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Microbial Ecology

, Volume 77, Issue 4, pp 1048–1066 | Cite as

Population and Culture Age Influence the Microbiome Profiles of House Dust Mites

  • Jan HubertEmail author
  • Marta Nesvorna
  • Jan Kopecky
  • Tomas Erban
  • Pavel Klimov
Invertebrate Microbiology

Abstract

Interactions with microorganisms might enable house dust mites (HDMs) to derive nutrients from difficult-to-digest structural proteins and to flourish in human houses. We tested this hypothesis by investigating the effects of changes in the mite culture growth and population of two HDM species on HDM microbiome composition and fitness. Growing cultures of laboratory and industrial allergen-producing populations of Dermatophagoides farinae (DFL and DFT, respectively) and Dermatophagoides pteronyssinus (DPL and DPT, respectively) were sampled at four time points. The symbiotic microorganisms of the mites were characterized by DNA barcode sequencing and quantified by qPCR using universal/specific primers. The population growth of mites and nutrient contents of mite bodies were measured and correlated with the changes in bacteria in the HDM microbiome. The results showed that both the population and culture age significantly influenced the microbiome profiles. Cardinium formed 93% and 32% of the total sequences of the DFL and DFT bacterial microbiomes, respectively, but this bacterial species was less abundant in the DPL and DPT microbiomes. Staphylococcus abundance was positively correlated with increased glycogen contents in the bodies of mites, and increased abundances of Aspergillus, Candida, and Kocuria were correlated with increased lipid contents in the bodies of mites. The xerophilic fungus Wallemia accounted for 39% of the fungal sequences in the DPL microbiome, but its abundance was low in the DPT, DFL, and DFT microbiomes. With respect to the mite culture age, we made three important observations: the mite population growth from young cultures was 5–8-fold higher than that from old cultures; specimens from old cultures had greater abundances of fungi and bacteria in their bodies; and yeasts predominated in the gut contents of specimens from young cultures, whereas filamentous mycelium prevailed in specimens from old cultures. Our results are consistent with the hypothesis that mites derive nutrients through associations with microorganisms.

Keywords

Nutrition Bacteria Fungi Yeasts Gut Symbiosis Diet Dermatophagoides pteronyssinus Dermatophagoides farinae 

Notes

Acknowledgements

The authors are grateful to Barry OConnor and the anonymous reviewers for providing useful comments on earlier drafts of this manuscript. We thank Marie Bostlova, Martin Markovic, Bc. Vit Molva, and Jan Hubert Jr. for their technical assistance; Prof. Krzysztof Solarz for the laboratory mite culture; and RNDr. Alexandr Zgarbovsky for the industrial allergen-producing mite cultures.

Funding Information

JH, TE, and MN were supported by the Czech Science Foundation (GACR) as part of Project No. 17-12068S. PBK was supported by the Russian Science Foundation (Project No. 16-14-10109), the Ministry of Education and Science of the Russian Federation (No. 6.1933.2014/K Project Code 1933), and the Russian Foundation for Basic Research (No. 15-04-0s5185-a).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Supplementary material

248_2018_1294_MOESM1_ESM.docx (766 kb)
ESM 1 (DOCX 766 kb)

References

  1. 1.
    Colloff MJ (2009) Dust mites. CSIRO Publishing, Collingwood.  https://doi.org/10.1007/978-90-481-2224-0 Google Scholar
  2. 2.
    Jacquet A (2011) The role of innate immunity activation in house dust mite allergy. Trends Mol. Med. 17:604–611.  https://doi.org/10.1016/j.molmed.2011.05.014 Google Scholar
  3. 3.
    Arlian LG (1991) House-dust-mite allergens: a review. Exp Appl Acarol 10:167–186.  https://doi.org/10.1007/BF01198649 Google Scholar
  4. 4.
    Calderon MA, Linneberg A, Kleine-Tebbe J, De Blay F, Hernandez Fernandez de Rojas D, Virchow JC, Demoly P (2015) Respiratory allergy caused by house dust mites: what do we truly know? J. Allergy Clin. Immunol. 136:38–48.  https://doi.org/10.1016/j.jaci.2014.10.012 Google Scholar
  5. 5.
    Vidal-Quist JC, Ortego F, Rombauts S, Castanera P, Hernandez-Crespo P (2017) Dietary shifts have consequences for the repertoire of allergens produced by the European house dust mite. Med. Vet. Entomol. 31:272–280.  https://doi.org/10.1111/mve.12234 Google Scholar
  6. 6.
    Colloff MJ (2009) Development, life histories and population dynamics. In: Colloff MJ (ed) Dust mites. CSIRO Publishing, Collingwood, pp 215–254.  https://doi.org/10.1007/978-90-481-2224-0_5 Google Scholar
  7. 7.
    Arlian LG, Dippold JS (1996) Development and fecundity of Dermatophagoides farinae (Acari: Pyroglyphidae). J. Med. Entomol. 33:257–260.  https://doi.org/10.1093/jmedent/33.2.257 Google Scholar
  8. 8.
    Arlian LG, Rapp CM, Ahmed SG (1990) Development of Dermatophagoides pteronyssinus (Acari: Pyroglyphidae). J. Med. Entomol. 27:1035–1040.  https://doi.org/10.1093/jmedent/27.6.1035 Google Scholar
  9. 9.
    Eraso E, Guisantes JA, Martinez J, Saenz-de-Santamaria M, Martinez A, Palacios R, Cisterna R (1997) Kinetics of allergen expression in cultures of house dust mites, Dermatophagoides pteronyssinus and D. farinae (Acari: Pyroglyphidae). J. Med. Entomol. 34:684–689.  https://doi.org/10.1093/jmedent/34.6.684 Google Scholar
  10. 10.
    Eraso E, Martinez J, Martinez A, Palacios R, Guisantes JA (1997) Quality parameters for the production of mite extracts. Allergol. Immunopathol. 25:113–117Google Scholar
  11. 11.
    Andersen A (1991) Nutritional value of yeast for Dermatophagoides pteronyssinus (Acari: Epidermoptidae) and the antigenic and allergenic composition of extracts during extended culturing. J. Med. Entomol. 28:487–491.  https://doi.org/10.1093/jmedent/28.4.487 Google Scholar
  12. 12.
    Eraso E, Martinez J, Garcia-Ortega P, Martinez A, Palacios R, Cisterna R, Guisantes JA (1998) Influence of mite growth culture phases on the biological standardization of allergenic extracts. J Investig Allergol Clin Immunol 8:201–206Google Scholar
  13. 13.
    Klimov PB, OConnor B (2013) Is permanent parasitism reversible?—critical evidence from early evolution of house dust mites. Syst. Biol. 62:411–423.  https://doi.org/10.1093/sysbio/syt008 Google Scholar
  14. 14.
    OConnor BM (1979) Evolutionary origins of astigmatid mites inhabiting stored products. In: Rodriguez GJ (ed) Recent advances in acarology, vol. 1. Academic Press, New York, pp 273–278.  https://doi.org/10.1016/b978-0-12-592201-2.50038-5 Google Scholar
  15. 15.
    Sugiura S, Ikeda H (2014) Keratin decomposition by trogid beetles: evidence from a feeding experiment and stable isotope analysis. Naturwissenschaften 101:187–196.  https://doi.org/10.1007/s00114-013-1137-z Google Scholar
  16. 16.
    Brandwein M, Steinberg D, Meshner S (2016) Microbial biofilms and the human skin microbiome. NPJ Biofilms Microbiomes 2(3):3.  https://doi.org/10.1038/s41522-016-0004-z Google Scholar
  17. 17.
    Horak B (1987) Preliminary study on the concentration and species composition of bacteria, fungi and mites in samples of house dust from Silesia (Poland). Allergol. Immunopathol. 15:161–166Google Scholar
  18. 18.
    de Saint Georges-Gridelet D (1987) Vitamin requirements of the European house dust mite, Dermatophagoides pteronyssinus (Acari: Pyroglyphidae), in relation to its fungal association. J. Med. Entomol. 24:408–411.  https://doi.org/10.1093/jmedent/24.4.408 Google Scholar
  19. 19.
    Van Asselt L (1999) Interactions between domestic mites and fungi. Indoor Built Environ 8:216–220.  https://doi.org/10.1159/000024644 Google Scholar
  20. 20.
    Naegele A, Reboux G, Scherer E, Roussel S, Millon L (2013) Fungal food choices of Dermatophagoides farinae affect indoor fungi selection and dispersal. Int. J. Environ. Health Res. 23:91–95.  https://doi.org/10.1080/09603123.2012.699029 Google Scholar
  21. 21.
    Luxton M (1972) Studies on the oribatid mites of a Danish beech wood soil. I. Nutritional biology. Pedobiologia 12:434–463Google Scholar
  22. 22.
    Erban T, Hubert J (2008) Digestive function of lysozyme in synanthropic acaridid mites enables utilization of bacteria as a food source. Exp Appl Acarol 44:199–212.  https://doi.org/10.1007/s10493-008-9138-x Google Scholar
  23. 23.
    Sabree ZL, Moran NA (2014) Host-specific assemblages typify gut microbial communities of related insect species. SpringerPlus 3:138.  https://doi.org/10.1186/2193-1801-3-138 Google Scholar
  24. 24.
    Douglas AE (2015) Multiorganismal insects: diversity and function of resident microorganisms. Annual Rev Entomol 60:17–34.  https://doi.org/10.1146/annurev-ento-010814-020822 Google Scholar
  25. 25.
    Erban T, Klimov PB, Smrz J, Phillips TW, Nesvorna M, Kopecky J, Hubert J (2016) Populations of stored product mite Tyrophagus putrescentiae differ in their bacterial communities. Front. Microbiol. 7:1046.  https://doi.org/10.3389/fmicb.2016.01046 Google Scholar
  26. 26.
    Hubert J, Kopecky J, Nesvorna M, Perotti MA, Erban T (2016) Detection and localization of Solitalea-like and Cardinium bacteria in three Acarus siro populations (Astigmata: Acaridae). Exp Appl Acarol 70:309–327.  https://doi.org/10.1007/s10493-016-0080-z Google Scholar
  27. 27.
    Chan T-F, Ji K-M, Yim AK-Y, Liu X-Y, Zhou J-W, Li R-Q, Yang KY, Li J, Li M, Law PT-W, Wu Y-L, Cai Z-L, Qin H, Bao Y, Leung RK-K, Ng PK-S, Zou J, Zhong X-J, Ran P-X, Zhong N-S, Liu Z-G, Tsui SK-W (2015) The draft genome, transcriptome, and microbiome of Dermatophagoides farinae reveal a broad spectrum of dust mite allergens. J. Allergy Clin. Immunol. 135:539–548.  https://doi.org/10.1016/j.jaci.2014.09.031 Google Scholar
  28. 28.
    Waldron R, McGowan J, Gordon N, McCarthy C, Mitchell EB, Doyle S, Fitzpatrick DA (2017) Draft genome sequence of Dermatophagoides pteronyssinus, the European house dust mite. Genome Announc 5:e00789–e00717.  https://doi.org/10.1128/genomeA.00789-17 Google Scholar
  29. 29.
    Erban T, Harant K, Hubert J (2017) Detailed two-dimensional gel proteomic mapping of the feces of the house dust mite Dermatophagoides pteronyssinus and comparison with D. farinae: reduced trypsin protease content in D. pteronyssinus and different isoforms. J. Proteome 162:11–19.  https://doi.org/10.1016/j.jprot.2017.04.021 Google Scholar
  30. 30.
    Kim JY, Yi M, Hwang Y, Lee JY, Lee I-Y, Yong D, Yong T-S (2018) 16S rRNA profiling of the Dermatophagoides farinae core microbiome: Enterococcus and Bartonella. Clin. Exp. Allergy 48:607–610.  https://doi.org/10.1111/cea.13104 Google Scholar
  31. 31.
    Petrova-Nikitina AD, Antropova AB, Bilanenko EN, Mokeeva VL, Chekunova LN, Bulgakova TA, Zheltikova TM (2011) Population dynamics of mites of the family Pyroglyphidae and micromycetes in laboratory cultures. Entomol Rev 91:377–387.  https://doi.org/10.1134/S0013873811030134 Google Scholar
  32. 32.
    Hubert J, Kopecky J, Perotti MA, Nesvorna M, Braig HR, Sagova-Mareckova M, Macovei L, Zurek L (2012) Detection and identification of species-specific bacteria associated with synanthropic mites. Microb. Ecol. 63:919–928.  https://doi.org/10.1007/s00248-011-9969-6 Google Scholar
  33. 33.
    Hubert J, Erban T, Kamler M, Kopecky J, Nesvorna M, Hejdankova S, Titera D, Tyl J, Zurek L (2015) Bacteria detected in the honeybee parasitic mite Varroa destructor collected from beehive winter debris. J. Appl. Microbiol. 119:640–654.  https://doi.org/10.1111/jam.12899 Google Scholar
  34. 34.
    Earley ZM, Akhtar S, Green SJ, Naqib A, Khan O, Cannon AR, Hammer AM, Morris NL, Li X, Eberhardt JM, Gamelli RL, Kennedy RH, Choudhry MA (2015) Burn injury alters the intestinal microbiome and increases gut permeability and bacterial translocation. PLoS One 10:e0129996.  https://doi.org/10.1371/journal.pone.0129996 Google Scholar
  35. 35.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:7537–7541.  https://doi.org/10.1128/AEM.01541-09 Google Scholar
  36. 36.
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD (2013) Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79:5112–5120.  https://doi.org/10.1128/AEM.01043-13 Google Scholar
  37. 37.
    Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10:996–998.  https://doi.org/10.1038/nmeth.2604 Google Scholar
  38. 38.
    Edgar RC (2016) UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv.  https://doi.org/10.1101/081257 https://www.biorxiv.org/content/early/2016/10/15/081257. Accessed 6 August 2017
  39. 39.
    Sarikhani E, Sagova-Mareckova M, Omelka M, Kopecky J (2017) The effect of peat and iron supplements on the severity of potato common scab and bacterial community in tuberosphere soil. FEMS Microbiol. Ecol. 93:fiw206.  https://doi.org/10.1093/femsec/fiw206 Google Scholar
  40. 40.
    Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM (2014) Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42:D633–D642.  https://doi.org/10.1093/nar/gkt1244 Google Scholar
  41. 41.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590–D596.  https://doi.org/10.1093/nar/gks1219 Google Scholar
  42. 42.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J. Mol. Biol. 215:403–410.  https://doi.org/10.1016/S0022-2836(05)80360-2 Google Scholar
  43. 43.
    Hammer O, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4:4 http://palaeo-electronica.org/2001_1/past/issue1_01.htm. Accessed 6 August 2017Google Scholar
  44. 44.
    Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2016) vegan: community ecology package. CRAN - The Comprehensive R Archive Network. R Foundation for Statistical Computing, Vienna, Austria. http://CRAN.R-project.org/package=vegan. Accessed 6 August 2017
  45. 45.
    R Development Core Team (2016) R: a language and environment for statistical computing, reference index version 3.3.1. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. Accessed 6 August 2017
  46. 46.
    Ondov BD, Bergman NH, Phillippy AM (2011) Interactive metagenomic visualization in a web browser. BMC Bioinformatics 12:385.  https://doi.org/10.1186/1471-2105-12-385 Google Scholar
  47. 47.
    Anderson MJ (2001) A new method for nonparametric multivariate analysis of variance. Austral Ecol 26:32–46.  https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x Google Scholar
  48. 48.
    Anderson MJ, Ellingsen KE, McArdle BH (2006) Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9:683–693.  https://doi.org/10.1111/j.1461-0248.2006.00926.x Google Scholar
  49. 49.
    Blanchet FG, Legendre P, Borcard D (2008) Forward selection of explanatory variables. Ecology 89:2623–2632.  https://doi.org/10.1890/07-0986.1 Google Scholar
  50. 50.
    White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5:e1000352.  https://doi.org/10.1371/journal.pcbi.1000352 Google Scholar
  51. 51.
    Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73:5261–5267.  https://doi.org/10.1128/AEM.00062-07 Google Scholar
  52. 52.
    Pruesse E, Peplies J, Glockner FO (2012) SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823–1829.  https://doi.org/10.1093/bioinformatics/bts252 Google Scholar
  53. 53.
    Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696–704.  https://doi.org/10.1080/10635150390235520 Google Scholar
  54. 54.
    Darriba D, Taboada GL, Doallo R, Posada D (2012) jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9:772–772.  https://doi.org/10.1038/nmeth.2109 Google Scholar
  55. 55.
    Lartillot N, Lepage T, Blanquart S (2009) PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics 25:2286–2288.  https://doi.org/10.1093/bioinformatics/btp368 Google Scholar
  56. 56.
    Rodrigue N, Lartillot N (2014) Site-heterogeneous mutation-selection models within the PhyloBayes-MPI package. Bioinformatics 30:1020–1021.  https://doi.org/10.1093/bioinformatics/btt729 Google Scholar
  57. 57.
    Jow H, Hudelot C, Rattray M, Higgs PG (2002) Bayesian phylogenetics using an RNA substitution model applied to early mammalian evolution. Mol. Biol. Evol. 19:1591–1601.  https://doi.org/10.1093/oxfordjournals.molbev.a004221 Google Scholar
  58. 58.
    Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Hohna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61:539–542.  https://doi.org/10.1093/sysbio/sys029 Google Scholar
  59. 59.
    Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59:307–321.  https://doi.org/10.1093/sysbio/syq010 Google Scholar
  60. 60.
    Rambaut A (2007) FigTree, a graphical viewer of phylogenetic trees. Molecular evolution, phylogenetics and epidemiology: research, software and publications of Andrew Rambaut and members of his research group. http://tree.bio.ed.ac.uk/software/figtree/. Accessed 6 August 2016
  61. 61.
    Liang Q, Chen L, Fulco AJ (1995) An efficient and optimized PCR method with high fidelity for site-directed mutagenesis. PCR Methods Appl 4:269–274.  https://doi.org/10.1101/gr.4.5.269 Google Scholar
  62. 62.
    Kaufmann C, Brown MR (2008) Regulation of carbohydrate metabolism and flight performance by a hypertrehalosaemic hormone in the mosquito Anopheles gambiae. J. Insect Physiol. 54:367–377.  https://doi.org/10.1016/j.jinsphys.2007.10.007 Google Scholar
  63. 63.
    Van Handel E (1985) Rapid determination of glycogen and sugars in mosquitoes. J. Am. Mosq. Control Assoc. 1:299–301Google Scholar
  64. 64.
    Kaufmann C (2014) Determination of lipid, glycogen and sugars in mosquitoes. In: Benedict M (ed) MR4 methods in Anopheles research, 4th edn. BEI Resources, Manassas, VA, USA. https://www.beiresources.org/Publications/MethodsinAnophelesResearch.aspx. Accessed 6 August 2016
  65. 65.
    Erban T, Ledvinka O, Nesvorna M, Hubert J (2017) Experimental manipulation shows a greater influence of population than dietary perturbation on the microbiome of Tyrophagus putrescentiae. Appl. Environ. Microbiol. 83:e00128–e00117.  https://doi.org/10.1128/AEM.00128-17 Google Scholar
  66. 66.
    Evans GO, Sheals JG, MacFarlane D (1961) The terrestrial Acari of the British Isles: an introduction to their morphology, biology and classification, vol. I. Introduction and biology. British Museum, LondonGoogle Scholar
  67. 67.
    Smrz J (1989) Internal anatomy of Hypochthonius rufulus (Acari: Oribatida). J. Morphol. 200:215–230.  https://doi.org/10.1002/jmor.1052000210 Google Scholar
  68. 68.
    Exbrayat J-M (2013) Histochemical and cytochemical methods of visualization1st edn. CRC Press, Boca Raton.  https://doi.org/10.1201/b14967 Google Scholar
  69. 69.
    Bochkov AV, Klimov PB, Hestvik G, Saveljev AP (2014) Integrated Bayesian species delimitation and morphological diagnostics of chorioptic mange mites (Acariformes: Psoroptidae: Chorioptes). Parasitol. Res. 113:2603–2627.  https://doi.org/10.1007/s00436-014-3914-9 Google Scholar
  70. 70.
    Klimov PB, Bochkov AV, OConnor BM (2016) Phylogenetic position of the house dust mite subfamily Guatemalichinae (Acariformes: Pyroglyphidae) based on integrated molecular and morphological analyses and different measures of support. Cladistics 32:261–275.  https://doi.org/10.1111/cla.12126 Google Scholar
  71. 71.
    Klimov PB, Oconnor BM (2009) Improved tRNA prediction in the American house dust mite reveals widespread occurrence of extremely short minimal tRNAs in acariform mites. BMC Genomics 10:598.  https://doi.org/10.1186/1471-2164-10-598 Google Scholar
  72. 72.
    Webster LMI, Thomas RH, McCormack GP (2004) Molecular systematics of Acarus siro s. lat., a complex of stored food pests. Mol. Phylogenet. Evol. 32:817–822.  https://doi.org/10.1016/j.ympev.2004.04.005 Google Scholar
  73. 73.
    Dermauw W, Van Leeuwen T, Vanholme B, Tirry L (2009) The complete mitochondrial genome of the house dust mite Dermatophagoides pteronyssinus (Trouessart): a novel gene arrangement among arthropods. BMC Genomics 10:107.  https://doi.org/10.1186/1471-2164-10-107 Google Scholar
  74. 74.
    Beroiz B, Couso-Ferrer F, Ortego F, Chamorro MJ, Arteaga C, Lombardero M, Castanera P, Hernandez-Crespo P (2014) Mite species identification in the production of allergenic extracts for clinical use and in environmental samples by ribosomal DNA amplification. Med. Vet. Entomol. 28:287–296.  https://doi.org/10.1111/mve.12052 Google Scholar
  75. 75.
    Noge K, Mori N, Tanaka C, Nishida R, Tsuda M, Kuwahara Y (2005) Identification of astigmatid mites using the second internal transcribed spacer (ITS2) region and its application for phylogenetic study. Exp Appl Acarol 35:29–46.  https://doi.org/10.1007/s10493-004-1953-0 Google Scholar
  76. 76.
    Yang B, Cai J, Cheng X (2011) Identification of astigmatid mites using ITS2 and COI regions. Parasitol. Res. 108:497–503.  https://doi.org/10.1007/s00436-010-2153-y Google Scholar
  77. 77.
    Griffiths DA, Cunnington AM (1971) Dermatophagoides microceras sp. n: a description and comparison with its sibling species, D. farinae Hughes, 1961. J. Stored Prod. Res. 7:1–14.  https://doi.org/10.1016/0022-474X(71)90032-4 Google Scholar
  78. 78.
    Nakamura Y, Kawai S, Yukuhiro F, Ito S, Gotoh T, Kisimoto R, Yanase T, Matsumoto Y, Kageyama D, Noda H (2009) Prevalence of Cardinium bacteria in planthoppers and spider mites and taxonomic revision of “Candidatus Cardinium hertigii” based on detection of a new Cardinium group from biting midges. Appl. Environ. Microbiol. 75:6757–6763.  https://doi.org/10.1128/AEM.01583-09 Google Scholar
  79. 79.
    Brody AR, McGrath JC, Wharton GW (1972) Dermatophagoides farinae: the digestive system. J N Y Entomol Soc 80:152–177Google Scholar
  80. 80.
    Wharton GW, Brody AR (1972) The peritrophic membrane of the mite, Dermatophagoides farinae: Acariformes. J. Parasitol. 58:801–804.  https://doi.org/10.2307/3278321 Google Scholar
  81. 81.
    Douglas AE, Hart BJ (1989) The significance of the fungus Aspergillus penicillioides to the house dust mite Dermatophagoides pteronyssinus. Symbiosis 7:105–116Google Scholar
  82. 82.
    Erban T, Hubert J (2010) Comparative analyses of proteolytic activities in seven species of synanthropic acaridid mites. Arch. Insect Biochem. Physiol. 75:187–206.  https://doi.org/10.1002/arch.20388 Google Scholar
  83. 83.
    Erban T, Hubert J (2010) Determination of pH in regions of the midguts of acaridid mites. J. Insect Sci. 10(42):1–12.  https://doi.org/10.1673/031.010.4201 Google Scholar
  84. 84.
    Childs M, Bowman CE (1981) Lysozyme activity in six species of economically important astigmatid mites. Comp Biochem Physiol B 70:615–617.  https://doi.org/10.1016/0305-0491(81)90305-9 Google Scholar
  85. 85.
    Kopecky J, Perotti MA, Nesvorna M, Erban T, Hubert J (2013) Cardinium endosymbionts are widespread in synanthropic mite species (Acari: Astigmata). J. Invertebr. Pathol. 112:20–23.  https://doi.org/10.1016/j.jip.2012.11.001 Google Scholar
  86. 86.
    Zchori-Fein E, Perlman SJ (2004) Distribution of the bacterial symbiont Cardinium in arthropods. Mol. Ecol. 13:2009–2016.  https://doi.org/10.1111/j.1365-294X.2004.02203.x Google Scholar
  87. 87.
    Hubert J, Stejskal V, Nesvorna M, Aulicky R, Kopecky J, Erban T (2016) Differences in the bacterial community of laboratory and wild populations of the predatory mite Cheyletus eruditus (Acarina: Cheyletidae) and bacteria transmission from its prey Acarus siro (Acari: Acaridae). J. Econ. Entomol. 109:1450–1457.  https://doi.org/10.1093/jee/tow032 Google Scholar
  88. 88.
    Santos-Garcia D, Rollat-Farnier P-A, Beitia F, Zchori-Fein E, Vavre F, Mouton L, Moya A, Latorre A, Silva FJ (2014) The genome of Cardinium cBtQ1 provides insights into genome reduction, symbiont motility, and its settlement in Bemisia tabaci. Genome Biol Evol 6:1013–1030.  https://doi.org/10.1093/gbe/evu077 Google Scholar
  89. 89.
    Penz T, Schmitz-Esser S, Kelly SE, Cass BN, Muller A, Woyke T, Malfatti SA, Hunter MS, Horn M (2012) Comparative genomics suggests an independent origin of cytoplasmic incompatibility in Cardinium hertigii. PLoS Genet. 8:e1003012.  https://doi.org/10.1371/journal.pgen.1003012 Google Scholar
  90. 90.
    Zhang Y-K, Chen Y-T, Yang K, Hong X-Y (2016) A review of prevalence and phylogeny of the bacterial symbiont Cardinium in mites (subclass: Acari). Syst Appl Acarol 21:978–990.  https://doi.org/10.11158/saa.21.7.11 Google Scholar
  91. 91.
    Gotoh T, Noda H, Ito S (2007) Cardinium symbionts cause cytoplasmic incompatibility in spider mites. Heredity 98:13–20.  https://doi.org/10.1038/sj.hdy.6800881 Google Scholar
  92. 92.
    Hodgson RK (1976) Sex ratio and sex determination in the American house dust mite, Dermataphagoides farinae. Ann Entomol Soc Am 69:1085–1086.  https://doi.org/10.1093/aesa/69.6.1085 Google Scholar
  93. 93.
    Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, Bohannan BJM (2016) Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J 10:655–664.  https://doi.org/10.1038/ismej.2015.142 Google Scholar
  94. 94.
    Kopecky J, Nesvorna M, Mareckova-Sagova M, Hubert J (2014) The effect of antibiotics on associated bacterial community of stored product mites. PLoS One 9:e112919.  https://doi.org/10.1371/journal.pone.0112919 Google Scholar
  95. 95.
    Chigira A, Miura K (2005) Detection of ‘Candidatus Cardinium’ bacteria from the haploid host Brevipalpus californicus (Acari: Tenuipalpidae) and effect on the host. Exp Appl Acarol 37:107–116.  https://doi.org/10.1007/s10493-005-0592-4 Google Scholar
  96. 96.
    Valerio CR, Murray P, Arlian LG, Slater JE (2005) Bacterial 16S ribosomal DNA in house dust mite cultures. J. Allergy Clin. Immunol. 116:1296–1300.  https://doi.org/10.1016/j.jaci.2005.09.046 Google Scholar
  97. 97.
    Hubert J, Erban T, Kopecky J, Sopko B, Nesvorna M, Lichovnikova M, Schicht S, Strube C, Sparagano O (2017) Comparison of microbiomes between red poultry mite populations (Dermanyssus gallinae): predominance of Bartonella-like bacteria. Microb. Ecol. 74:947–960.  https://doi.org/10.1007/s00248-017-0993-z Google Scholar
  98. 98.
    Segers FHID, Kesnerova L, Kosoy M, Engel P (2017) Genomic changes associated with the evolutionary transition of an insect gut symbiont into a blood-borne pathogen. ISME J 11:1232–1244.  https://doi.org/10.1038/ismej.2016.201 Google Scholar
  99. 99.
    Kesnerova L, Moritz R, Engel P (2016) Bartonella apis sp. nov., a honey bee gut symbiont of the class Alphaproteobacteria. Int. J. Syst. Evol. Microbiol. 66:414–421.  https://doi.org/10.1099/ijsem.0.000736 Google Scholar
  100. 100.
    Kong HH, Oh J, Deming C, Conlan S, Grice EA, Beatson MA, Nomicos E, Polley EC, Komarow HD, NISC Comparative Sequence Program, Murray PR, Turner ML, Segre JA (2012) Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 22:850–859.  https://doi.org/10.1101/gr.131029.111 Google Scholar
  101. 101.
    Cho I, Blaser MJ (2012) The human microbiome: at the interface of health and disease. Nat Rev Genet 13:260–270.  https://doi.org/10.1038/nrg3182 Google Scholar
  102. 102.
    Tang VH, Chang BJ, Srinivasan A, Mathaba LT, Harnett GB, Stewart GA (2013) Skin-associated Bacillus, staphylococcal and micrococcal species from the house dust mite, Dermatophagoides pteronyssinus and bacteriolytic enzymes. Exp Appl Acarol 61:431–447.  https://doi.org/10.1007/s10493-013-9712-8 Google Scholar
  103. 103.
    Merk K, Borelli C, Korting HC (2005) Lactobacilli—bacteria–host interactions with special regard to the urogenital tract. Int J Med Microbiol 295:9–18.  https://doi.org/10.1016/j.ijmm.2004.11.006 Google Scholar
  104. 104.
    Inada N, Shoji J, Yamagami S (2017) Atopic keratoconjunctivitis complicated by Kocuria koreensis keratitis: the first case. Allergy Asthma Clin Immunol 13(6):6.  https://doi.org/10.1186/s13223-017-0178-9 Google Scholar
  105. 105.
    Hay DB, Hart BJ, Pearce RB, Kozakiewicz Z, Douglas AE (1992) How relevant are house dust mite–fungal interactions in laboratory culture to the natural dust system? Exp Appl Acarol 16:37–47.  https://doi.org/10.1007/BF01201491 Google Scholar
  106. 106.
    Lustgraaf B (1978) Ecological relationships between xerophilic fungi and house-dust mites (Acarida: Pyroglyphidae). Oecologia 33:351–359.  https://doi.org/10.1007/BF00348118 Google Scholar
  107. 107.
    Simon D, Straumann A, Dahinden C, Simon H-U (2013) Frequent sensitization to Candida albicans and profilins in adult eosinophilic esophagitis. Allergy 68:945–948.  https://doi.org/10.1111/all.12157 Google Scholar
  108. 108.
    Avula-Poola S, Morgan MS, Arlian LG (2012) Diet influences growth rates and allergen and endotoxin contents of cultured Dermatophagoides farinae and Dermatophagoides pteronyssinus house dust mites. Int. Arch. Allergy Immunol. 159:226–234.  https://doi.org/10.1159/000336026 Google Scholar
  109. 109.
    Vidal-Quist JC, Ortego F, Lombardero M, Castanera P, Hernandez-Crespo P (2015) Allergen expression in the European house dust mite Dermatophagoides pteronyssinus throughout development and response to environmental conditions. Med. Vet. Entomol. 29:137–146.  https://doi.org/10.1111/mve.12102 Google Scholar
  110. 110.
    Colloff MJ (1987) Effects of temperature and relative humidity on development times and mortality of eggs from laboratory and wild populations of the European house-dust mite Dermatophagoides pteronyssinus (Acari: Pyroglyphidae). Exp Appl Acarol 3:279–289.  https://doi.org/10.1007/BF01193165 Google Scholar
  111. 111.
    Hart BJ, Crowther D, Wilkinson T, Biddulph P, Ucci M, Pretlove S, Ridley I, Oreszczyn T (2007) Reproduction and development of laboratory and wild house dust mites (Acari: Pyroglyphidae) and their relationship to the natural dust ecosystem. J. Med. Entomol. 44:568–574.  https://doi.org/10.1093/jmedent/44.4.568 Google Scholar
  112. 112.
    Matsumoto K (1965) Studies on environmental factors for breeding of grain mites VII. Relationship between reproduction of mites and kind of carbohydrates in the diet. Med Entomol Zool 16:118–122.  https://doi.org/10.7601/mez.16.118 Google Scholar
  113. 113.
    Smrz J, Trelova M (1995) The association of bacteria and some soil mites (Acari: Oribatida and Acaridida). Acta Zool Fennica 196:120–123Google Scholar
  114. 114.
    Smrz J (2002) Microanatomical and microbiological characteristics of the quiescent state of Scutovertex minutus (Acari: Oribatida). Exp Appl Acarol 27:103–112.  https://doi.org/10.1023/A:1021527904766 Google Scholar
  115. 115.
    Smrz J (2003) Microanatomical and biological aspects of bacterial associations in Tyrophagus putrescentiae (Acari: Acaridida). Exp Appl Acarol 31:105–113.  https://doi.org/10.1023/B:APPA.0000005111.05959.d6 Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Crop Research InstitutePrague 6-RuzyneCzechia
  2. 2.Department of Ecology and Evolutionary BiologyUniversity of MichiganAnn ArborUSA
  3. 3.Institute of BiologyUniversity of TyumenTyumenRussia

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