuval, B. Postcopulatory sexual selection in Mediterranean fruit flies: Advantages for large and protein-fed males. Anim. Behav. 58, 247–254. https://doi.org/10.1006/anbe.1999.1137 (1999).CAS 
Article 
PubMed 

Google Scholar 
34.Abraham, S. et al. Remating behavior in Anastrepha fraterculus (Diptera: Tephritidae) females is affected by male juvenile hormone analog treatment but not by male sterilization. Bull. Entomol. Res. 103, 310–317. https://doi.org/10.1017/S0007485312000727 (2013).CAS 
Article 
PubMed 

Google Scholar 
35.Sánchez-Rosario, M., Pérez-Staples, D., Toledo, J., Valle-Mora, J. & Liedo, P. Artificial selection on mating competitiveness of Anastrepha ludens for sterile insect technique application. Entomol. Exp. Appl. 162, 133–147. https://doi.org/10.1111/eea.12540 (2017).Article 

Google Scholar 
36.Colwell, A. E. & Shorey, H. H. The courtship behavior of the house fly, Musca domestica (Diptera: Muscidae). Ann. Entomol. Soc. Am. 68, 152–156. https://doi.org/10.1093/aesa/68.1.152 (1975).Article 

Google Scholar 
37.Yeh, S. D., Liou, S. R. & True, J. Genetics of divergence in male wing pigmentation and courtship behavior between Drosophila elegans and D. gunungcola. Heredity 96, 383–395. https://doi.org/10.1038/sj.hdy.6800814 (2006).Article 
PubMed 

Google Scholar 
38.Benelli, G. & Romano, D. Looking for the right mate—What do we really know on the courtship and mating of Lucilia sericata (Meigen)?. Acta Trop. https://doi.org/10.1016/j.actatropica.2018.08.013 (2019).Article 
PubMed 

Google Scholar 
39.Wicker-Thomas, C. Pheromonal communication involved in courtship behavior in Diptera. J. Insect Physiol. 53, 1089–1100. https://doi.org/10.1016/j.jinsphys.2007.07.003 (2007).CAS 
Article 
PubMed 

Google Scholar 
40.Tadeo, E., Aluja, M. & Rull, J. Alternative mating tactics as potential prezygotic barriers to gene flow between two sister species of frugivorous fruit flies. J. Insect Behav. 26, 708–720. https://doi.org/10.1007/s10905-013-9383-7 (2013).Article 

Google Scholar 
41.Burk, T. & Webb, J. C. Effect of male size on calling propensity, song parameters, and mating success in Caribean fruit flies (Anastrepha suspensa (Loew)). Ann. Entomol. Soc. Am. 76, 678–682. https://doi.org/10.1093/aesa/76.4.678 (1983).Article 

Google Scholar 
42.Briceño, R. D. & Eberhard, W. G. Possible Fisherian changes in female mate-choice criteria in a mass-reared strain of Ceratitis capitata (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 93, 343–345. https://doi.org/10.1603/0013-8746(2000)093[0343:PFCIFM]2.0.CO;2 (2000).Article 

Google Scholar 
43.Poramarcom, R. & Boake, C. R. B. Behavioural influences on male mating success in the Oriental fruit fly, Dacus dorsalis Hendel. Anim. Behav. 42, 453–460. https://doi.org/10.1016/S0003-3472(05)80044-2 (1991).Article 

Google Scholar 
44.Dukas, R. & Scott, A. Fruit fly courtship: The female perspective. Curr. Zool. 61, 1008–1014. https://doi.org/10.1093/czoolo/61.6.1008 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Benelli, G. et al. Contest experience enhances aggressive behaviour in a fly: When losers learn to win. Sci. Rep. 5, 9347. https://doi.org/10.1038/srep09347 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Landolt, P. J. Reproductive maturation and premating period of the papaya fruit fly Toxotrypana curvicauda (Diptera: Tephritidae). Fla. Entomol. 67, 240–244 (1984).Article 

Google Scholar 
47.Martínez Rogelio. Comportamiento agonista de Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae). Bachelors dissertation, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, Mexico, (2016)48.Arzuffi, A. Factores determinantes del orden jerárquico en el acocil Cambarellus zempoalensis (Crustacea: Cambaridae). Doctorate dissertation, Instituto Politécnico Nacional. Mexico City (1997).49.Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007). https://doi.org/10.1002/ajpa.1330740314.
Google Scholar 
50.Markow, T. Behavioral and sensory basis of courtship success in Drosophila melanogaster. PNAS 84, 6200–6204. https://doi.org/10.1073/pnas.84.17.6200 (1987).ADS 
CAS 
Article 
PubMed 

Google Scholar 
51.Arzuffi, B.A., Salazar-Marcial, L. & Robledo, Q.N. Cortejo y apareamiento de Toxotrypana curvicauda (Diptera:Tephritidae): análisis cuantitativo y efecto de la edad. Primer Congreso Internacional de Agronomía Tropical y Segundo Simposio Nacional Agroalimentario. Villahermosa, Tabasco, México (2009).52.Salazar-Marcial, L., Arzuffi, B. R. & Robledo, Q. N. Efecto de la edad sobre el cortejo y el apareamiento en Toxotrypana curvicauda (Diptera:Tephritidae). Entomol. Mex. 9, 328–329 (2010).
Google Scholar  More

Population structureThere are N households in the population, and a single educational institution (either a school or a school, dependent on scenarios to be introduced later) with M rooms and a maximum capacity dependent on the scenario being tested. Effective contacts between individuals occur within each household, as well as rooms and common areas (entrances, bathrooms, hallways, etc.) of the institution. All groups of individuals (households and rooms) in the model are assumed to be well-mixed.Each individual (agent) in the model is assigned an age, household, room in the childcare facility and an epidemiological status. Age is categorical, so that every individual is either considered a child (C) or an adult (A). Epidemiological status is divided into stages in the progression of the disease; agents can either be susceptible (S), exposed to the disease (E), presymptomatic (an initial asymptomatic infections period P), symptomatically infected (I), asymptomatically infected (A) or removed/recovered (R), as shown in Fig. 1b.In the model, some children in the population are enrolled as students in the institution and assigned a classroom based on assumed scenarios of classroom occupancy while some adults are assigned educator/caretaker roles in these classroom (again dependent on the occupancy scenario being tested). Assignments are made such that there is only one educator per household and that children do not attend the same institution as a educator in the household (if there is one), and vice versa.Interaction and disease progressionThe basic unit of time of the model is a single day, over which each attendee (of the institution) spends time at both home and at the institution. The first interactions of each day are established within each household, where all members of the household interact with each other. An asymptomatically infectious individual of age i will transmit the disease to a susceptible housemate with the age j with probability (beta ^H_{i,j}), while symptomatically infectious members will self-isolate (not interact with housemates) for a period of 14 days.The second set of interpersonal interactions occur within the institution. Individuals (both students and educators) in each room interact with each other, where an infectious individual of age i transmits the disease to some susceptible individual of age j with probability (beta ^C_{i,j}). To signify common areas within the building (such as hallways, bathrooms and entrances), each individual will then interact with every other individual in the institution. There, an infectious individual of age j will infect a susceptible individual of age i with probability (beta ^O_{i,j}).To simulate community transmission (for example, public transport, coffee shops and other sources of infection not explicitly modelled here), each susceptible attendee is infected with probability (lambda _S). Susceptible individuals not attending the institution in some capacity are infected at rate (lambda _N), where (lambda _N >lambda _S) to compensate for those consistent effective interactions outside of the institution that are neglected by the model (such as workplace interactions among essential workers and members of the public).Figure 1b shows the progression of the illness experienced by each individual in the model. In each day, susceptible (S) individuals exposed to the disease via community spread or interaction with infectious individuals (those with disease statuses P, A and I) become exposed (E), while previously exposed agents become presymptomatic (P) with probability (delta). Presymptomatic agents develop an infection in each day with probability (delta), where they can either become symptomatically infected (I) with probability (eta) or asymptomatically infected (A) with probability (1-eta).The capacity of the sole educational institution in the model is divided evenly between 5 rooms, with class size and student-educator ratio governed by one of three basic scenarios: seven students and three educators per room (7 : 3), eight students and two educators per room (8 : 2), and fifteen students and two educators per room (15 : 2). Classroom assignments for children can be either randomized or grouped by household (siblings are put in the same class).Symptomatically infected agents (I) are removed from the simulation after 1 day (status R) with probability (gamma _I), upon which they self-isolate for 14 days, and therefore no longer pose a risk to susceptible individuals. Asymptomatically infected agents (A) remain infectious but are presumed able to maintain regular effective contact with other individuals in the population due to their lack of noticeable symptoms; they recover during this period (status R) with probability (gamma _A). Disease statuses are updated at the end of each day, after which the cycles of interaction and infection reoccur the next day.The actions of symptomatic (status I) agents depend on age and role. Individuals that become symptomatic maintain a regular schedule for 1 day following initial infection (including effective interaction within the institution, if attending), after which they serve a mandatory 14-day isolation period at home during which they interaction with no one (including other members of their household). On the second day after the individual’s development of symptoms, their infection is considered a disease outbreak centred in their assigned room, triggering the closure of that room for 14 days. All individuals assigned to that room are sent home, where they self-isolate for 14 days due to presumed exposure to the disease. Symptomatically infected children are not replaced, and simply return to their assigned classroom upon recovery. At the time of classroom reopening, any symptomatic educator is replaced by a substitute for the duration of their recovery, upon which they reprise their previous role in the institution; the selection of a substitute is made under previous constraints on educator selection (one educator per household. with no one chosen from households hosting any children currently enrolled in the institution).ParameterisationThe parameter values are given in Supplementary Table S4. The sizes of households in the simulation was determined from 2016 Statistics Canada census data on the distribution of family sizes42. We note that Statistics Canada data only report family sizes of 1, 2 or 3 children: the relative proportions for 3+ children were obtained by assuming that (65 %) of families of 3+ children had 3 children, (25%) had 4 children, (10%) had 5 children, and none had more than 5 children. Each educator was assumed to be a member of a household that did not have children attending the school. Again using census data, we assumed that (36%) of educators live in homes with no children, where an individual lives alone with probability 0.282, while households hosting 3, 4, 5, 6, and seven adults occur with probability 0.345, 0.152, 0.138, 0.055, 0.021 and 0.009 respectively. Others live with (ge 1) children in households following the size and composition distribution depending on the number of adults in the household. For single-parent households, a household with a single child occurs with probability 0.169, and households with 2, 3, 4 and 5 children occur with probabilities 0.079, 0.019, 0.007 and 0.003 respectively. With two-parent households, those probabilities become 0.284, 0.307, 0.086, 0.033 and 0.012.The age-specific transmission rates in households are given by the matrix:$$begin{aligned} begin{bmatrix} beta ^H_{1,1} &{} beta ^H_{1,2} \ beta ^H_{2,1} &{} beta ^H_{2,2} \ end{bmatrix} equiv beta ^H begin{bmatrix} c^H_{1,1} &{} c^H_{1,2} \ c^H_{2,1} &{} c^H_{2,2} \ end{bmatrix}, end{aligned}$$
(1)
where (c^H_{i,j}) gives the number of contacts per day reported between individuals of ages i and j estimated from data28 and the baseline transmission rate (beta ^H) is calibrated. To estimate (c^H_{i,j}) from the data in Ref.28, we used the non-physical contacts of age class 0–9 years and 25–44 years of age with themselves and one another in Canadian households. Based on a meta-analysis, the secondary attack rate of SARS-CoV-2 appears to be approximately (15 %) on average in both Asian and Western households43. Hence, we calibrated (beta ^H) such that a given susceptible person had a (15 %) chance of being infected by a single infected person in their own household over the duration of their infection averaged across all scenarios tested. As such, age specific transmission is given by the matrix$$begin{aligned} beta ^Hcdot begin{bmatrix} 0.5378 &{} 0.3916 \ 0.3632 &{} 0.3335 end{bmatrix}. end{aligned}$$
(2)
To determine (lambda _S) we used case notification data from Ontario during lockdown, when schools, workplaces, and schools were closed44. During this period, Ontario reported approximately 200 cases per day. The Ontario population size is 14.6 million, so this corresponds to a daily infection probability of (1.37 times 10^{-5}) per person. However, cases are under-ascertained by a significant factor in many countries. We assumed an under-ascertainment factor of 8.45 based on an empirical estimate of under-reporting45, meaning there are actually 8.45 times more cases than reported in Ontario, giving rise to (lambda _S = 1.16 times 10^{-4}) per day; (lambda _N) was set to (2cdot lambda _S). We emphasize that this number may fall later in the pandemic as testing capacity increases, although some individuals may still never get tested–especially schoolchildren, who are often asymptomatic.The age-specific transmission rates in the school rooms is given by the matrix$$begin{aligned} begin{bmatrix} beta ^C_{1,1} &{} beta ^C_{1,2} \ beta ^C_{2,1} &{} beta ^C_{2,2} \ end{bmatrix} equiv beta ^C begin{bmatrix} c^C_{1,1} &{} c^C_{1,2} \ c^C_{2,1} &{} c^C_{2,2} \ end{bmatrix} equiv beta ^C begin{bmatrix} 1.2356 &{} 0.0588 \ 0.1176 &{} 0.0451 end{bmatrix}, end{aligned}$$
(3)
where (c^C_{i,j}) is the number of contacts per day reported between age i and j estimated from data28. To estimate (c^C_{i,j}) from the data in Ref.28, we used the non-physical contacts of age class 0–9 years and 20–54 years of age, with themselves and one another, in Canadian schools. Epidemiological data on secondary attack rates in educational institutions are rare, since childcare centres and schools were closed early in the outbreak in most areas. We note that contacts in families are qualitatively similar in nature and duration to contacts in schools with small group sizes, although these contacts are generally more dispersed among the larger groups in rooms than among the smaller groups in households. On the other hand, rooms may represent equally favourable conditions for aerosol transmission, as opposed to close contact. Hence, we assumed that (beta ^C = alpha _C beta ^H), with a baseline value of (alpha _C = 0.75) based on more dispersed contacts expected in the larger room group, although we varied this assumption in sensitivity analysis.To determine (beta ^O) we assumed that (beta ^O = alpha _O beta ^C) where (alpha _O ll 1) to account for the fact that students spend less time in common areas than in their rooms. To estimate (alpha _O), we note that (beta ^O) is the probability that a given infected person transmits the infection to a given susceptible person. If students and staff have a probability p per hour of visiting a common area, then their chance of meeting a given other student/staff in the same area in that area is (p^2). We assumed that (p=0.05) and thus (alpha _O = 0.0025). The age-specific contact matrix for (beta ^O) was the same as that used for (beta ^C) (Eq. 3).Model initializationUpon population generation, each agent is initially susceptible (S). Individuals are assigned to households as described in the “Parameterisation” section, and children are assigned to rooms either randomly or by household. We assume that parents in households with more than one child will decide to enroll their children in the same institution for convenience with probability (xi =80%), so that each additional child in multi-child households will have probability (1-xi) of not being assigned to the institution being modelled.Households hosting educators are generated separately. As in the “Parameterisation” section, we assume that (36%) of educators live in adult-only houses, while the other educators live in houses with children, both household sizes following the distributions outlined in the “Parameterisation” section. The number of educator households is twice that required to fully supply the school due to the replacement process for symptomatic educators outlined in the “Disease Progression” section.Initially, a proportion of all susceptible agents (R_{init}) is marked as removed/recovered (R) to account for immunity caused by previous infection moving through the population. A single randomly chosen school attendee is chosen as a primary case and is made presymptomatic (P) to introduce a source of infection to the model. All simulations are run until there are no more potentially infectious (E, P, I, A) individuals left in the population and the institution is at full capacity. All results were averaged over 2000 trials.Estimating β
H
Agents in the simulation were divided into two classes: “children” (ages 0–9) and “adults” (ages 25–44). Available data on contact rates28 was stratified into age categories of width 5 years starting at age 0 (0–5, 5–9, 10–14, etc.). The mean number of contacts per day (c_{i,j}^H) for each class we considered (shown in Eq. 2) was estimated by taking the mean of the contact rates of all age classes fitting within our presumed age ranges for children and adults.For (beta ^H) calibration, we created populations by generating a sufficient number of households to fill the institution in each of the three tested scenarios; 15 : 2, 8 : 2 and 7 : 3. In each household, a single randomly chosen individual was infected (each member with equal probability) by assigning them a presymptomatic disease status P; all other members were marked as susceptible (disease status S). In each day of the simulation, each member of each household was allowed to interact with the infected member, becoming exposed to the disease with probability given in Eq. 2. Upon exposure, they were assigned disease status E. At the beginning of each subsequent day, presymptomatic individuals proceeded to infected statuses I and A, and infected agents were allowed to recover as dictated by Fig. 1b and Supplementary Table S4. This cycle of interaction and recovery within each household was allowed to continue until all infected individuals were recovered from illness.We did not allow exposed agents (status E) to progress to an infectious stage (I or A) since we were interested in finding out how many infections within the household would result from a single infected household member, as opposed to added secondary infections in later days. At the end of each trial, the specific probability of infection ((pi _n)) in each household (H_n) was calculated by dividing the number of exposed agents in the household ((E_n)) by the size of the household (|H_n|) less 1 (accounting for the member initially infected). Single occupant households ((|H_n|=1)) were excluded from the calculation. The total probability of infection (pi) was then taken as the mean of all (pi _n), so that$$begin{aligned} pi =frac{1}{D}sum _{n}pi _n=frac{1}{D}sum _{|H_n|ge 2}frac{E_n}{|H_n|-1}, end{aligned}$$
(4)
where D represents the total number of multiple occupancy households in the simulation. This modified disease simulation was run for 2000 trials each of different prospective values of (beta ^H) ranging from 0 to 0.21. The means of all corresponding final estimates of the infection rate were taken per value of (beta ^H), and the value corresponding to a infection rate of (15%) was interpolated.Simplifying assumptionsOur model makes simplifying assumptions that may influence its predictions. For instance, we assume that classrooms are homogeneously mixing and did not take social structure into account. Social structure might slow the spread of COVID-19 in classrooms. We also assumed that public health authorities will respond to a confirmed case by closing the classroom, although in practice, they may keep the class running if they think the case does not represent an infection risk to children or adults. This would reduce the number of student-days lost to closure. Similarly, we did not account for potential contacts between school children outside of classes, although students of a classroom that has been closed may still interact with their classmates outside of school. Other simplifying assumptions are mentioned in the “Discussion” section. More

We modified the reported DNA extraction methods using a commercial DNA extraction kit: the cetyl trimethylammonium bromide (CTAB) method13 with the addition of skim milk to prevent the absorption of DNA and a bead beating method14. In this report, this method is named the modified CTAB-bead method. The proposed real-time PCR assay may be suitable for the quantification of P. infestans population densities, at least in Japanese upland soils, because P. infestans DNA from various kinds of upland soils was well quantified, and there were no false positives in the negative control plots. Thus, we conclude that the P. infestans population density can be represented by the quantity of DNA determined using real-time PCR. One udifluvent and udult soil quantified slightly small amounts of DNA, and there were small differences among soil types at the same population densities. However, this should not be of great consequence because the differences compared with the other upland soils are within tenfold; thus, these small differences are likely due to the soil characteristics. A previous study reported that no single method of cell lysis or purification is appropriate for all soils15. Thus, the proposed real-time PCR assay is available to quantify the pathogen densities in soils such that most soil samples containing 4–400 zoosporangia/g soil plots except decomposed granite soil and sea sand were quantified as approximately 1–100 pg/g soil. Although this method can be used to quantify P. infestans DNA levels in soil, not all soil samples containing the same number of zoosporangia yielded similar results, as the amount of DNA absorbed was dependent on the soil type. Thus, a calibration curve may be required when a new soil type is tested in which a zoosporangia suspension or P. infestans DNA is added to nondiseased soil. Regarding decomposed granite soil and sea sand, which are not upland soils and not suitable for potato cultivation, the reason for the small DNA quantities may be that a large amount of DNA is absorbed onto silica under Na+- or Ca2+-rich conditions16. If the soil type is sandy or clayey, the DNA quantities may be smaller than those in other soil types. For further development of this method, the addition of an internal control, such as GFP-induced plasmid DNA17, to correct the raw data might be effective. Additionally, changing the glass beads used in this method to zirconia or iron beads may also be effective due to the powerful homogenization and lower amount of DNA absorption achieved with the latter two bead types. However, these improvements may be unnecessary because the proposed assay has a small detection limit such that samples containing only 4 zoosporangia/g soil were detected and quantified. Ristaino et al.18 reported that real-time LAMP and droplet digital PCR can be used to quantify P. infestans DNA from plant tissue. Compared with these tools, the proposed real-time PCR assay has some advantages, such as a wide dynamic range. For this reason, this assay may be widely applied to upland soils.This is the first report of the quantification of P. infestans population densities in naturally infested soil samples, and changes in the population densities were analysed using real-time PCR. These results also showed that this quantitative method provides reproducible results, because changes in P. infestans DNA were correlated with symptom development throughout the growth periods. DNA quantities during the epidemics (5 and 18 August 2017 and 2018, respectively) were converted into P. infestans population densities in zoosporangia equivalents based on the results obtained for udant B (experimental field, HARC), as shown in Fig. 1; thus, there were approximately 104–105 and 103–104 zoosporangia in the ridgetop soils. These results indicate that a large amount of P. infestans existed in the field ridgetop soils where the plants were blighted. Quantified DNA may be from zoosporangia, mycelia, or small residue or free DNA but not from oospores. Because the A1 mating type has been dominant in Japan since 200519, sexual reproduction rarely occurs, at least in potato fields. We have not verified the availability of soils containing oospores. In future studies, inoculated soil containing oospores should be tested. However, soils containing P. infestans oospores might be quantified using the proposed assay because previous studies reported that soils containing three potato pathogens and oospores of Pythium spp. have been quantified using CTAB and bead beating methods20,21. If the proposed assay can quantify P. infestans DNA from oospores in soil, we might apply this assay to soil diagnosis before planting.A previous study reported that the inoculum potentials of soil decreased as foliage lesions became less abundant2. Our study corresponds to this previous study because the quantities of P. infestans DNA in soil were consistent with foliage symptom development (in 2017 and 2018) and the number of lesions per plant (in 2018). Hence, the proposed real-time PCR method can be an alternative to bioassays and used as a method to quantify the P. infestans population density. Bioassays require special knowledge and techniques of plant pathology because researchers have to judge whether inoculated tubers were rotted due to P. infestans. On the other hand, real-time PCR assays are easy and require only minimal knowledge and techniques of molecular biology. In 2018, symptom development stopped from late July to early August due to a heat wave. The DNA quantities were reflected in foliage symptoms, with small quantities of DNA estimated during this period. These results imply that this method is highly sensitive for estimating even weekly population changes. The quantities of DNA were decreased to one-tenth of their former numbers in a week after the desiccation of the foliage. As indicated by the decrease, most P. infestans zoospores or zoosporangia cannot survive in/on the soil and quickly die and are degraded by microorganisms and DNase6,22,23. However, if new A2 strains migrated into Japan and oospores were found in field soil, DNA would be detected for a long time even in the noncultivation period. Surprisingly, an infinitesimal quantity of DNA was detected one month after the foliage had disappeared in 2017. This DNA may have been from another plant out of experimental fields or DNA absorbed to some kind of soil material and may persist against DNase24.Figure 5 shows a positive correlation between the quantity of P. infestans DNA and the inoculum potential. Thus, the proposed real-time PCR method is suitable for indirect estimation of P. infestans inoculum potential. In this analysis, two data sets containing zero values were eliminated as outliers because a zero value in this experiment signifies “below the detection limit”; we cannot determine the exact value. Thus, data sets containing zero values cannot be included in the analysis to evaluate the applicability of the proposed real-time PCR assay instead of the bioassay for estimating inoculum potential. Previous estimation methods, such as bioassays, require an incubation period of approximately 2–3 weeks, expert knowledge of P. infestans and incubation space. On the other hand, real-time PCR requires several hours to estimate the population densities, minimal knowledge of molecular biology and no incubation space. For these reasons, we can more easily estimate P. infestans inoculum potential with real-time PCR than with bioassays. In the experiments using commercial potato fields, a larger amount of DNA was quantified from ridge bottom soils than from any other location. This result agrees with a previous report that most rainwater was deposited at the bottoms of ridges, and the rainwater contained fewer than 500 zoosporangia when blight was present on the crop25. According to this result, soils sampled from the bottom of a ridge are suitable for whole field estimation of P. infestans population densities.In this study, the quantities of DNA and inoculum potentials were larger in field A than in fields B and C. This result suggests that the proposed real-time PCR assay may be suitable for comparison among potato fields. In field A, late blight occurred because farmers could not conduct chemical control due to heavy rain. Field B was a non-controlled commercial field, and incomplete chemical spraying gave rise to a non-controlled spot in field C. Fields B and C were not perfectly managed for preventing late blight; however, some control, such as cultural or chemical control, was performed in some part. On the other hand, field A did not receive control measures at all. This might be why field A had larger DNA quantities and inoculum potentials than fields B and C.For the results from non-controlled fields, P. infestans did not percolate through the soil but instead remained at the surface because most soil samples from ridgetops contained larger amounts of DNA than those from the tuber periphery. A previous study reported that more than half of the tubers in the top 5.1 cm of soil were blighted, and the population of blighted tubers decreased with increasing depth26. We can show the same conclusions using real-time PCR. However, in commercial fields, all soils sampled from the tuber periphery contained larger amounts of DNA and had a lower inoculum potential than those from the ridge surfaces. Rainy and cold conditions (approximately at 13 °C) continued from 14 to 18 August 2018, several days before sampling27. The weather might have sustained indirect germination, and many zoospores were released and percolated through the soil. However, zoospores are motile for only a short time28 and cannot survive for a long time. Thus, much of the quantified DNA was from dead P. infestans or free DNA, and less inoculum potential was found near tubers. Soil samples from ridgetops showed larger inoculum potential than those from the tuber periphery. This may be because ridgetop samples contain a large amount of fresh P. infestans from foliage lesions.In this study, we successfully developed a real-time PCR assay to estimate the P. infestans densities in upland soils, and the proposed assay is available not only for the estimation of population density but also inoculum potential. In the future, this research can provide to a new decision support system for predicting and preventing potato storage rot. The P. infestans soil population density is the most important factor influencing potato storage rot. The possibilities or severities of potato storage rot may be predicted by estimating the P. infestans population density in soil before harvesting. Previous storage planning suggested that potato storage rot might occur if many foliage lesions occur during the growing season. In this study, most P. infestans DNA from foliage lesions degraded within one week. Thus, the possibility and severity of storage rot may be low if the quantity of P. infestans DNA immediately before harvesting is small, even if many foliage lesions occur during the growing season. Additionally, the previous quantitative method (bioassay) requires an incubation period of approximately one week or more2,7. On the other hand, the real-time PCR assay does not require an incubation period, and it takes only several hours to quantify the P. infestans population density in the sample soil. Potato storage rot may be reduced because the storage plan can be selected accurately and rapidly by using real-time PCR compared with previous methods. For example, tubers harvested from fields harbouring high levels of P. infestans DNA can be shipped as soon as possible to prevent potato storage rot. However, many other factors may be involved in the spread of this disease, such as surface injury5. A decision support system would allow potato storage companies to evaluate and address factors associated with potato storage rot and establish appropriate countermeasures to prevent economic losses. More

1.Martin, J. H., Mifsud, D. & Rapisarda, C. The whiteflies (Hemiptera: Aleyrodidae) of Europe and the Mediterranean basin. Bull. Entomol. Res. 90, 407–448 (2000).CAS 
PubMed 
Article 

Google Scholar 
2.Omongo, C. A. et al. African cassava whitefly, Bemisia tabaci, resistance in African and South American cassava genotypes. J. Integr. Agric. 11, 327–336 (2012).Article 

Google Scholar 
3.Omongo, C. A. et al. Host plant resistance to African Bemisia tabaci in local landraces and improved cassava mosaic disease resistant genotypes in Uganda. In 6th International Scientific Meeting of the Cassava Biotechnology Network (Abstracts), Vol. 84, 8–14 (2004).4.Legg, J. P., Sseruwagi, P. & Brown, J. Bemisia whiteflies cause physical damage and yield losses to cassava in Africa. In Sixth International Scientific Meeting of the Cassava Biotechnology Network 78 (2004).5.Lloyd, L. L. The control of the greenhouse white fly (Asterochiton vaporariorum) with notes on its biology 1. Ann Appl Biol. 9, 1–32 (1922).Article 

Google Scholar 
6.McAuslane, H. J. & Smith, S. A. Sweet Potato Whitefly B Biotype, Bemisia tabaci (Gennadius) (Insecta: Hemiptera: Aleyrodidae) (University of Florida, 2015).
Google Scholar 
7.Viscarret, M. M., Botto, E. N. & Polaszek, A. N. Whiteflies (Hemiptera: Aleyrodidae) of economic importance and their natural enemies (Hymenoptera: Aphelinidae, Signiphoridae) in Argentina. Rev. Chil. Entomol. 26, 5–11 (2000).
Google Scholar 
8.Abd-Rabou, S. & Simmons, A. M. Survey of natural enemies of whiteflies (Hemiptera: Aleyrodidae) in Egypt with new local and world records. Entomol. News 124, 38–56 (2014).Article 

Google Scholar 
9.Roopa, H. K. et al. Phylogenetic analysis of Trialeurodes spp. (Hemiptera: Aleyrodidae) from India based on differences in mitochondrial and nuclear DNA. Fla Entomol. 1, 1086–94 (2012).Article 

Google Scholar 
10.De Barro, P. J., Liu, S. S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: a statement of species status. Annu. Rev. Entomol. 7, 1–9 (2011).Article 
CAS 

Google Scholar 
11.Liu, S. S., Colvin, J. & De Barro, P. J. Species concepts as applied to the whitefly Bemisia tabaci systematics: how many species are there? J. Integr. Agric. 11, 176–186 (2012).Article 

Google Scholar 
12.CABI/EPPO. Distribution Maps of Quarantine Pests for Europe (eds Smith I. M. & Charles L. M. F.) 768 (CAB International, 1998).13.Brown, J. K. Current status of Bemisia tabaci as a plant pest and virus vector in agroecosystems worldwide. FAO Plant Prot. Bull. 42, 3–2 (1994).
Google Scholar 
14.Brown, J. K. Molecular markers for the identification and global tracking of whitefly vector—begomovirus complexes. Virus Res. 71, 233–260 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Maruthi, M. N., Hillocks, R. J., Rekha, A. R. & Colvin, J. Transmission of Cassava brown streak virus by whiteflies. In Sixth International Scientific Meeting of the Cassava Biotechnology Network—Adding Value to a Small-Farmer Crop 8–14 (2004).16.Mugerwa, H. et al. Genetic diversity and geographic distribution of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) genotypes associated with cassava in East Africa. Ecol. Evol. 2, 2749–2762 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Maruthi, M. N. et al. Reproductive incompatibility and cytochrome oxidase I gene sequence variability amongst host-adapted and geographically separate Bemisia tabaci populations (Hemiptera: Aleyrodidae). Syst. Entomol. 29, 560–568 (2004).Article 

Google Scholar 
18.Legg, J. P. et al. Comparing the regional epidemiology of the cassava mosaic and cassava brown streak virus pandemics in Africa. Virus Res. 159, 161–170 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Prijović, M. et al. Genetic variation of the greenhouse whitefly, Trialeurodes vaporariorum (Hemiptera: Aleyrodidae), among populations from Serbia and neighbouring countries, as inferred from COI sequence variability. Bull. Entomol. Res. 104, 357–366 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Palevsky, E. et al. How specific is the phoretic relationship between broad mite, Polyphagotarsonemus latus (Banks) (Acari: Tarsonemidae), and its insect vectors? Exp. Appl. Acarol. 25, 217–224 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Mound, L. A. & Halsey, S. H. Whitefly of the World. A Systematic Catalogue of the Aleyrodidae (Homoptera) with Host Plant and Natural Enemy Data (Wiley, 1978).
Google Scholar 
22.Legg, J. P., French, R., Rogan, D., Okao-Okuja, G. & Brown, J. K. A distinct Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodidae) genotype cluster is associated with the epidemic of severe cassava mosaic virus disease in Uganda. Mol. Ecol. 11, 1219–1229 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Legg, J. P. Epidemiology of a whitefly-transmitted cassava mosaic geminivirus pandemic in Africa. In Bemisia: Bionomics and Management of a Global Pest (eds Stansly, P. A. & Naranjo, S. E.) 233–257 (Springer, 2009).24.Legg, J. P. et al. Spatio-temporal patterns of genetic change amongst populations of cassava Bemisia tabaci whiteflies driving virus pandemics in East and Central Africa. Virus Res. 186, 61–75 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Delatte, H. et al. A new silverleaf-inducing biotype Ms of Bemisia tabaci (Hemiptera: Aleyrodidae) indigenous to the islands of the south-west Indian Ocean. Bull. Entomol. Res. 95, 29–35 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Jones, A. L. & Markham, R. H. Whitefly and whitefly-borne viruses in the tropics: building a knowledge base for global action. CIAT 341, 129–140 (2005).
Google Scholar 
27.Hodges, G. S. & Evans, G. A. An identification guide to the whiteflies (Hemiptera: Aleyrodidae) of the Southeastern United States. Fla Entomol. 1, 518–534 (2005).Article 

Google Scholar 
28.Calvert, L. A. et al. Morphological and mitochondrial DNA marker analyses of whiteflies (Homoptera: Aleyrodidae) colonizing cassava and beans in Colombia. Ann. Entomol. Soc. Am. 94, 512–519 (2001).CAS 
Article 

Google Scholar 
29.Ovalle, T. M., Parsa, S., Hernández, M. P. & Becerra Lopez-Lavalle, L. A. Reliable molecular identification of nine tropical whitefly species. Ecol. Evol. 4, 3778–3787 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Shatters, R. G. Jr., Powell, C. A., Boykin, L. M., Liansheng, H. E. & McKenzie, C. L. Improved DNA barcoding method for Bemisia tabaci and related Aleyrodidae: development of universal and Bemisia tabaci biotype-specific mitochondrial cytochrome c oxidase I polymerase chain reaction primers. J. Econ. Entomol. 102, 750–758 (2009).CAS 
PubMed 
Article 

Google Scholar 
31.Alhudaib, K. A., Rezk, A. A., Abdel-Banat, B. M. & Soliman, A. M. Molecular identification of the biotype of whitefly (Bemisia tabaci) inhabiting the eastern region of Saudi Arabia. J. Biol. Sci. 14, 494–500 (2014).Article 
CAS 

Google Scholar 
32.Cavalieri, V., Manglli, A., Tiberini, A., Tomassoli, L. & Rapisarda, C. Rapid identification of Trialeurodes vaporariorum, Bemisia tabaci (MEAM1 and MED) and tomato-infecting criniviruses in whiteflies and in tomato leaves by real-time reverse transcription-PCR assay. Bull. Insectol. 67, 219–225 (2014).
Google Scholar 
33.Dickey, A. M., Stocks, I. C., Smith, T., Osborne, L. & McKenzie, C. L. DNA barcode development for three recent exotic whitefly (Hemiptera: Aleyrodidae) invaders in Florida. Fla Entomol. 98, 473–478 (2015).Article 

Google Scholar 
34.Brown, J. K. et al. Molecular diagnostic development for begomovirus-betasatellite complexes undergoing diversification: a case study. Virus Res. 241, 29–41 (2017).CAS 
PubMed 
Article 

Google Scholar 
35.Frohlich, D. R., Torres-Jerez, I., Bedford, I. D., Markham, P. G. & Brown, J. K. A phylogeographical analysis of the Bemisia tabaci species complex based on mitochondrial DNA markers. Mol. Ecol. 8, 1683–1691 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
37.Xiong, B. & Kocher, T. D. Comparison of mitochondrial DNA sequences of seven morphospecies of black flies (Diptera: Simuliidae). Genome 34, 306–311 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Narang, S. K., Seawright, J. A. & Suarez, M. F. Genetic structure of natural populations of Anopheles albimanus in Colombia. J. Am. Mosq. Control 7, 437–445 (1991).CAS 

Google Scholar 
39.Dinsdale, A., Cook, L., Riginos, C., Buckley, Y. M. & De Barro, P. Refined global analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) mitochondrial cytochrome oxidase 1 to identify species level genetic boundaries. Ann. Entomol. Soc. Am. 103, 196–208 (2010).Article 

Google Scholar 
40.Berry, S. D. et al. Molecular evidence for five distinct Bemisia tabaci (Homoptera: Aleyrodidae) geographic haplotypes associated with cassava plants in sub-Saharan Africa. Ann. Entomol. Soc. Am. 97, 852–859 (2004).CAS 
Article 

Google Scholar 
41.Brown, J. K. et al. Characterization and distribution of esterase electromorphs in the whitefly, Bemisia tabaci (Genn.) (Homoptera: Aleyrodidae). Biochem. Genet. 33, 205–214 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.De Barro, P. J. & Carver, M. Cabbage whitefly, Aleyrodes proletella (L.) (Hemiptera: Aleyrodidae), newly discovered in Australia. Aust. J. Entomol. 36, 255–256 (1997).Article 

Google Scholar 
43.Springate, S. & Colvin, J. Pyrethroid insecticide resistance in British populations of the cabbage whitefly Aleyrodes proletella. Pest. Manag. Sci. 68, 260–267 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Chen, Z. T., Mu, L. X., Wang, J. R. & Du, Y. Z. Complete mitochondrial genome of the citrus spiny whitefly Aleurocanthus spiniferus (Quaintance) (Hemiptera: Aleyrodidae): implications for the phylogeny of whiteflies. PLoS ONE 11, e0161385 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Boykin, L. M. et al. Global relationships of Bemisia tabaci (Hemiptera: Aleyrodidae) revealed using Bayesian analysis of mitochondrial COI DNA sequences. Mol. Phylogenet. Evol. 44, 1306–1319 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Lee, W., Park, J., Lee, G. S., Lee, S. & Akimoto, S. I. Taxonomic status of the Bemisia tabaci complex (Hemiptera: Aleyrodidae) and reassessment of the number of its constituent species. PLoS ONE 8, e63817 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Manzari, S. & Quicke, D. L. A cladistic analysis of whiteflies, subfamily Aleyrodinae (Hemiptera: Sternorrhyncha: Aleyrodidae). J. Nat. Hist. 40, 2423–2554 (2006).Article 

Google Scholar 
48.Gamarra, H., Carhuapoma, P., Mujica, N., Kreuze, J. & Kroschel, J. Greenhouse whitefly, Trialeurodes vaporariorum (Westwood 1956). In Pest Distribution and Risk Atlas for Africa—Potential Global and Regional Distribution and Abundance of Agricultural and Horticultural Pests and Associated Biocontrol Agents Under Current and Future Climates (eds Kroschel, J., Mujica, N., Carhuapoma, P., & Sporleder, M.) 154–168 (International Potato Center (CIP), 2016).49.Khamis, F. M. et al. Insights in the global genetics and gut microbiome of Black Soldier Fly, Hermetia illucens: implications for animal feed safety control. Front. Microbiol. 34, 1538 (2020).Article 

Google Scholar 
50.Simon, C. et al. Evolution, weighting, and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
Article 

Google Scholar 
51.Frohlich, D., Torres-Jerez, I., Bedford, I. D., Markham, P. G. & Brown, J. K. A phylogeographic analysis of the Bemisia tabaci species complex based on mitochondrial DNA markers. Mol. Ecol. 8, 1593–1602 (1999).Article 

Google Scholar 
52.Xiong, B. & Kocher, T. D. Comparison of mitochondrial DNA sequences of seven morphospecies of black flies (Diptera: Simuliidae). Genome 34, 306–311 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
59.CABI. Invasive Species Compendium (CAB International). Available online at www.cabi.org/isc/datasheet/8925 (2019).60.Fick, S. E. & Hijmans, R. J. Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
61.Taylor, K. E., Stouffer, R. J. & Meeh, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
Article 

Google Scholar 
62.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high-resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
63.Kuhn, M. et al. caret: Classification and Regression Training. R package version 6.0-71. https://CRAN.R-project.org/package=caret (2016). More

1.Hansson, G. C. Mucins and the microbiome. Annu. Rev. Biochem. 89, 769–793 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Cross, B. W. & Ruhl, S. Glycan recognition at the saliva—oral microbiome interface. Cell. Immunol. 333, 19–33 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Tabak, L. A. In defense of the oral cavity: structure, biosynthesis, and function of salivary mucins. Annu. Rev. Physiol. 57, 547–564 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Deng, L. et al. Oral streptococci utilize a Siglec-like domain of serine-rich repeat adhesins to preferentially target platelet sialoglycans in human blood. PLoS Pathog. 10, e1004540 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Shanker, E. & Federle, M. J. Quorum sensing regulation of competence and bacteriocins in Streptococcus pneumoniae and mutans. Genes 8, 15 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
6.Nakano, K., Nomura, R. & Ooshima, T. Streptococcus mutans and cardiovascular diseases. Jpn. Dent. Sci. Rev. 44, 29–37 (2008).Article 

Google Scholar 
7.Murchison, H. H., Barrett, J. F., Cardineau, G. A. & Curtiss, R. Transformation of Streptococcus mutans with chromosomal and shuttle plasmid (pYA629) DNAs. Infect. Immun. 54, 273–282 (1986).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Villedieu, A. et al. Prevalence of tetracycline resistance genes in oral bacteria. Antimicrob. Agents Chemother. 47, 878–882 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Chansley, P. E. & Kral, T. A. Transformation of fluoride resistance genes in Streptococcus mutans. Infect. Immun. 57, 1968–1970 (1989).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Hernando-Amado, S., Coque, T. M., Baquero, F. & Martínez, J. L. Defining and combating antibiotic resistance from one health and global health perspectives. Nat. Microbiol. 4, 1432–1442 (2019).CAS 
PubMed 
Article 

Google Scholar 
11.Villedieu, A. et al. Genetic basis of erythromycin resistance in oral bacteria. Antimicrob. Agents Chemother. 48, 2298–2301 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Olsen, I., Tribble, G. D., Fiehn, N.-E. & Wang, B.-Y. Bacterial sex in dental plaque. J. Oral Microbiol. 5, 20736 (2013).Article 

Google Scholar 
13.Loesche, W. J. Role of Streptococcus mutans in human dental decay. Microbiol. Rev. 50, 353–380 (1986).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Loesche, W. J., Rowan, J., Straffon, L. H. & Loos, P. J. Association of Streptococcus mutans with human dental decay. Infect. Immun. 11, 1252–1260 (1975).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Mathews, S. A., Kurien, B. T. & Scofield, R. H. Oral manifestations of Sjögren’s syndrome. J. Dent. Res. 87, 308–318 (2008).CAS 
PubMed 
Article 

Google Scholar 
16.Pramanik, R., Osailan, S. M., Challacombe, S. J., Urquhart, D. & Proctor, G. B. Protein and mucin retention on oral mucosal surfaces in dry mouth patients. Eur. J. Oral. Sci. 118, 245–253 (2010).CAS 
PubMed 
Article 

Google Scholar 
17.Frenkel, E. S. & Ribbeck, K. Salivary mucins in host defense and disease prevention. J. Oral Microbiol. 7, 29759 (2015).PubMed 
Article 
CAS 

Google Scholar 
18.Ahn, S.-J., Wen, Z. T. & Burne, R. A. Multilevel control of competence development and stress tolerance in Streptococcus mutans UA159. Infect. Immun. 74, 1631–1642 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Ahn, S.-J., Ahn, S.-J., Wen, Z. T., Brady, L. J. & Burne, R. A. Characteristics of biofilm formation by Streptococcus mutans in the presence of saliva. Infect. Immun. 76, 4259–4268 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Duarte, S. et al. Influences of starch and sucrose on Streptococcus mutans biofilms. Oral Microbiol. Immunol. 23, 206–212 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Mitchell, T. J. The pathogenesis of streptococcal infections: from tooth decay to meningitis. Nat. Rev. Microbiol. 1, 219–230 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Frenkel, E. S. & Ribbeck, K. Salivary mucins protect surfaces from colonization by cariogenic bacteria. Appl. Environ. Microbiol. 81, 332–338 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
23.Frenkel, E. S. & Ribbeck, K. Salivary mucins promote the coexistence of competing oral bacterial species. ISME J. 11, 1286–1290 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Levine, M. Salivary proteins may be useful for determining caries susceptibility. J. Evid. Based Dent. Pract. 13, 91–93 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Thomsson, K. A., Schulz, B. L., Packer, N. H. & Karlsson, N. G. MUC5B glycosylation in human saliva reflects blood group and secretor status. Glycobiology 15, 791–804 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Ajdic, D. et al. Genome sequence of Streptococcus mutans UA159, a cariogenic dental pathogen. Proc. Natl Acad. Sci. USA 99, 14434–14439 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Paik, S., Brown, A., Munro, C. L., Cornelissen, C. N. & Kitten, T. The sloABCR operon of Streptococcus mutans encodes an Mn and Fe transport system required for endocarditis virulence and its Mn-dependent repressor. J. Bacteriol. 185, 5967–5975 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Nicolas, G. G. Detection of putative new mutacins by bioinformatic analysis using available web tools. BioData Min. 4, 22 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Aframian, N. & Eldar, A. A bacterial tower of Babel: quorum-sensing signaling diversity and its evolution. Annu. Rev. Microbiol. 74, 587–606 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Merritt, J., Qi, F. & Shi, W. A unique nine-gene comY operon in Streptococcus mutans. Microbiology 151, 157–166 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Underhill, S. A. M. et al. Intracellular signaling by the comRS system in Streptococcus mutans genetic competence. mSphere 3, e00444-18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Dufour, D., Cordova, M., Cvitkovitch, D. G. & Lévesque, C. M. Regulation of the competence pathway as a novel role associated with a streptococcal bacteriocin. J. Bacteriol. 193, 6552–6559 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Hossain, M. S. & Biswas, I. Mutacins from Streptococcus mutans UA159 are active against multiple streptococcal species. Appl. Environ. Microbiol. 77, 2428–2434 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Merritt, J. & Qi, F. The mutacins of Streptococcus mutans: regulation and ecology. Mol. Oral. Microbiol 27, 57–69 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Son, M., Shields, R. C., Ahn, S. J., Burne, R. A. & Hagen, S. J. Bidirectional signaling in the competence regulatory pathway of Streptococcus mutans. FEMS Microbiol. Lett. 362, fnv159 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Reck, M., Tomasch, J. & Wagner-Döbler, I. The alternative sigma factor SigX controls bacteriocin synthesis and competence, the two quorum sensing regulated traits in Streptococcus mutans. PLoS Genet. 11, e1005353 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Perry, J. A., Cvitkovitch, D. G. & Lévesque, C. M. Cell death in Streptococcus mutans biofilms: a link between CSP and extracellular DNA. FEMS Microbiol. Lett. 299, 261–266 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Wenderska, I. B. et al. A novel function for the competence inducing peptide, XIP, as a cell death effector of Streptococcus mutans. FEMS Microbiol. Lett. 336, 104–112 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Perry, D. & Kuramitsu, H. K. Genetic transformation of Streptococcus mutans. Infect. Immun. 32, 1295–1297 (1981).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Desai, K., Mashburn-Warren, L., Federle, M. J. & Morrison, D. A. Development of competence for genetic transformation of Streptococcus mutans in a chemically defined medium. J. Bacteriol. 194, 3774–3780 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Khan, R. et al. Extracellular identification of a processed type II ComR/ComS pheromone of Streptococcus mutans. J. Bacteriol. 194, 3781–3788 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Khan, R. et al. A positive feedback loop mediated by Sigma X enhances expression of the streptococcal regulator ComR. Sci. Rep. 7, 5984 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Nakano, K. et al. Streptococcus mutans clonal variation revealed by multilocus sequence typing. J. Clin. Microbiol. 45, 2616–2625 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mukherjee, S. & Bassler, B. L. Bacterial quorum sensing in complex and dynamically changing environments. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-019-0186-5 (2019).45.Visch, L. L., Gravenmade, E. J., Schaub, R. M., Van Putten, W. L. & Vissink, A. A double-blind crossover trial of CMC- and mucin-containing saliva substitutes. Int. J. Oral Max. Surg. 15, 395–400 (1986).CAS 
Article 

Google Scholar 
46.Silverman, H. S. et al. In vivo glycosylation of mucin tandem repeats. Glycobiology 11, 459–471 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Zalewska, A., Zwierz, K., Zółkowski, K. & Gindzieński, A. Structure and biosynthesis of human salivary mucins. Acta Biochim. Pol. 47, 1067–1079 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Wheeler, K. M. et al. Mucin glycans attenuate the virulence of Pseudomonas aeruginosa in infection. Nat. Microbiol. 4, 2146–2154 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Werlang, C., Cárcarmo-Oyarce, G. & Ribbeck, K. Engineering mucus to study and influence the microbiome. Nat. Rev. Mater. https://doi.org/10.1038/s41578-018-0079-7 (2019).50.Wang, B. X. et al. Mucin glycans signal through the sensor kinase RetS to inhibit virulence-associated traits in Pseudomonas aeruginosa. Curr. Biol. 31, 90–102 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Huang, Y., Mechref, Y. & Novotny, M. V. Microscale nonreductive release of O-Linked glycans for subsequent analysis through MALDI mass spectrometry and capillary electrophoresis. Anal. Chem. 73, 6063–6069 (2001).CAS 
PubMed 
Article 

Google Scholar 
52.Khan, R. et al. Comprehensive transcriptome profiles of Streptococcus mutans UA159 map core streptococcal competence genes. mSystems 1, e00038 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Rayment, S. A., Liu, B., Offner, G. D., Oppenheim, F. G. & Troxler, R. F. Immunoquantification of human salivary mucins MG1 and MG2 in stimulated whole saliva: factors influencing mucin levels. J. Dent. Res. 79, 1765–1772 (2000).CAS 
PubMed 
Article 

Google Scholar 
54.Son, M., Ahn, S.-J., Guo, Q., Burne, R. A. & Hagen, S. J. Microfluidic study of competence regulation in Streptococcus mutans: environmental inputs modulate bimodal and unimodal expression of comX. Mol. Microbiol. 86, 258–272 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Ricomini Filho, A. P., Khan, R., Åmdal, H. A. & Petersen, F. C. Conserved pheromone production, response and degradation by Streptococcus mutans. Front. Microbiol. 10, 2140 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Hagen, S. J. & Son, M. Origins of heterogeneity in Streptococcus mutans competence: interpreting an environment-sensitive signaling pathway. Phys. Biol. 14, 015001 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Hillman, J. D., Mo, J., McDonell, E., Cvitkovitch, D. & Hillman, C. H. Modification of an effector strain for replacement therapy of dental caries to enable clinical safety trials. J. Appl. Microbiol. 102, 1209–1219 (2007).CAS 
PubMed 
Article 

Google Scholar 
58.Singla, D., Sharma, A., Sachdev, V. & Chopra, R. Distribution of Streptococcus mutans and Streptococcus sobrinus in dental plaque of indian pre-school children using PCR and SB-20M agar medium. J. Clin. Diagn. Res. 10, ZC60–ZC63 (2016).PubMed 
PubMed Central 

Google Scholar 
59.Rodriguez, A. M. et al. Physiological and molecular characterization of genetic competence in Streptococcus sanguinis. Mol. Oral Microbiol. 26, 99–116 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Darch, S. E. et al. Spatial determinants of quorum signaling in a Pseudomonas aeruginosa infection model. Proc. Natl Acad. Sci. USA 115, 4779–4784 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Wu, C. et al. Regulation of ciaXRH operon expression and identification of the CiaR regulon in Streptococcus mutans. J. Bacteriol. 192, 4669–4679 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Qi, F., Merritt, J., Lux, R. & Shi, W. Inactivation of the ciaH gene in Streptococcus mutans diminishes mutacin production and competence development, alters sucrose-dependent biofilm formation, and reduces stress tolerance. Infect. Immun. 72, 4895–4899 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Biswas, S. & Biswas, I. Role of HtrA in surface protein expression and biofilm formation by Streptococcus mutans. Infect. Immun. 73, 6923–6934 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Senadheera, M. D. et al. A VicRK signal transduction system in Streptococcus mutans affects gtfBCD, gbpB, and ftf expression, biofilm formation, and genetic competence development. J. Bacteriol. 187, 4064–4076 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Domenech, A. et al. Proton motive force disruptors block bacterial competence and horizontal gene transfer. Cell Host Microbe 27, 544–555 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Merritt, J., Zheng, L., Shi, W. & Qi, F. Genetic characterization of the hdrRM operon: a novel high-cell-density-responsive regulator in Streptococcus mutans. Microbiology 153, 2765–2773 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Okinaga, T., Niu, G., Xie, Z., Qi, F. & Merritt, J. The hdrRM operon of Streptococcus mutans encodes a novel regulatory system for coordinated competence development and bacteriocin production. J. Bacteriol. 192, 1844–1852 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Alves, L. A. et al. PepO is a target of the two-component systems VicRK and CovR required for systemic virulence of Streptococcus mutans. Virulence 11, 521–536 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Underhill, S. A. M., Shields, R. C., Burne, R. A. & Hagen, S. J. Carbohydrate and PepO control bimodality in competence development by Streptococcus mutans. Mol. Microbiol. 112, 1388–1402 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Kaspar, J. R., Lee, K., Richard, B., Walker, A. R. & Burne, R. A. Direct interactions with commensal streptococci modify intercellular communication behaviors of Streptococcus mutans. ISME J. https://doi.org/10.1038/s41396-020-00789-7 (2020).71.Idone, V. et al. Effect of an orphan response regulator on Streptococcus mutans sucrose-dependent adherence and cariogenesis. Infect. Immun. 71, 4351–4360 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Nagasawa, R., Sato, T. & Senpuku, H. Raffinose induces biofilm formation by Streptococcus mutans in low concentrations of sucrose by increasing production of extracellular DNA and fructan. Appl. Environ. Microbiol. 83, e00869 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Suzuki, Y., Nagasawa, R. & Senpuku, H. Inhibiting effects of fructanase on competence-stimulating peptide-dependent quorum sensing system in Streptococcus mutans. J. Infect. Chemother. 23, 634–641 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Yoshida, A., Ansai, T., Takehara, T. & Kuramitsu, H. K. LuxS-based signaling affects Streptococcus mutans biofilm formation. Appl. Environ. Microbiol. 71, 2372–2380 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Son, M., Ghoreishi, D., Ahn, S.-J., Burne, R. A. & Hagen, S. J. Sharply tuned pH response of genetic competence regulation in Streptococcus mutans: a microfluidic study of the environmental sensitivity of comX. Appl. Environ. Microbiol. 81, 5622–5631 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Nielsen, S. S. in Food Analysis Laboratory Manual 137–141 (Springer, 2017).77.Aoki, K. et al. The diversity of O-linked glycans expressed during Drosophila melanogaster development reflects stage- and tissue-specific requirements for cell signaling. J. Biol. Chem. 283, 30385–30400 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Kumagai, T., Katoh, T., Nix, D. B., Tiemeyer, M. & Aoki, K. In-gel β-elimination and aqueous-organic partition for improved O- and sulfoglycomics. Anal. Chem. 85, 8692–8699 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Anumula, K. R. & Taylor, P. B. A comprehensive procedure for preparation of partially methylated alditol acetates from glycoprotein carbohydrates. Anal. Biochem. 203, 101–108 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Liu, Y. et al. The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting glycan microarray-based data. Glycobiology 27, 280–284 (2017).CAS 
PubMed 

Google Scholar 
81.Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
PubMed 
Article 

Google Scholar 
82.O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).Article 
CAS 

Google Scholar 
83.Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 
Article 

Google Scholar 
87.Thissen, D., Steinberg, L. & Kuang, D. Quick and easy implementation of the Benjamini–Hochberg procedure for controlling the false positive rate in multiple comparisons. J. Educ. Behav. Stat. 27, 77–83 (2002).Article 

Google Scholar 
88.Aymanns, S., Mauerer, S., Zandbergen, G., Wolz, C. & Spellerberg, B. High-level fluorescence labeling of Gram-positive pathogens. PLoS ONE 6, e19822 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Takehara, S., Yanagishita, M., Podyma-Inoue, K. A. & Kawaguchi, Y. Degradation of MUC7 and MUC5B in human saliva. PLoS ONE 8, e69059 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More