Ecology

1.
Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, Pretoria, 2006).
Google Scholar 
2.
van Zinderen Bakker, E. M. The evolution of late Quaternary paleoclimates of Southern Africa. Palaeoecol. Afr. 9, 160–202 (1976).
Google Scholar 

3.
Cockcroft, M. J., Wilkinson, M. J. & Tyson, P. D. The application of a present-day climatic model to the late Quaternary in southern Africa. Clim. Change 10, 161–181 (1987).
ADS  Google Scholar 

4.
Chase, B. M. & Meadows, M. E. Late Quaternary dynamics of southern Africa’s winter rainfall zone. Earth Sci. Rev. 84(3), 103–138 (2007).
ADS  Google Scholar 

5.
Bistinas, I., Harrison, S. P., Prentice, I. C. & Pereira, J. M. C. Causal relationships vs. emergent patterns in the global controls of fire frequency. Biogeosciences 11, 5087–5101 (2014).
ADS  Google Scholar 

6.
Hoetzel, S., Dupont, L., Schefuß, E., Rommerskirchen, F. & Wefer, G. The role of fire in Miocene to Pliocene C 4 grassland and ecosystem evolution. Nat. Geosci. 6(12), 1027–1030 (2013).
ADS  CAS  Google Scholar 

7.
Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165(2), 525–538 (2005).
CAS  PubMed  Google Scholar 

8.
Ripley, B. et al. Fire ecology of C3 and C4 grasses depends on evolutionary history and frequency of burning but not photosynthetic type. Ecology 96(10), 2679–2691 (2015).
PubMed  Google Scholar 

9.
Pinto, H., Sharwood, R. E., Tissue, D. T. & Ghannoum, O. Photosynthesis of C3, C3–C4, and C4 grasses at glacial CO2. J. Exp. Bot. 65(13), 3669–3681 (2014).
PubMed  PubMed Central  Google Scholar 

10.
Roth-Nebelsick, A. & Konrad, W. Habitat responses of fossil plant species to palaeoclimate—possible interference with CO2?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 467, 277–286 (2017).
Google Scholar 

11.
Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112(3), 285–299 (1997).
ADS  PubMed  Google Scholar 

12.
Edwards, E. J., Osborne, C. P., Strömberg, C. A., Smith, S. A. & C4 Grasses Consortium. The origins of C4 grasslands: integrating evolutionary and ecosystem science. Science 328(5978), 587–591 (2010).
CAS  PubMed  Google Scholar 

13.
Hönisch, B., Hemming, N. G., Archer, D., Siddall, M. & McManus, J. F. Atmospheric carbondioxide concentration across the mid-Pleistocene transition. Science 324(5934), 1551–1554 (2009).
ADS  PubMed  Google Scholar 

14.
Yan, Y. et al. Two-million-year-old snapshots of atmospheric gases from Antarctic ice. Nature 574(7780), 663–666 (2019).
ADS  CAS  PubMed  Google Scholar 

15.
Faith, J. T., Rowan, J. & Du, A. Early hominins evolved within non-analog ecosystems. Proc. Natl. Acad. Sci. 116(43), 21478–21483 (2019).
ADS  CAS  PubMed  Google Scholar 

16.
Sealy, J., Naidoo, N., Hare, V. J., Brunton, S. & Faith, J. T. Climate and ecology of the palaeo-Agulhas Plain from stable carbon and oxygen isotopes in bovid tooth enamel from Nelson Bay Cave, South Africa. Quat. Sci. Rev. 235, 105974 (2019).
Google Scholar 

17.
Horwitz, L. K. & Chazan, M. Past and present at Wonderwerk Cave (Northern Cape Province, South Africa). Afr. Archaeol. Rev. 32(4), 595–612 (2015).
Google Scholar 

18.
Ecker, M. et al. The palaeoecological context of the Oldowan-Acheulean in southern Africa. Nat. Ecol. Evol. 2(7), 1080–1086 (2018).
PubMed  Google Scholar 

19.
Matmon, A. et al. New chronology for the southern Kalahari Group sediments with implications for sediment-cycle dynamics and early hominin occupation. Quat. Res. 84(1), 118–132 (2015).
Google Scholar 

20.
Vainer, S., Erel, Y. & Matmon, A. Provenance and depositional environments of Quaternary sediments in the southern Kalahari Basin. Chem. Geol. 476, 352–369 (2018).
ADS  CAS  Google Scholar 

21.
Prentice, I. C. et al. Modeling fire and the terrestrial carbon balance. Glob. Biogeochem. Cycles 25(3), 2–13 (2011).
Google Scholar 

22.
Braconnot, P. et al. Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum-Part 1: experiments and large-scale features. Clim. Past 3(2), 261–277 (2007).
Google Scholar 

23.
Kelley, D. I. et al. A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences 10(5), 3313–3340 (2013).
ADS  Google Scholar 

24.
Chazan, M. et al. Archaeology, paleoenvironment and chronology of the early middle stone age component of Wonderwerk cave in the interior of southern Africa. J. Palaeolithic Archaeol. https://doi.org/10.1007/s41982-020-00051-8 (2020).
Article  Google Scholar 

25.
Lee-Thorp, J. A. & Beaumont, P. B. Vegetation and seasonality shifts during the late Quaternary deduced from 13C/12C ratios of grazers at Equus Cave, South Africa. Quat. Res. 43, 426–432 (1995).
Google Scholar 

26.
Vogel, J. C. The geographical distribution of Kranz species in southern Africa. South Afr. J. Sci. 75, 209–215 (1978).
Google Scholar 

27.
Zhou, H., Helliker, B. R., Huber, M., Dicks, A. & Akçay, E. C4 photosynthesis and climate through the lens of optimality. Proc. Natl. Acad. Sci. 115(47), 12057–12062 (2018).
CAS  PubMed  Google Scholar 

28.
Rubin, F., Palmer, A. R. & Tyson, C. Patterns of endemism within the Karoo National Park, South Africa. Bothalia 31(1), 117–133 (2001).
Google Scholar 

29.
Walker, S. J., Lukich, V. & Chazan, M. Kathu townlands: a high density earlier stone age locality in the interior of South Africa. PLoS ONE 9(7), e103436 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

30.
Lee-Thorp, J. A., Sponheimer, M. & Luyt, J. Tracking changing environments using stable carbon isotopes in fossil tooth enamel: an example from the South African hominin sites. J. Hum. Evol. 53(5), 595–601 (2007).
PubMed  Google Scholar 

31.
Codron, D., Brink, J. S., Rossouw, L. & Clauss, M. The evolution of ecological specialization in southern African ungulates: competition- or physical environmental turnover?. Oikos 117, 344–353 (2008).
Google Scholar 

32.
Plummer, T. W. et al. The environmental context of Oldowan hominin activities at Kanjera South, Kenya. In Interdisciplinary approaches to the Oldowan (eds Hovers, E. & Braun, D. R.) 149–160 (Springer, Berlin, 2009).
Google Scholar 

33.
Cerling, T. E. et al. Dietary changes of large herbivores in the Turkana Basin, Kenya from 4 to 1 Ma. Proc. Natl. Acad. Sci. 112(37), 11467–11472 (2015).
ADS  CAS  PubMed  Google Scholar 

34.
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. https://doi.org/10.1038/s41597-020-0453-3 (2020).
Article  PubMed  PubMed Central  Google Scholar 

35.
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9(2), 161–185 (2003).
ADS  Google Scholar 

36.
Thonicke, K. et al. The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7(6), 1991–2011 (2010).
ADS  CAS  Google Scholar 

37.
Haxeltine, A. & Prentice, I. C. BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Glob. Biogeochem. Cycles 10(4), 693–709 (1996).
ADS  CAS  Google Scholar 

38.
Haxeltine, A. & Prentice, I. C. A general model for the light-use efficiency of primary production. Funct. Ecol. 10, 551–561 (1996).
Google Scholar 

39.
Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 plants. Planta 149, 78–90 (1980).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Farquhar, G. D. & Von Caemmerer, S. Modelling of photosynthetic response to environmental conditions. In Physiological Plant Ecology II: Water Relations and Carbon Assimilation (eds Nobel, P. S. et al.) 549–587 (Springer, Berlin, 1982).
Google Scholar 

41.
Monteith, J. L. A reinterpretation of stomatal responses to humidity. Plant Cell Environ. 18, 357–364 (1995).
Google Scholar 

42.
Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels (Vol. 115). Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture (1972).

43.
Sato, H., Kelley, D. I., Mayor, S. J., Cowling S. A., Calvo, M. M. & Prentice, I. C. Fire and low CO2 opened dry corridors in South America during the Last Glacial Maximum. Under Review for Nature Geosciences: NGS-2019–07–01558B (2020).

44.
Prentice, I. C., Harrison, S. P. & Bartlein, P. J. Global vegetation and terrestrial carbon cycle changes after the last ice age. New Phytol. 189(4), 988–998 (2011).
CAS  PubMed  Google Scholar  More

Cohort and data description
In order to examine seasonal changes of human molecular data, we leveraged the power of longitudinal multiomics data from profiling of 105 individuals (55 women and 50 men) with ages ranging from 25 to 75 years old (Fig. 1a; Supplementary Table 1). This cohort was generally healthy and well characterized for glucose dysregulation using annual oral glucose tolerance tests (OGTTs), insulin resistance measuring steady-state plasma glucose (SSPG), fasting glucose and hemoglobin A1c (HbA1c; an indicator of the average level of blood glucose over the past 100 days)19 as well as quarterly sample collections with measurements of transcriptomes (from peripheral blood mononuclear cells), proteome and metabolome from plasma, targeted cytokine and growth factor assays using serum. Nasal and gut microbiomes were analyzed using 16S rRNA sequencing providing information at the genus level and host exome sequencing was performed once from PBMCs (Fig. 1b). Moreover, 51 clinical laboratory tests were acquired on each visit and they were aligned to the meteorological data (e.g. air temperature), pollen counts (e.g. mold spores, grass pollens, tree pollens, weed pollens) and airborne fungi from the San Francisco bay area. In total, there were 902 visits (average across different types of omes‘) from which samples were drawn over up to 4 years (see “Methods”). The sample collections were generally evenly distributed throughout the year (Fig. 1b). Nearly all individuals lived in the San Francisco Bay Area with the exception of three individuals who lived in Southern California and frequented the Bay area (Supplementary Data 1). Participants in our study were well characterized for steady-state plasma glucose (SSPG) using the modified insulin suppression test20, in which 31 participants were insulin sensitive (SSPG  0.05, Supplementary Table 5, Supplementary Fig. 10). In our analysis we used subject ID as a random effect to account for different numbers of samples per subject. On the other hand, physical activity measured in total metabolic equivalent of task (MET) is significantly different between the IR and the IS groups in February, May, June, and August (P-value = 0.01787, Supplementary Fig. 11). However, a post-hoc analysis of all the omics features that were identified to be significantly different between the IR and the IS groups, are not associated with the physical activity. More

1.
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019—Data Booklet (ST/ESA/ SER.A/377), (2019). https://population.un.org/wpp/Publications/Files/WPP2019_DataBooklet.pdf
2.
Pimentel, D. & Burgess, M. Environmental and economic costs of the application of pesticides primarily in the United States. In Integrated Pest Management: Innovation-Development Process (eds Peshin, R. & Dhawan, A. K.) 47–71 (Springer, Dordrecht, 2014). https://doi.org/10.1007/978-1-4020-8992-3_4
Google Scholar 

3.
Devine, G. J. & Furlong, M. J. Insecticide use: Contexts and ecological consequences. Agric. Hum. Values 24(3), 281–306. https://doi.org/10.1007/s10460-007-9067-z (2007).
Article  Google Scholar 

4.
Sanchis, V. & Bourguet, D. Bacillus thuringiensis: Applications in agriculture and insect resistance management. A review. Agron. Sustain. Dev. 28(1), 11–20. https://doi.org/10.1051/agro:2007054 (2008).
Article  Google Scholar 

5.
WHO report. WHO specifications and evaluations for public health pesticides: Bacillus thuringiensis subspecies israelensis strain AM65-52. (World Health Organization, Geneva, 2007).

6.
Rizzati, V., Briand, O., Guillou, H. & Gamet-Payrastre, L. Effects of pesticide mixtures in human and animal models: An update of the recent literature. Chem. Biol. Interact. 254, 231–246. https://doi.org/10.1016/j.cbi.2016.06.003 (2016).
Article  PubMed  CAS  Google Scholar 

7.
Lacey, L. A. et al. Insect pathogens as biological control agents: Back to the future. J. Invertebr. Pathol. 132, 1–41. https://doi.org/10.1016/j.jip.2015.07.009 (2015).
Article  PubMed  CAS  Google Scholar 

8.
Adang, M. J., Crickmore, N. & Jurat-Fuentes, J. L. Diversity of Bacillus thuringiensis Crystal Toxins and Mechanism of Action. Adv. Insect Physiol. 47, 39–87. https://doi.org/10.1016/B978-0-12-800197-4.00002-6 (2014).
Article  Google Scholar 

9.
Crickmore, N. Bacillus thuringiensis toxin classification. In Bacillus thuringiensis and Lysinibacillus sphaericus. (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 41-52, (Spinger, Cham, 2017).

10.
WHO report. Guideline specification for bacterial larvicides for public health use. WHO document WHO/CDS/CPC/WHOPES/99.2 (World Health Organization, Geneva, 1999).

11.
Bravo, A., Pacheco, S., Gomez, I., Garcia-Gomez B., Onofre, J., Soberon, M. Insecticidal Proteins from Bacillus thuringiensis and their Mechanism of Action. In Bacillus thuringiensis and Lysinibacillus sphaericus (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 53–66, (Spinger, Cham, 2017).

12.
Palma, L., Muñoz, D., Berry, C., Murillo, J. & Caballero, P. Bacillus thuringiensis toxins: An overview of their biocidal activity. Toxins 6(12), 3296–3325. https://doi.org/10.3390/toxins6123296 (2014).
Article  PubMed  PubMed Central  CAS  Google Scholar 

13.
Ben-Dov, E. et al. Extended screening by PCR for seven cry-group genes from field-collected strains of Bacillus thuringiensis. Appl. Environ. Microb. 63(12), 4883–4890. https://doi.org/10.1128/aem.63.12.4883-4890.1997 (1997).
CAS  Google Scholar 

14.
Berry, C. et al. Complete sequence and organization of pBtoxis, the toxin-coding plasmid of Bacillus thuringiensis subsp. israelensis. Appl. Environ. Microbiol. 68(10), 5082–5095. https://doi.org/10.1128/aem.68.10.5082-5095.2002 (2002).
Article  PubMed  PubMed Central  CAS  Google Scholar 

15.
Bravo, A., Gill, S. S. & Soberon, M. Mode of action of Bacillus thuringiensis Cry and Cyt toxins and their potential for insect control. Toxicon 49, 423–435. https://doi.org/10.1016/j.toxicon.2006.11.022 (2007).
Article  PubMed  CAS  Google Scholar 

16.
Wei, J. et al. Activation of Bt protoxin Cry1Ac in resistant and susceptible cotton bollworm. PLoS ONE 11(6), e0156560. https://doi.org/10.1371/journal.pone.0156560 (2016).
Article  PubMed  PubMed Central  CAS  Google Scholar 

17.
Bravo, A., Likitvivatanavong, S., Gill, S. S. & Soberon, M. Bacillus thuringiensis: A story of a successful bioinsecticide. Insect Biochem. Mol. Biol. 41(7), 423–431. https://doi.org/10.1016/j.ibmb.2011.02.006 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

18.
Caccia, S. et al. Midgut microbiota and host immunocompetence underlie Bacillus thuringiensis killing mechanism. Proc. Natl. Acad. Sci. USA 113(34), 9486–9491. https://doi.org/10.1073/pnas.1521741113 (2016).
Article  PubMed  CAS  Google Scholar 

19.
Glare, T.R., O’Callaghan, M. Bacillus thuringiensis: Biology, Ecology and Safety. ISBN: 9780471496304, 350, (Wiley, New York, 2000).

20.
Rubio-Infante, N. & Moreno-Fierros, L. An overview of the safety and biological effects of Bacillus thuringiensis Cry toxins in mammals. J. Appl. Toxicol. 36, 630–648. https://doi.org/10.1002/jat.3252 (2016).
Article  PubMed  CAS  Google Scholar 

21.
EFSA Panel on Biological Hazards (BIOHAZ). Risks for public health related to the presence of Bacillus cereus and other Bacillus spp. including Bacillus thuringiensis in foodstuffs. EFSA J. https://doi.org/10.2903/j.efsa.2016.4524 (2016).
Article  Google Scholar 

22.
Amichot, M., Curty, C., Benguettat-Magliano, O., Gallet, A. & Wajnberg, E. Side effects of Bacillus thuringiensis var. kurstaki on the hymenopterous parasitic wasp Trichogramma chilonis. Environ. Sci. Pollut. Res. Int. 23, 3097–3103. https://doi.org/10.1007/s11356-015-5830-7 (2016).
Article  PubMed  CAS  Google Scholar 

23.
Renzi, M. T. et al. Chronic toxicity and physiological changes induced in the honey bee by the exposure to fipronil and Bacillus thuringiensis spores alone or combined. Ecotoxicol. Environ. Saf. 127, 205–213. https://doi.org/10.1016/j.ecoenv.2016.01.028 (2016).
Article  PubMed  CAS  Google Scholar 

24.
Caquet, T., Roucaute, M., Le Goff, P. & Lagadic, L. Effects of repeated field applications of two formulations of Bacillus thuringiensis var. israelensis on non-target saltmarsh invertebrates in Atlantic coastal wetlands. Ecotoxicol. Environ. Saf. 74, 1122–1130. https://doi.org/10.1016/j.ecoenv.2011.04.028 (2011).
Article  PubMed  CAS  Google Scholar 

25.
Duguma, D. et al. Microbial communities and nutrient dynamics in experimental microcosms are altered after the application of a high dose of Bti. J. Appl. Ecol. 52, 763–773. https://doi.org/10.1111/1365-2664.12422 (2015).
Article  CAS  Google Scholar 

26.
Venter, H. J. & Bøhn, T. Interactions between Bt crops and aquatic ecosystems: A review. Environ. Toxicol. Chem. 35(12), 2891–2902. https://doi.org/10.1002/etc.3583 (2016).
Article  PubMed  CAS  Google Scholar 

27.
van Frankenhuyzen, K. Specificity and cross-order activity of Bacillus thuringiensis pesticidal proteins. In Bacillus thuringiensis and Lysinibacillus sphaericus (eds Fiuza, L.M. et al.) ISBN 978-3-319-56677-1, 127–172, (Springer, Cham, 2017).

28.
Bizzarri, M. F. & Bishop, A. H. The ecology of Bacillus thuringiensis on the phylloplane: Colonization from soil, plasmid transfer, and interaction with larvae of Pieris brassicae. Microb. Ecol. 56(1), 133–139. https://doi.org/10.1007/s00248-007-9331-1 (2008).
Article  PubMed  CAS  Google Scholar 

29.
Raymond, B., Wyres, K. L., Sheppard, S. K., Ellis, R. J. & Bonsall, M. B. Environmental factors determining the epidemiology and population genetic structure of the Bacillus cereus group in the field. PLoS Pathog. 6(5), e1000905. https://doi.org/10.1371/journal.ppat.1000905 (2010).
Article  PubMed  PubMed Central  CAS  Google Scholar 

30.
Hendriksen, N. B. & Hansen, B. M. Long-term survival and germination of Bacillus thuringiensis var. kurstaki in a field trial. Can. J. Microbiol. 48(3), 256–261. https://doi.org/10.1139/w02-009 (2002).
Article  PubMed  CAS  Google Scholar 

31.
Hung, T. P. et al. Persistence of detectable insecticidal proteins from Bacillus thuringiensis (Cry) and toxicity after adsorption on contrasting soils. Environ. Pollut. 208, 318–325. https://doi.org/10.1016/j.envpol.2015.09.046 (2016).
Article  PubMed  CAS  Google Scholar 

32.
Hung, T. P. et al. Fate of insecticidal Bacillus thuringiensis Cry protein in soil: Differences between purified toxin and biopesticide formulation. Pest Manag. Sci. 72, 2247–2253. https://doi.org/10.1002/ps.4262 (2016).
Article  PubMed  CAS  Google Scholar 

33.
Enger, K. S. et al. Evaluating the long-term persistence of Bacillus spores on common surfaces. Microb. Biotechnol. 11(6), 1048–1059. https://doi.org/10.1111/1751-7915.13267 (2018).
Article  PubMed  PubMed Central  CAS  Google Scholar 

34.
Couch, T.L. Industrial fermentation and formulation of entomopathogenic bacteria. In Entomopathogenic Bacteria: From Laboratory to Field Application (eds Charles, J.-F. et al.) ISBN 978-90-481-5542-2, 297–316.43, (Springer, Dordrecht, 2000).

35.
Brar, S. K., Verma, M., Tyagi, R. D. & Valéro, J. R. Recent advances in downstream processing and formulations of Bacillus thuringiensis based biopesticides. Process Biochem. 41(2), 323–342. https://doi.org/10.1016/j.procbio.2005.07.015 (2006).
Article  CAS  Google Scholar 

36.
Setlow, P. Spore resistance properties. Microbiol. Spectr. 2(5), TBS-0003-2012. https://doi.org/10.1128/microbiolspec.TBS-0003-2012 (2014).
Article  CAS  Google Scholar 

37.
European Food Safety Authority. Conclusion on the peer review of the pesticide risk assessment of the active substance Bacillus thuringiensis subsp. Kurstaki (strains ABTS 351, PB 54, SA 11, SA 12, EG 2348). EFSA J. 10(2), 2540. https://doi.org/10.2903/j.efsa.2012.2540 (2012).
Article  CAS  Google Scholar 

38.
Bächli, G. TaxoDros: The database on Taxonomy of Drosophilidae: Database 2020/1.https://www.taxodros.uzh.ch. (1999–2020).

39.
Tennessen, J. M. & Thummel, C. S. Coordinating growth and maturation—Insights from Drosophila. Curr. Biol. 21(18), R750–R757. https://doi.org/10.1016/j.cub.2011.06.033 (2011).
Article  PubMed  PubMed Central  CAS  Google Scholar 

40.
Benz, G. & Perron, J. M. The toxic action of Bacillus thuringiensis “exotoxin” on Drosophila reared in yeast-containing and yeast-free media. Experientia 23(10), 871–872 (1967).
PubMed  CAS  Google Scholar 

41.
Saadoun, I., Al-Moman, F., Obeidat, M., Meqdam, M. & Elbetieha, A. Assessment of toxic potential of local Jordanian Bacillus thuringiensis strains on Drosophila melanogaster and Culex sp. (Diptera). J. Appl. Microbiol. 90, 866–872. https://doi.org/10.1046/j.1365-2672.2001.01315.x (2001).
Article  PubMed  CAS  Google Scholar 

42.
Khyami-Horani, H. Toxicity of Bacillus thuringiensis and B. sphaericus to laboratory populations of Drosophila melanogaster (Diptera: Drosophilidae). J. Basic Microbiol. 42(2), 105–110. https://doi.org/10.1002/1521-4028(200205)42:23.0.CO;2-S (2002). 
Article  PubMed  Google Scholar 

43.
Obeidat, M. Toxicity of local Bacillus thuringiensis isolates against Drosophila melanogaster. WJAS 4(2), 161–167 (2008).
Google Scholar 

44.
Obeidat, M., Khymani-Horani, H. & Al-Momani, F. Toxicity of Bacillus thuringiensis β-exotoxins and δ-endotoxins to Drosophila melanogaster, Ephestia kuhniella and human erythrocytes. Afr. J. Biotechnol. 11(46), 10504–10512 (2012).
Google Scholar 

45.
Cossentine, J., Robertson, M. & Xu, D. Biological activity of Bacillus thuringiensis in Drosophila suzukii (Diptera: Drosophilidae). J. Econ. Entomol. 109(3), 1–8. https://doi.org/10.1093/jee/tow062 (2016).
Article  CAS  Google Scholar 

46.
Biganski, S., Jehle, J. A. & Kleepies, R. G. Bacillus thuringiensis serovar israelensis has no effect on Drosophila suzukii Matsumura. J. Appl. Entomol. 142, 33–36. https://doi.org/10.1111/jen.12415 (2017).
Google Scholar 

47.
Haller, S., Romeis, J. X. R. & Meissle, M. Effects of purified or plant-produced Cry proteins on Drosophila melanogaster (Diptera: Drosophilidae) larvae. Sci. Rep. 7(1), 11172. https://doi.org/10.1038/s41598-017-10801-4 (2017).
ADS  Article  PubMed  PubMed Central  CAS  Google Scholar 

48.
Benado, M. & Brncic, D. An eight-year phenological study of a local drosophilid community in Central Chile. J. Zool. Syst. Evol. Res. 32, 51–63. https://doi.org/10.1111/j.1439-0469.1994.tb00470.x (1994).
Article  Google Scholar 

49.
Nunney, L. The colonization of oranges by the cosmopolitan Drosophila. Oecologia 108, 552–561. https://www.jstor.org/stable/4221451 (1996).
ADS  PubMed  Google Scholar 

50.
Mitsui, H. & Kimura, M. T. Coexistence of drosophilid flies: Aggregation, patch size diversity and parasitism. Ecol. Res. 15, 93–100.  https://doi.org/10.1046/j.1440-1703.2000.00328.x (2000).
Google Scholar 

51.
Withers, P. & Allemand, R. Les drosophiles de la région Rhône-Alpes (Diptera, Drosophilidae). Bull. Soc. Entomol. Fr. 117(4), 473–482. https://www.persee.fr/doc/bsef_0037-928x_2012_num_117_4_3076 (2012).
Google Scholar 

52.
Stevens, T., Song, S., Bruning, J. B., Choo, A. & Baxter, S. W. Expressing a moth abcc2 gene in transgenic Drosophila causes susceptibility to Bt Cry1Ac without requiring a cadherin-like protein receptor. Insect Biochem. Mol. Biol. 80, 61–70. https://doi.org/10.1016/j.ibmb.2016.11.008 (2017).
Article  PubMed  CAS  Google Scholar 

53.
George, Z., Crickmore, N. Bacillus thuringiensis applications in agriculture. In Bacillus thuringiensis Biotechnology (ed Sansinenea, E.) 392, (Springer, Dordrecht, 2012).

54.
Nepoux, V., Haag, C. R. & Kawecki, T. J. Effects of inbreeding on aversive learning in Drosophila. J. Evol. Biol. 23, 2333–2345. https://doi.org/10.1111/j.1420-9101.2010.02094.x (2010).
Article  PubMed  CAS  Google Scholar 

55.
Vantaux, A., Ouattarra, I., Lefèvre, T. & Dabiré, K. R. Effects of larvicidal and larval nutritional stresses on Anopheles gambiae development, survival and competence for Plasmodium falciparum. Parasite. Vector. 9, 226. https://doi.org/10.1186/s13071-016-1514-5 (2016).
Article  CAS  Google Scholar 

56.
Moret, Y. & Schmid-Hempel, P. Survival for immunity: The price of immune system activation for bumblebee workers. Science 290(5494), 1166–1168. https://doi.org/10.1126/science.290.5494.1166 (2000).
ADS  Article  PubMed  CAS  Google Scholar 

57.
Kutzer, M. A. & Armitage, S. A. O. The effect of diet and time after bacterial infection on fecundity, resistance, and tolerance in Drosophila melanogaster. Ecol. Evol. 6(13), 4229–4242. https://doi.org/10.1002/ece3.2185 (2016).
Article  PubMed  PubMed Central  Google Scholar 

58.
Andersen, L. H., Kristensen, T. N., Loeschcke, V., Toft, S. & Mayntz, D. Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster. J. Insect Physiol. 56, 336–340. https://doi.org/10.1016/j.jinsphys.2009.11.006 (2010).
Article  PubMed  CAS  Google Scholar 

59.
Rion, S. & Kawecki, T. J. Evolutionary biology of starvation resistance: What we have learned from Drosophila. J. Evol. Biol. 20(5), 1655–1664. https://doi.org/10.1111/j.1420-9101.2007.01405.x (2007).
Article  PubMed  CAS  Google Scholar 

60.
Burger, J. M. S., Buechel, S. D. & Kawecki, T. J. Dietary restriction affects lifespan but not cognitive aging in Drosophila melanogaster. Aging Cell 9, 327–335. https://doi.org/10.1111/j.1474-9726.2010.00560.x (2010).
Article  PubMed  CAS  Google Scholar 

61.
Khazaeli, A. A. & Curtsinger, J. W. Genetic analysis of extended lifespan in Drosophila melanogaster III. On the relationship between artificially selected and wild stocks. Genetica 109, 245–253. https://doi.org/10.1023/a:1017569318401 (2000).
Article  PubMed  CAS  Google Scholar 

62.
Atkinson, W. & Shorrocks, B. Breeding site specificity in the domestic species of Drosophila. Oecologia 29(3), 223–232. https://www.jstor.org/stable/4215461 (1977).
ADS  PubMed  CAS  Google Scholar 

63.
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manag. https://doi.org/10.1603/IPM10010 (2011).
Article  Google Scholar 

64.
Delbac, L. et al. Drosophila suzukii est-elle une menace pour la vigne?. Phytoma 679, 16–21 (2014).
Google Scholar 

65.
Poyet, M. et al. Invasive host for invasive pest: When the Asiatic cherry fly (Drosophila suzukii) meets the American black cherry (Prunus serotine) in Europe. Agric. For. Entomol. 16(3), 251–259. https://doi.org/10.1111/afe.12052 (2014).
Article  Google Scholar 

66.
Poulin, B., Lefebvre, G. & Paz, L. Red flag for green spray: Adverse trophic effects of Bti on breeding birds. J. Appl. Ecol. 47, 884–889. https://doi.org/10.1111/j.1365-2664.2010.01821.x (2010).
Article  Google Scholar 

67.
Zeigler, D.R. Bacillus genetic stock center catalog of strains, 7th edition. Part 2: Bacillus thuringiensis and Bacillus cereus. http://www.bgsc.org/_catalogs/Catpart2.pdf (1999).

68.
Gonzales, J. M. Jr., Brown, B. J. & Carlton, B. C. Transfer of Bacillus thuringiensis plasmids coding for δ-endotoxin among strains of B. thuringiensis and B. cereus. Proc. Natl Acad. Sci. USA 79, 6951–6955. https://doi.org/10.1073/pnas.79.22.6951 (1982).
ADS  Article  Google Scholar 

69.
Santos, M., Borash, D. J., Joshi, A., Bounlutay, N. & Mueller, L. D. Density-dependent natural selection in Drosophila: Evolution of growth rate and body size. Evolution 51(2), 420–432. https://doi.org/10.2307/2411114 (1997).
Article  PubMed  Google Scholar 

70.
Bradberry, S. M., Proudfoot, A. T. & Vale, J. A. Glyphosate poisoning. Toxicol. Rev. 23(3), 159–167. https://doi.org/10.2165/00139709-200423030-00003 (2004).
Article  PubMed  CAS  Google Scholar 

71.
R Development Core Team. R: A language and environment for statistical computing. ISBN 3-900051-07-0 https://www.R-project.org (R Foundation for Statistical Computing, Vienna, 2008).

72.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Google Scholar 

73.
Kosmidis I. brglm: Bias Reduction in Binary-Response Generalized Linear Models. R package version 0.6.1, https://www.ucl.ac.uk/~ucakiko/software.html, (2017).

74.
Horton, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50(3), 346–363. https://doi.org/10.1002/bimj.200810425 (2008).
MathSciNet  Article  Google Scholar 

75.
Therneau, T.M., Grambsch, P.M. Modeling Survival Data: Extending The Cox Model. ISBN 0-387-98784-3 (Springer, New York, 2000).

76.
Therneau, T.M. coxme: Mixed Effects Cox Models. R package version 2.2-5. https://CRAN.R-project.org/package=coxme (2015). More

For sustainable use of marine-animal resources, preservation of endangered species, and conservation of ecosystems, it is very important to understand the biology of individual marine animal. From the viewpoints of physiology, ethology, and environmentology, marine animals have been studied by bioelectric measurement1,2,3,4, bio-logging5,6,7,8,9, and DNA (genome) analysis10,11,12,13,14,15, respectively. Recent technological innovations helped studies on bio-logging and DNA analysis advance rapidly, but advancement of bioelectric-measurement technology, which has existed for a long time, lags behind those of bio-logging and DNA analysis.
Now, aiming to obtain good harvests, the aquaculture industry requires bioelectric measurements to grasp the health condition of marine animals from pathophysiological viewpoints. Moreover, the electrocardiogram (ECG), which is a kind of bioelectric measurement, carries high expectations because it can evaluate psychological stress of marine animals just as it can evaluate that of humans16,17,18,19. Moreover, ECG can be used in fish ethological- and physiological studies2,4, so innovating techniques and devices for ECG measurement will contribute to developing these studies.
In regards to bioelectric measurement targeting marine animals, to prevent electric short-circuiting between the pair of bioelectrodes via seawater (which is conductive), one or multiple pairs of bioelectrodes are embedded inside the living body by incision surgery20,21, which can impose a heavy workload on inexperienced experimenters. Moreover, the animal can often become agitated without anesthesia and consume much physical energy when the electrodes are implanted into its body. To reduce these burdens, we propose a novel method of measuring bioelectric signals—which utilizes the conductivity of seawater surrounding the animal—by using only one bioelectrode attached at each measurement point (in contrast to the conventional method, which requires a pair of bioelectrodes). To the best of our knowledge, a similar method has not been reported.
In this paper, the proposed method of bioelectric measurement for marine animals under the seawater is first overviewed. Next, the bioelectric measurement system for the chosen experimental subjects, namely, fish, is described, and the availability of the proposed method is verified. Then, the experimental procedures and results of bioelectric measurements are presented. Finally, possible applications of the proposed method are discussed. More

1.
Singh BK, Bardgett RD, Smith P, Reay DS. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat Rev Microbiol. 2010;8:779–90.
CAS  Article  Google Scholar 
2.
Schlesinger WH, Andrews JA. Soil respiration and the global carbon cycle. Biogeochemistry. 2000;48:7–20.
CAS  Article  Google Scholar 

3.
Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:13630.
CAS  Article  Google Scholar 

4.
Six J, Frey SD, Thiet RK, Batten KM. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci Soc Am J. 2006;70:555–69.
CAS  Article  Google Scholar 

5.
Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.
CAS  Article  Google Scholar 

6.
Zhou J, Xue K, Xie J, Deng Y, Wu L, Cheng X, et al. Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Change. 2012;2:106–10.
CAS  Article  Google Scholar 

7.
Aerts R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos. 1997;79:439–49.
Article  Google Scholar 

8.
Bradford MA, Veen GFC, Bonis A, Bradford EM, Classen AT, Cornelissen JHC, et al. A test of the hierarchical model of litter decomposition. Nat Ecol Evol. 2017;1:1836–45.
Article  Google Scholar 

9.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
CAS  Article  Google Scholar 

10.
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;42:293–323.
CAS  Article  Google Scholar 

11.
Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.
Article  Google Scholar 

12.
Rose JM, Vora NM, Countway PD, Gast RJ, Caron DA. Effects of temperature on growth rate and gross growth efficiency of an Antarctic bacterivorous protist. ISME J. 2009;3:252–60.
CAS  Article  Google Scholar 

13.
Schulz-Bohm K, Geisen S, Wubs ERJ, Song C, de Boer W, Garbeva P. The prey’s scent—volatile organic compound mediated interactions between soil bacteria and their protist predators. ISME J. 2017;11:817–20.
CAS  Article  Google Scholar 

14.
Kuikman PJ, Jansen AG, van Veen JA, Zehnder AJB. Protozoan predation and the turnover of soil organic carbon and nitrogen in the presence of plants. Biol Fertil Soils. 1990;10:22–28.
CAS  Article  Google Scholar 

15.
Crowther TW, Boddy L, Hefin Jones T. Functional and ecological consequences of saprotrophic fungus–grazer interactions. ISME J. 2012;6:1992–2001.
CAS  Article  Google Scholar 

16.
Bradford MA, Tordoff GM, Eggers T, Jones TH, Newington JE. Microbiota, fauna, and mesh size interactions in litter decomposition. Oikos. 2002;99:317–23.
Article  Google Scholar 

17.
Jousset A, Rochat L, Pechy-Tarr M, Keel C, Scheu S, Bonkowski M. Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters. ISME J. 2009;3:666–74.
CAS  Article  Google Scholar 

18.
Crowther TW, Thomas SM, Maynard DS, Baldrian P, Covey K, Frey SD, et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc Natl Acad Sci. 2015;112:7033.
CAS  Article  Google Scholar 

19.
Serna-Chavez HM, Fierer N, van Bodegom PM. Global drivers and patterns of microbial abundance in soil. Glob Ecol Biogeogr. 2013;22:1162–72.
Article  Google Scholar 

20.
Scharlemann JPW, Tanner EVJ, Hiederer R, Kapos V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 2014;5:81–91.
CAS  Article  Google Scholar  More

1.
Nowell RW, Green S, Laue BE, Sharp PM. The extent of genome flux and its role in the differentiation of bacterial lineages. Genome Biol Evol. 2014;6:1514–29.
PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Ochman H, Lawrence JG, Groisman EA. Lateral gene transfer and the nature of bacterial innovation. Nature. 2000;405:299–304.
CAS  PubMed  Article  Google Scholar 

3.
Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Albalat R, Cañestro C. Evolution by gene loss. Nat Rev Genet. 2016;17:379–91.
CAS  PubMed  Article  Google Scholar 

5.
Jacq C, Miller JR, Brownlee GG. A pseudogene structure in 5S DNA of Xenopus laevis. Cell. 1977;12:109–20.
CAS  PubMed  Article  Google Scholar 

6.
Li W-H, Gojobori T, Nei M. Pseudogenes as a paradigm of neutral evolution. Nature. 1981;292:237–9.
CAS  PubMed  Article  Google Scholar 

7.
Ohta T. The nearly neutral theory of molecular evolution. Annu Rev Ecol Syst. 1992;23:263–86.
Article  Google Scholar 

8.
Bolotin E, Hershberg R. Gene loss dominates as a source of genetic variation within clonal pathogenic bacterial species. Genome Biol Evol. 2015;7:2173–87.
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Hottes AK, Freddolino PL, Khare A, Donnell ZN, Liu JC, Tavazoie S. Bacterial adaptation through loss of function. PLoS Genet. 2013;9:e1003617.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Sharma V, Hecker N, Roscito JG, Foerster L, Langer BE, Hiller M. A genomics approach reveals insights into the importance of gene losses for mammalian adaptations. Nat Commun. 2018;9:1–9.
Article  CAS  Google Scholar 

11.
Sokurenko EV, Hasty DL, Dykhuizen DE. Pathoadaptive mutations: gene loss and variation in bacterial pathogens. Trends Microbiol. 1999;7:191–5.
CAS  PubMed  Article  Google Scholar 

12.
Ortega AP, Villagra NA, Urrutia IM, Valenzuela LM, Talamilla-Espinoza A, Hidalgo AA, et al. Lose to win: marT pseudogenization in Salmonella enterica serovar Typhi contributed to the surV-dependent survival to H2O2, and inside human macrophage-like cells. Infect Genet Evol. 2016;45:111–21.
CAS  PubMed  Article  Google Scholar 

13.
Goodhead I, Darby AC. Taking the pseudo out of pseudogenes. Curr Opin Microbiol. 2015;23:102–9.
CAS  PubMed  Article  Google Scholar 

14.
Johnson LJ. Pseudogene rescue: an adaptive mechanism of codon reassignment. J Evol Biol. 2010;23:1623–30.
CAS  PubMed  Article  Google Scholar 

15.
Librado P, Vieira FG, Rozas J. BadiRate: estimating family turnover rates by likelihood-based methods. Bioinformatics. 2012;28:279–81.
CAS  PubMed  Article  Google Scholar 

16.
David LA, Alm EJ. Rapid evolutionary innovation during an Archaean genetic expansion. Nature. 2011;469:93–96.
CAS  PubMed  Article  Google Scholar 

17.
Avni E, Montoya D, Lopez D, Modlin R, Pellegrini M, Snir S. A phylogenomic study quantifies competing mechanisms for pseudogenization in prokaryotes—the Mycobacterium leprae case. PLoS One. 2017;13:e0204322.
Article  CAS  Google Scholar 

18.
Ochman H. The nature and dynamics of bacterial genomes. Science. 2006;311:1730–3.
CAS  PubMed  Article  Google Scholar 

19.
Grote J, Thrash JC, Huggett MJ, Landry ZC, Carini P, Giovannoni SJ, et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio. 2012;3:e00252–12.
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.
PubMed  PubMed Central  Article  Google Scholar 

21.
Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Luo H, Moran MA. How do divergent ecological strategies emerge among marine bacterioplankton lineages? Trends Microbiol. 2015;23:577–84.
CAS  PubMed  Article  Google Scholar 

23.
Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.
PubMed  PubMed Central  Article  Google Scholar 

24.
Tujula NA, Crocetti GR, Burke C, Thomas T, Holmström C, Kjelleberg S. Variability and abundance of the epiphytic bacterial community associated with a green marine Ulvacean alga. ISME J. 2010;4:301–11.
PubMed  Article  Google Scholar 

25.
Littman RA, Willis BL, Pfeffer C, Bourne DG. Diversities of coral-associated bacteria differ with location, but not species, for three acroporid corals on the Great Barrier Reef. FEMS Microbiol Ecol. 2009;68:152–63.
CAS  PubMed  Article  Google Scholar 

26.
Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5:355–62.
CAS  PubMed  Article  Google Scholar 

27.
Sweet MJ, Croquer A, Bythell JC. Bacterial assemblages differ between compartments within the coral holobiont. Coral Reefs. 2011;30:39–52.
Article  Google Scholar 

28.
Crossland CJ, Barnes DJ, Borowitzka MA. Diurnal lipid and mucus production in the staghorn coral Acropora acuminata. Mar Biol. 1980;60:81–90.
CAS  Article  Google Scholar 

29.
Shashar N, Stambler N. Endolithic algae within corals—life in an extreme environment. J Exp Mar Biol Ecol. 1992;163:277–86.
CAS  Article  Google Scholar 

30.
Highsmith RC. Lime-boring algae in hermatypic coral skeletons. J Exp Mar Biol Ecol. 1981;55:267–81.
Article  Google Scholar 

31.
Kühl M, Holst G, Larkum AWD, Ralph PJ. Imaging of oxygen dynamics within the endolithic algal community of the massive coral Porites lobata. J Phycol. 2008;44:541–50.
PubMed  Article  CAS  Google Scholar 

32.
Kalhoefer D, Thole S, Voget S, Lehmann R, Liesegang H, Wollher A, et al. Comparative genome analysis and genome-guided physiological analysis of Roseobacter litoralis. BMC Genomics. 2011;12:324.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Lachnit T, Fischer M, Künzel S, Baines JF, Harder T. Compounds associated with algal surfaces mediate epiphytic colonization of the marine macroalga Fucus vesiculosus. FEMS Microbiol Ecol. 2013;84:411–20.
CAS  PubMed  Article  Google Scholar 

34.
Singh RP, Reddy CRK. Seaweed–microbial interactions: key functions of seaweed-associated bacteria. FEMS Microbiol Ecol. 2014;88:213–30.
CAS  PubMed  Article  Google Scholar 

35.
Khailov KM, Burlakova ZP. Release of dissolved organic matter by marine seaweeds and distribution of their total organic production to inshore communities. Limnol Oceanogr. 1969;14:521–7.
Article  Google Scholar 

36.
Wai TC, Ng JSS, Leung KMY, Dudgeon D, Williams GA. The source and fate of organic matter and the significance of detrital pathways in a tropical coastal ecosystem. Limnol Oceanogr. 2008;53:1479–92.
CAS  Article  Google Scholar 

37.
Braeckman U, Pasotti F, Vázquez S, Zacher K, Hoffmann R, Elvert M, et al. Degradation of macroalgal detritus in shallow coastal Antarctic sediments. Limnol Oceanogr. 2019;64:1423–41.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Moran MA, Belas R, Schell MA, Gonzalez JM, Sun F, Sun S, et al. Ecological genomics of marine roseobacters. Appl Environ Microbiol. 2007;73:4559–69.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Sonnenschein EC, Nielsen KF, D’Alvise P, Porsby CH, Melchiorsen J, Heilmann J, et al. Global occurrence and heterogeneity of the Roseobacter clade species Ruegeria mobilis. ISME J. 2017;11:569–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Slightom RN, Buchan A. Surface colonization by marine roseobacters: integrating genotype and phenotype. Appl Environ Microbiol. 2009;75:6027–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Thole S, Kalhoefer D, Voget S, Berger M, Engelhardt T, Liesegang H, et al. Phaeobacter gallaeciensis genomes from globally opposite locations reveal high similarity of adaptation to surface life. ISME J. 2012;6:2229–44.
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.
CAS  PubMed  Article  Google Scholar 

43.
Brinkhoff T, Giebel H-A, Simon M. Diversity, ecology, and genomics of the Roseobacter clade: a short overview. Arch Microbiol. 2008;189:531–9.
CAS  PubMed  Article  Google Scholar 

44.
Luo H, Löytynoja A, Moran MA. Genome content of uncultivated marine Roseobacters in the surface ocean. Environ Microbiol. 2012;14:41–51.
CAS  PubMed  Article  Google Scholar 

45.
Lerat E, Ochman H. Ψ-Φ: Exploring the outer limits of bacterial pseudogenes. Genome Res. 2004;14:2273–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Lerat E, Ochman H. Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res. 2005;33:3125–32.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Kuo C-H, Ochman H. The extinction dynamics of bacterial pseudogenes. PLoS Genet. 2010;6:e1001050.
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Umezaki I. Ecological studies of Sargassum hemiphyllum C. AGARDH in Obama Bay, Japan Sea. Nippon Suisan Gakkaishi. 1984;50:1677–83.
Article  Google Scholar 

49.
Tam TW, Ang PO. Repeated physical disturbances and the stability of sub-tropical coral communities in Hong Kong. China Aquat Conserv Mar Freshw Ecosyst. 2008;18:1005–24.
Article  Google Scholar 

50.
Cheang CC, Chu KH, Ang PO. Phylogeography of the marine macroalga Sargassum hemiphyllum (Phaeophyceae, Heterokontophyta) in northwestern Pacific. Mol Ecol. 2010;19:2933–48.
CAS  PubMed  Article  Google Scholar 

51.
Raghunathan C, Venkataraman K. Diversity and distribution of corals and their associated fauna of Rani Jhansi Marine National Park, Andaman and Nicobar Islands. In: Venkataraman K, Raghunathan C, Sivaperuman C, (eds). Ecology of Faunal Communities on the Andaman and Nicobar Islands. Berlin, Heidelberg: Springer; 2012. p. 177–208.
Google Scholar 

52.
Ang PO. Phenology of Sargassum spp. in Tung Ping Chau Marine Park, Hong Kong SAR, China. J Appl Phycol. 2006;18:629–36.
Article  Google Scholar 

53.
Huggett MJ, Apprill A. Coral microbiome database: Integration of sequences reveals high diversity and relatedness of coral-associated microbes. Environ Microbiol Rep. 2019;11:372–85.
PubMed  Article  Google Scholar 

54.
Passel MWJ, van, Marri PR, Ochman H. The emergence and fate of horizontally acquired genes in Escherichia coli. PLoS Comput Biol. 2008;4:e1000059.
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Ochman H. Distinguishing the ORFs from the ELFs: short bacterial genes and the annotation of genomes. Trends Genet. 2002;18:335–7.
CAS  PubMed  Article  Google Scholar 

56.
Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.
PubMed  PubMed Central  Article  Google Scholar 

57.
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
CAS  Article  Google Scholar 

58.
Schliep KP. Phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Liu Y, Harrison PM, Kunin V, Gerstein M. Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome Biol. 2004;5:R64.
PubMed  PubMed Central  Article  Google Scholar 

60.
Halldal P. Photosynthetic capacities and photosynthetic action spectra of endozoic algae of the massive coral Favia. Biol Bull. 1968;134:411–24.
CAS  Article  Google Scholar 

61.
Shibata K, Haxo FT. Light transmission and spectral distribution through epi- and endozoic algal layers in the brain coral, Favia. Biol Bull. 1969;136:461–8.
CAS  Article  Google Scholar 

62.
Park JT, Uehara T. How bacteria consume their own exoskeletons (turnover and recycling of cell wall peptidoglycan). Microbiol Mol Biol Rev. 2008;72:211–27.
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Mauck J, Chan L, Glaser L. Turnover of the cell wall of gram-positive bacteria. J Biol Chem. 1971;246:1820–7.
CAS  PubMed  Google Scholar 

64.
Goodell E. Recycling of murein by Escherichia coli. J Bacteriol. 1985;163:305–10.
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Uehara T, Suefuji K, Jaeger T, Mayer C, Park JT. MurQ etherase is required by Escherichia coli in order to metabolize Anhydro-N-Acetylmuramic acid obtained either from the environment or from its own cell wall. J Bacteriol. 2006;188:1660–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Dik DA, Marous DR, Fisher JF, Mobashery S. Lytic transglycosylases: concinnity in concision of the bacterial cell wall. Crit Rev Biochem Mol Biol. 2017;52:503–42.
PubMed  PubMed Central  Article  Google Scholar 

67.
Jiang H, Kong R, Xu X. The N-acetylmuramic acid 6-phosphate etherase gene promotes growth and cell differentiation of cyanobacteria under light-limiting conditions. J Bacteriol. 2010;192:2239–45.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Ferrer LM, Szmant AM. Nutrient regeneration by the endolithic community in coral skeletons. In: Proceedings of the 6th International Coral Reef Symposium 1988. pp 1–4.

69.
Risk MJ, Muller HR. Porewater in coral heads: evidence for nutrient regeneration. Limnol Oceanogr. 1983;28:1004–8.
Article  Google Scholar 

70.
Yu LJ, Wu JR, Zheng ZZ, Lin CC, Zhan XB. Changes in gene transcription and protein expression involved in the response of Agrobacterium sp. ATCC 31749 to nitrogen availability during curdlan production. Appl Biochem Microbiol. 2011;47:487–93.
CAS  Article  Google Scholar 

71.
Wada S, Aoki M, Mikami A, Komatsu T, Tsuchiya Y, Sato T, et al. Bioavailability of macroalgal dissolved organic matter in seawater. Mar Ecol Prog Ser. 2008;370:33–44.
CAS  Article  Google Scholar 

72.
Essenberg MK, Cooper RA. Two ribose-5-phosphate isomerases from Escherichia coli K12: partial characterisation of the enzymes and consideration of their possible physiological roles. Eur J Biochem. 1975;55:323–32.
CAS  PubMed  Article  Google Scholar 

73.
Nelson CE, Goldberg SJ, Wegley Kelly L, Haas AF, Smith JE, Rohwer F, et al. Coral and macroalgal exudates vary in neutral sugar composition and differentially enrich reef bacterioplankton lineages. ISME J. 2013;7:962–79.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Mulligan C, Fischer M, Thomas GH. Tripartite ATP-independent periplasmic (TRAP) transporters in bacteria and archaea. FEMS Microbiol Rev. 2011;35:68–86.
CAS  PubMed  Article  Google Scholar 

75.
Beyenbach KW, Wieczorek H. The V-type H+ ATPase: molecular structure and function, physiological roles and regulation. J Exp Biol. 2006;209:577–89.
CAS  PubMed  Article  Google Scholar 

76.
Guadayol Ò, Silbiger NJ, Donahue MJ, Thomas FIM. Patterns in temporal variability of temperature, oxygen and pH along an environmental gradient in a coral reef. PLoS One. 2014;9:e85213.
PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Bodenmiller DM, Spiro S. The yjeB(nsrR) gene of Escherichia coli encodes a nitric oxide-sensitive transcriptional regulator. J Bacteriol. 2006;188:874–81.
CAS  PubMed  PubMed Central  Article  Google Scholar 

78.
Gilberthorpe NJ, Lee ME, Stevanin TM, Read RC, Poole RK. NsrR: a key regulator circumventing Salmonella enterica serovar Typhimurium oxidative and nitrosative stress in vitro and in IFN-γ-stimulated J774.2 macrophages. Microbiology. 2007;153:1756–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
da Fonseca RR, Johnson WE, O’Brien SJ, Vasconcelos V, Antunes A. Molecular evolution and the role of oxidative stress in the expansion and functional diversification of cytosolic glutathione transferases. BMC Evol Biol. 2010;10:281.
PubMed  PubMed Central  Article  CAS  Google Scholar 

80.
Green ER, Mecsas J. Bacterial secretion systems—an overview. Microbiol Spectr. 2016;4:1–32.
CAS  Article  Google Scholar 

81.
Ansari MI, Schiwon K, Malik A, Grohmann E. Biofilm formation by environmental bacteria. In: Malik A, Grohmann E (eds). Environmental protection strategies for sustainable development. 2012. Springer Netherlands, Dordrecht, pp 341–77.

82.
Meron D, Efrony R, Johnson WR, Schaefer AL, Morris PJ, Rosenberg E, et al. Role of flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2009;75:5704–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Attmannspacher U, Scharf BE, Harshey RM. FliL is essential for swarming: motor rotation in absence of FliL fractures the flagellar rod in swarmer cells of Salmonella enterica. Mol Microbiol. 2008;68:328–41.
CAS  PubMed  Article  Google Scholar 

84.
Fernando SC, Wang J, Sparling K, Garcia GD, Francini-Filho RB, de Moura RL, et al. Microbiota of the major south atlantic reef building coral Mussismilia. Micro Ecol. 2015;69:267–80.
Article  Google Scholar 

85.
Pollock FJ, McMinds R, Smith S, Bourne DG, Willis BL, Medina M, et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat Commun. 2018;9:4921.
PubMed  PubMed Central  Article  CAS  Google Scholar 

86.
Marcelino VR, van Oppen MJ, Verbruggen H. Highly structured prokaryote communities exist within the skeleton of coral colonies. ISME J. 2018;12:300–3.
PubMed  Article  Google Scholar 

87.
Hill C. Virulence or niche factors: what’s in a name? J Bacteriol. 2012;194:5725–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Egan S, Harder T, Burke C, Steinberg P, Kjelleberg S, Thomas T. The seaweed holobiont: understanding seaweed–bacteria interactions. FEMS Microbiol Rev. 2013;37:462–76.
CAS  PubMed  Article  Google Scholar 

89.
Levy A, Salas Gonzalez I, Mittelviefhaus M, Clingenpeel S, Herrera Paredes S, Miao J, et al. Genomic features of bacterial adaptation to plants. Nat Genet. 2018;50:138–50.
CAS  Article  Google Scholar 

90.
Koren O, Rosenberg E. Bacteria associated with mucus and tissues of the coral Oculina patagonica in summer and winter. Appl Environ Microbiol. 2006;72:5254–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

91.
Wang X, Grus WE, Zhang J. Gene losses during human origins. PLoS Biol. 2006;4:e52.
PubMed  PubMed Central  Article  CAS  Google Scholar  More

Inhibition of cell signaling by commensal streptococci
To study how S. mutans ComRS signaling could be impacted by the presence of a competing species, we empirically optimized a dual-species model system (Fig. 1a) in which a strain of S. mutans carrying the promoter regions of comS or comX (PcomS, PcomX) fused to a codon-optimized green fluorescence protein (gfp) reporter gene could be cocultured with wild-type strains of Streptococcus gordonii DL1, Streptococcus sanguinis SK150, or S. sp. A12. All experiments were performed in chemically defined medium (CDM) [38, 39] because activation of the ComRS circuit occurs spontaneously in CDM as cell density increases, with no need for addition of synthetic XIP or overexpression of the gene for the XIP precursor (comS) (Supplementary Fig. 1). CDM is also heavily buffered with phosphate, which is advantageous because ComRS signaling is optimal at neutral pH values [40, 41]. The buffer also prevents the generation of strongly acidic conditions by S. mutans, which is detrimental to the comparatively acid-sensitive commensal Streptococcus spp.
Fig. 1: Loss of S. mutans peptide signaling in presence of competitor.

a An oral Streptococcus spp. competitor strain (blue) was cocultured in chemically defined medium (CDM) with an S. mutans PcomX::gfp reporter strain (green). As cell density of the reporter strain increases during growth, the XIP peptide that originates from the comS gene will be produced and accumulates extracellularly. XIP is then reimported into the cell through the Opp oligopeptide permease, binds to ComR and activates the comX promoter. Additionally, intracellular signaling occurs with ComS binding directly to ComR. The reporter strain harbors a plasmid, pDL278, carrying a copy of gfp that is driven by the comX promoter (PcomX) to monitor ComRS signaling activation. b Cocultures of the S. mutans PcomX::gfp reporter strain grown with either S. mutans UA159 (control, green circles), S. gordonii DL1 (blue squares), S. sanguinis SK150 (orange triangles), or S. sp. A12 (red diamonds). Colored, non-connected symbols represent relative fluorescent units (RFUs) plotted on the left y-axis, while black, connected lines with symbols represent growth of the cocultures over the course of the experiment measured by optical density at 600 nm plotted on the right y-axis. Data are averages from three biological replicates of the experiment. c Percentage of each species remaining within the coculture after 18 h of monitoring, determined by colony forming unit (CFU) plating. The PcomX::gfp reporter strain is represented in the orange bars, while the competitor, listed on the left y-axis, is represented in blue. Average of collected CFUs is shown to the right. Data represent averages from three biological replicates of the experiment that was conducted in panel (b). d Cocultures of the S. mutans PcomX::gfp reporter strain in which 5 µM sXIP was added prior to the start of the experiment. e Cocultures of the S. mutans PcomX::gfp reporter strain that contains a plasmid that overexpresses the XIP peptide precursor, ComS. Control represents the PcomX::gfp reporter that contained an empty vector only.

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When the PcomX::gfp reporter strain was cocultured with wild-type S. mutans UA159 (control), robust ComRS signaling was observed as cell density increased (Fig. 1b). However, when cocultured with a competitor Streptococcus spp., no signal from the S. mutans reporter could be detected above background levels; i.e., the nonspecific fluorescence generated by an S. mutans strain that did not contain a copy of the gfp gene. The lack of fluorescence in the cocultures with commensals was not due to growth inhibition of S. mutans as the reporter strain constituted 10 ± 3%, 37 ± 5%, or 54 ± 3% of the total colony forming units (CFUs) recovered after 18 h of coculturing with S. gordonii DL1, S. sanguinis SK150, or S. sp. A12, respectively (Fig. 1c). The quantity of S. mutans cells in the commensal cocultures compared favorably with the recovery of the reporter strain (54 ± 5%) in coculture with wild-type S. mutans UA159. Of note, the fact that equal proportions of reporter and wild-type S. mutans were recovered from cocultures demonstrated that the presence of the GFP gene fusion did not compromise the fitness of the reporter strain, further verified by growth rate comparisons between wild-type and reporter strains (Supplementary Fig. 1).
Two strategies were implemented to try to recover active ComRS signaling by the reporter strain during cocultivation with commensal streptococci. First, synthetic XIP was added to the cocultures to a final concentration of 5 µM just prior to the beginning of the fluorescence monitoring phase of the experiments, and cocultures were observed as above. No detectable fluorescence signal was recorded above background in the cocultures, with or without exogenously added XIP (Fig. 1d). Second, a plasmid encoding a copy of the XIP precursor comS under the control of a highly expressed constitutive promoter (P23) [42] was introduced into the S. mutans reporter strain; we previously reported that overexpression of comS could strongly activate PcomX [28]. However, no increase in GFP expression was observed in cocultures of the comS overexpressing strain with the commensals, whereas signaling was greatly enhanced when cocultured with strain UA159 as a control (Fig. 1e).
To ensure these observations were not limited to only planktonic growth conditions, we examined S. mutans ComRS signaling in cocultured biofilm populations. While almost all cells harboring the PcomX::gfp reporter were GFP-positive in the control biofilms (coculture of the reporter with wild-type S. mutans), confocal imaging of biofilms containing competitor streptococci uniformly showed that almost no S. mutans cells were expressing detectable GFP (Fig. 2a). However, in some frames (0.22 × 0.22 mm frames, ~30,000 S. mutans cells per frame), a small number of cells (1–3 cells per frame) were GFP-positive. When 3D renderings of these areas within the biofilm were constructed, GFP-positive cells were found close to the substratum (Fig. 2b and Movie S1, same area of biofilm as top panel of Fig. 2b). Also, PcomX-active cells were not necessarily confined to distinct S. mutans microcolonies, and in some cases could be seen adjacent to the competitor streptococci, which carried a constitutively expressed red fluorescent protein (DsRed2) for their identification. To quantify the different types of cells in the biofilm populations, we physically dispersed the biofilms by sonication and analyzed the populations by flow cytometry (Supplementary Fig. 2). About 1 in 10,000 S. mutans cells counted displayed activation of PcomX within the biofilms, which was similar to the proportions of GFP-expressing cells in planktonic growth conditions (Supplemental Table 1).
Fig. 2: S. mutans peptide signaling in coculture biofilms.

a 3D volume projections of imaged biofilms in the XY-orientation (from the top looking down). Each biofilm contains either S. mutans UA159 with a constitutive gfp reporter plasmid (top row), or the PcomX::gfp reporter plasmid (bottom row) that was cocultured with either S. mutans (control; left), S. gordonii DL1 (middle), or S. sp. A12 (right) who all constitutively produce DsRed2. To the right of each expanded color image is the black and white image capture of each individual channel: blue (top), green (middle), and red (bottom). b Zoomed image frames of PcomX-active cells within cocultured biofilms with S. gordonii DL1. The images captured are a single z plane near or at the biofilm substratum. Two different areas of the biofilm (top and bottom rows) were imaged. Each panel represents one color channel of blue (SYTO 42 stained; total cells), green (PcomX::gfp positive cells), or red (S. gordonii P23::DsRed2) followed by the merged image on the far right. The top panel of (b) is the same area of biofilm shown in Movie S1.

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Commensal signaling inhibition is dependent on cell contact
Changes in phenotypes that are observed when two different species of bacteria are cocultured can usually be induced by secreted molecules from one of the bacterial strains [1]. We suspected that molecule(s) secreted by the competitor strains are required for shutting down cell–cell signaling in S. mutans. To explore this hypothesis, we cultured the competitors individually overnight and collected the supernatant fluids after centrifugation. The supernates were then filter sterilized, pH adjusted from ~6.3 to 7.0 with NaOH, and carbohydrate was added back to achieve a final concentration of added glucose to 20 mM. We then inoculated our reporter strain into the commensal supernates and monitored fluorescence activity (Fig. 3a). Surprisingly, ComRS signaling was readily observed in all supernates. In fact, reporter activity tended to be higher in the supernates of competitors compared to controls.
Fig. 3: Cell contact dependence in signaling inhibition.

a Growth and fluorescence of S. mutans PcomX::gfp reporter strain in spent supernatant fluids of either S. mutans UA159 (control, green circles), S. gordonii DL1 (blue squares), S. sanguinis SK150 (orange triangles), or S. sp. A12 (red diamonds). Depiction on top shows methods used to treat supernatant fluids following harvesting and prior to reporter strain inoculation. Overnight cultures of selected strains where centrifuged, spent supernates removed, filter sterilized, the pH was adjusted to 7.0 and 20 mM additional glucose was added. The PcomX::gfp reporter strain was then inoculated and monitored for 18 h in a Synergy 2 multimode plate reader. b Growth of cocultures in a transwell apparatus. The PcomX::gfp reporter strain was first inoculated in 0.1 mL of CDM medium in a 96-well microtiter plate. The transwell plate was then overlaid on top of the 96-well plate, and 0.1 mL of CDM inoculated with either S. mutans UA159 (control, green circles), S. gordonii DL1 (blue squares), S. sanguinis SK150 (orange triangles), or S. sp. A12 (red diamonds) was added to the top chamber, as shown. Cultures of the reporter strain and competitor were separated by a 0.4 µM pore size polycarbonate filter membrane. Fluorescence (RFUs) of the reporter strain was monitored for 18 h.

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In another experiment to confirm these results, we grew competitor and our reporter strains together in a transwell apparatus, so that both bacterial strains shared the same growth medium, but were physically separated by 0.4 µm pore size polycarbonate membrane that would allow passage of small molecule(s) between the chambers (Fig. 3b). Even in the transwell system, cell signaling was robust in cocultures containing competitor species. This result is consistent with data showing that the proximity of live commensal cells with S. mutans prior to signal activation is required for the signaling inhibition.
Impairment of S. mutans cell signaling by oral commensals is conserved across species
We next screened a collection of low-passage oral streptococci that had been previously genome sequenced [43] to determine whether the ability to inhibit S. mutans ComRS signaling was conserved across commensal species and to assess whether the presence or absence of certain genes might contribute to inhibition of peptide signaling. Ten different low-passage clinical isolates of S. gordonii, ten isolates of S. sanguinis, and five isolates of S. sp. A12-related organisms [19] were cocultured with our S. mutans ComRS signaling reporter. The S. sp. A12-related organisms included strains classified as A12-like (A13 and BCC21), as Streptococcus australis (G1 and G2), or as Streptococcus parasanguinis (A1). Interestingly, significant production of GFP by S. mutans was evident when cultured with one isolate of S. sanguinis (BCC64) and with three isolates that were classified as A12-related (BCC21, G1 and G2) (Fig. 4a). However, these results were most likely due to the inability of these isolates to grow well within the CDM medium during the course of the experiment (Supplementary Fig. 3). In fact, after 18 h of monitoring, these isolates comprised  0.1 after 12 h as monitored using a Bioscreen system, see Supplementary Fig. 3) inhibited PcomX activation. Thus, if a commensal strain could grow in CDM, even somewhat poorly, it could completely inhibit ComRS signaling.
Fig. 4: Conservation of ComRS signaling antagonism across oral isolates.

a Relative fluorescent units (RFUs) of the S. mutans PcomX::gfp reporter strain cocultured with clinical oral isolates of either S. gordonii, S. sanguinis or S. sp. A12-like strains. Relative fluorescent units were recorded after coculture inoculation at 1:1 ratio and 12 h of incubation at 37 °C. Results from four biological replicates of the experiment are shown. b RFUs after 12 h of incubation of the PcomX::gfp reporter harbored in various S. mutans clinical isolates. The PcomX::gfp reporter strain was cocultured with either S. mutans UA159 (control; black dots and bars) or an oral competitor streptococci (S. sp. A12, red dots and bars).

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We also tested several genomically and phenotypically diverse isolates of S. mutans [44, 45], both in coculture with our PcomX::gfp reporter in the UA159 background (Supplementary Fig. 4) and against competitor Streptococcus spp., after transformation of the S. mutans strains with the PcomX reporter plasmid (Fig. 4b). Various levels of spontaneous activation of the PcomX::gfp reporter were observed among the different S. mutans strains in monocultures in CDM, consistent with recent reports showing strain-dependent differences in S. mutans peptide signaling [46]. One isolate, Smu107 (R221), had undetectable levels of GFP in monoculture in CDM alone. All others showed activity above baseline. However, when cocultured with S. sp. A12, ComRS signaling was inhibited to an extent similar to that observed with strain wild-type UA159. Therefore, the ability to obstruct ComRS signaling is conserved among isolates of S. gordonii, S. sanguinis, and A12-related streptococci, and inhibition by commensals is similarly conserved in genomically diverse isolates of S. mutans.
Relatively small proportions of live commensal streptococci can inhibit signaling
To verify that the ability of the competitor species to grow (viability) was required for inhibition of peptide signaling, we used two different treatments of the competitor species S. sp. A12 after it was grown to mid-exponential phase: 80 °C for 0.5 h in a heating block (Fig. 5a) or treatment with 4% paraformaldehyde for 1 h at ambient temperature (Fig. 5b). After treatment, the inactivated commensal cells were washed and resuspended in fresh CDM and then mixed with the S. mutans reporter strain to begin the experiment. With heat-treated cells, some ComRS signal activity was evident, but not near the levels seen with S. mutans-only controls. However, when the paraformaldehyde-treated cells were used, the competitor did not inhibit signaling and fluorescence, with levels being similar to the S. mutans-only control. Importantly, we determined that there was a greater number of live cells, by plating and counting CFUs, for the competitor after heat treatment, compared to paraformaldehyde fixing (Supplementary Fig. 5), which likely explains the difference in effects on PcomX activation. These results support that metabolically active and growing competitors are required for S. mutans ComRS signaling obstruction.
Fig. 5: Importance of oral competitor cell density in signaling inhibition.

Cocultures of the S. mutans PcomX::gfp reporter strain with untreated or treated cells by either a 0.5 h heat inactivation at 80 °C or b 1 h suspension in 4% paraformaldehyde. Data represent averages from three biological replicates. c Dilution of an oral competitor streptococci (S. sp. A12) in coculture with the S. mutans PcomX::gfp reporter strain. Legend (top left) refers to the amount of S. sp. A12 within the coculture at the time of initial inoculation. Bottom: addition of either control (UA159; blue squares) or an oral competitor streptococci (S. gordonii DL1; orange triangles) at 4.5 h to a growing culture of the S. mutans PcomX::gfp strain when competence activation was d fully detected, e beginning to be detected, or f not yet detected. See Supplementary Fig. 7 for comparisons at 4.5 and 12 h, specifically.

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Based on the intermediate inhibitory effects seen with reduced proportions of a live competitor on our reporter strain, i.e. with heat-treated cells, we tested whether some minimal proportion of live competitor was required to exert effects on ComRS signaling. We utilized S. sp. A12 and varied the percentage of S. mutans and S. sp. A12 in the cocultures, after determining that the proportions of cells recovered after 18 h were similar to the proportions in the initial inocula (Supplementary Fig. 6). Complete inhibition of S. mutans ComRS signaling occurred when S. sp. A12 constituted ≥6.3% of the initial inoculum (Fig. 5c). At 3.1 or 1.6% of S. sp. A12, reporter activity was detectable, but at lower levels than when no S. sp. A12 was present. No difference in S. mutans reporter activity was observed when  More

Hundreds of people in the nineteenth-century United States reported seeing the Gloucester Sea Serpent (above), which was probably a marine creature bedecked with fishing debris. Credit: Museum of Fine Arts, Boston

Fisheries
30 September 2020

Centuries-old ‘unidentified marine objects’ hint that sea creatures have been getting entangled in fishing lines since before the invention of plastic.

‘Sea serpents’ spotted around Great Britain and Ireland in the nineteenth century were probably whales and other marine animals ensnared in fishing gear — long before the advent of the plastic equipment usually blamed for such entanglements.
The snaring of sea creatures in fishing equipment is often considered a modern phenomenon, because the hemp and cotton ropes used in the past degraded more quickly than their plastic counterparts. But Robert France at Dalhousie University in Truro, Canada, identified 51 probable entanglements near Great Britain and Ireland dating as far back as 1809.
France analysed 214 accounts of ‘unidentified marine objects’ from the early nineteenth century to 2000, looking for observations of a monster that had impressive length, a series of humps protruding above the sea surface and a fast, undulating movement through the water. France says that such accounts describe not sea serpents but whales, basking sharks (Cetorhinus maximus) or other marine animals trailing fishing gear such as buoys or other floats.
Such first-hand accounts could help researchers to construct a better picture of historical populations of marine species and the pressures they faced, France says. More