Within species expressed genetic variability and gene expression response to different temperatures in the rotifer Brachionus calyciflorus sensu stricto

English version

Autoři: Sofia Paraskevopoulou ^aff001; Alice B. Dennis ^aff001; Guntram Weithoff ^aff002; Stefanie Hartmann ^aff004; Ralph Tiedemann ^aff001
Působiště autorů: Unit of Evolutionary Biology/Systematic Zoology, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany ^aff001; Unit of Ecology and Ecosystem Modelling, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany ^aff002; Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany ^aff003; Unit of Evolutionary Adaptive Genomics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany ^aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223134

Souhrn

Genetic divergence is impacted by many factors, including phylogenetic history, gene flow, genetic drift, and divergent selection. Rotifers are an important component of aquatic ecosystems, and genetic variation is essential to their ongoing adaptive diversification and local adaptation. In addition to coding sequence divergence, variation in gene expression may relate to variable heat tolerance, and can impose ecological barriers within species. Temperature plays a significant role in aquatic ecosystems by affecting species abundance, spatio-temporal distribution, and habitat colonization. Recently described (formerly cryptic) species of the Brachionus calyciflorus complex exhibit different temperature tolerance both in natural and in laboratory studies, and show that B. calyciflorus sensu stricto (s.s.) is a thermotolerant species. Even within B. calyciflorus s.s., there is a tendency for further temperature specializations. Comparison of expressed genes allows us to assess the impact of stressors on both expression and sequence divergence among disparate populations within a single species. Here, we have used RNA-seq to explore expressed genetic diversity in B. calyciflorus s.s. in two mitochondrial DNA lineages with different phylogenetic histories and differences in thermotolerance. We identify a suite of candidate genes that may underlie local adaptation, with a particular focus on the response to sustained high or low temperatures. We do not find adaptive divergence in established candidate genes for thermal adaptation. Rather, we detect divergent selection among our two lineages in genes related to metabolism (lipid metabolism, metabolism of xenobiotics).

Klíčová slova:

Gene expression – Transcriptome analysis – Genetic polymorphism – Thermal stresses – Lipid metabolism – Rotifers – Xenobiotic metabolism

Zdroje

1. Nosil P, Funk DJ, Ortiz‐Barrientos D. Divergent selection and heterogeneous genomic divergence. Mol Ecol. 2009; 18: 375–402. doi: 10.1111/j.1365-294X.2008.03946.x 19143936

2. Li YF, Costello JC, Holloway AK, Hahn MW, Rausher M. “Reverse ecology” and the power of population genomics. Evolution. 2008; 62: 2984–2994. doi: 10.1111/j.1558-5646.2008.00486.x 18752601

3. Wallace RL, Snell TW, Smith HA. Rotifer: ecology and general biology. In: Thorp J, & Covich A, editors, Freshwater Invertebrates. London, Elsevier; 2015.

4. Wallace RL, Smith HA, Rotifera. In: Likens GE, editor, Encyclopedia of Inland Waters. Oxford, Elsevier; 2009.

5. Fontaneto D. Molecular phylogenies as a tool to understand diversity in rotifers. Int Rev Hydrobiol. 2014; 99: 178–187. doi: 10.1002/iroh.201301719

6. Franch-Gras L, Hahn C, García-Roger EM, Carmona MJ, Serra M, Gómez A. Genomic signatures of local adaptation to the degree of environmental predictability in rotifers. Sci Rep. 2018; 8(1): Article number: 16051. doi: 10.1038/s41598-018-34188-y 30375419

7. Orsini L, Gilbert D, Podicheti R, Jansen M, Brown JB. Data Descriptor : Daphnia magna transcriptome by RNA-Seq across 12 environmental stressors. Sci Data. 2016; 3: 160030.

8. Gabaldón C, Fontaneto D, Carmona MJ, Montero-Pau J, Serra M. Ecological differentiation in cryptic rotifer species: what we can learn from the Brachionus plicatilis complex. Hydrobiologia. 2017; 796: 7–18. doi: 10.1007/s10750-016-2723-9

9. Jenkins DG, Buikema AL Jr (1998) Do similar communities develop in similar sites? A test with zooplankton structure and function. Ecol Monogr 68:421–443

10. Gómez A, Montero-Pau J, Lunt DH, Serra M. Persistent genetic signatures of colonization in Brachionus manjavacas rotifers in the Iberian Peninsula. Mol Ecol. 2007; 16: 3228–3240. doi: 10.1111/j.1365-294X.2007.03372.x 17651199

11. Papakostas S, Michaloudi E, Proios K, Brehm M, Verhage L, Rota J, et al. Integrative taxonomy recognizes evolutionary units despite widespread mitonuclear discordance: Evidence from a rotifer cryptic species complex. Syst Biol. 2016; 65(3): 508–524. doi: 10.1093/sysbio/syw016 26880148

12. Xl Xiang, Yl Xi, Xl Wen, Zhang JY, Ma Q. Spatial patterns of genetic differentiation in Brachionus calyciflorus species complex collected from East China in summer. Hydrobiologia. 2010; 638: 67–83. doi: 10.1007/s10750-009-0010-8

13. Xl Xiang, Yl Xi, Xl Wen, Zhang G, Wang JX, Hu K. Patterns and processes in the genetic differentiation of the Brachionus calyciflorus complex, a passively dispersing freshwater zooplankton. Mol Phylogenet Evol. 2011; 59: 386–98. doi: 10.1016/j.ympev.2011.02.011 21335094

14. De Meester L, Gómez A, Okamura B, Schwenk K. The monopolization hypothesis and the dispersal-gene flow paradox in aquatic organisms. Acta Oecol. 2002; 23: 121–135. doi: 10.1016/S1146-609X(02)01145-1

15. Campillo S, García-Roger EM, Carmona MJ, Gómez A, Serra M. Selection on life-history traits and genetic population divergence in rotifers. J Evol Biol. 2009; 22: 2542–2553. doi: 10.1111/j.1420-9101.2009.01871.x 19878499

16. Campillo S, García-Roger EM, Carmona MJ, Serra M. Local adaptation in rotifer populations. Evol Ecol. 2011; 25: 933–947. doi: 10.1007/s10682-010-9447-5

17. Franch-Gras L, García-Roger EM, Serra M, Carmona MJ. Adaptation in response to environmental unpredictability. Proc R Soc B. 2017; 284:e20170427. doi: 10.1098/rspb.2017.0427 29212717

18. Paaijmans KP, Heinig RL, Seliga RA, Blanford JI, Blanford S, Murdock CC, et al. Temperature variation makes ectotherms more sensitive to climate change. Glob Chang Biol. 2013; 19(8): 2373–2380. doi: 10.1111/gcb.12240 23630036

19. Parmesan C. Ecological and Evolutionary Responses to Recent Climate Change. Annu Rev Ecol Evol Syst. 2006; 37(1): 637–669.

20. Kelly MW, Sanford E, Grosberg RK. Limited potential for adaptation to climate change in a broadly distributed marine crustacean. Proc R Soc B Biol Sci. 2012; 279(1727): 349–356. doi: 10.1098/rspb.2011.0542 21653591

21. Geerts A, De Meester L, Stoks R. Heat tolerance and its evolutionary potential along a latitudinal gradient in Daphnia magna. Evol Ecol Res. 2014; 16: 517–528.

22. Zhang Y, Zhou A, Xi YL, Sun Q, Ning LF, Xie P, Wen XL, Xiang XL. Temporal patterns and processes of genetic differentiation of the Brachionus calyciflorus (Rotifera) complex in a subtropical shallow lake. Hydrobiologia. 2018; 80: 313–331. doi: 10.1007/s10750-017-3407-9

23. Gómez A, Carmona MJ, Serra M. Ecological factors affecting gene flow in the Brachionus plicatilis complex (Rotifera). Oecologia. 1997; 111: 350–356. doi: 10.1007/s004420050245 28308129

24. Paraskevopoulou S, Tiedemann R, Weithoff G. Differential response to heat stress among evolutionary lineages of an aquatic invertebrate species complex. Biol Lett. 2018; 14: 20180498. doi: 10.1098/rsbl.2018.0498 30487258

25. Snell TW. Effect of temperature, salinity and food level on sexual and asexual reproduction in Brachionus plicatilis (Rotifera). Mar Biol. 1986; 92: 157–162.

26. Kauler P, Enesco HE. The effect of temperature on life history parameters and the cost of reproduction in the rotifer Brachionus calyciflorus. J Freshw Ecol. 2011; 26: 399–408. doi: 10.1080/02705060.2011.563998

27. Li L, Niu C, Ma R. Rapid temporal succession identified by COI of the rotifer Brachionus calyciflorus Pallas in Xihai Pond, Beijing, China, in relation to ecological traits. J Plankton Res. 2010; 32: 951–959 doi: 10.1093/plankt/fbq014

28. Khang TF, Lau CY. Getting the most out of RNA-seq data analysis. PeerJ. 2015; 3: e1360 doi: 10.7717/peerj.1360 26539333

29. Smolina I, Kollias S, Møller EF, Lindeque P, Sundaram AYM, Fernandes JMO, et al. Contrasting transcriptome response to thermal stress in two key zooplankton species, Calanus finmarchicus and C. glacialis. Mar Ecol Prog Ser. 2015; 534: 79–93. doi: 10.3354/meps11398

30. Schoville SD, Barreto FS, Moy GW, Wolff A, Burton RS. Investigating the molecular basis of local adaptation to thermal stress: Population differences in gene expression across the transcriptome of the copepod Tigriopus californicus. BMC Evol Biol. 2012; 12(1): 170. doi: 10.1186/1471-2148-12-170 22950661

31. Yampolsky LY, Zeng E, Lopez J, Williams PJ, Dick KB, Colbourne JK, et al. Functional genomics of acclimation and adaptation in response to thermal stress in Daphnia. BMC Genomics. 2014;15(1):1–12. doi: 10.1186/1471-2164-15-859 25282344

32. Hanson SJ, Stelzer CP, Welch DBM, Logsdon JM. Comparative transcriptome analysis of obligately asexual and cyclically sexual rotifers reveals genes with putative functions in sexual reproduction, dormancy, and asexual egg production. BMC Genomics. 2013; 14(1): 412 doi: 10.1186/1471-2164-14-412 23782598

33. Gribble KE, Mark Welch DB. Genome-wide transcriptomics of aging in the rotifer Brachionus manjavacas, an emerging model system. BMC Genomics. 2017;18:217. doi: 10.1186/s12864-017-3540-x 28249563

34. Han J, Kim DH, Kim HS, Kim HJ, Declerck SAJ, Hagiwara A, et al. Genome-wide identification of 31 cytochrome P450 (CYP) genes in the freshwater rotifer Brachionus calyciflorus and analysis of their benzo[α]pyrene-induced expression patterns. Comp Biochem Physiol Part D Genomics Proteomics. 2017; 25: 26–33. doi: 10.1016/j.cbd.2017.10.003 29126086

35. Smith HA, Burns AR, Shearer T, Snell TW. Three heat shock proteins are essential for rotifer thermotolerance. J. Exp. Mar. Biol. Ecol. 2012; 413:1–6. doi: 10.1016/j.jembe.2011.11.027

36. Kaneko G, Kinoshita S, Yoshinaga T, Tsukamoto K, Watabe S. Changes in expression patterns of stress protein genes during population growth of the rotifer Brachionus plicatilis. Fish Sci. 2002; 68(6): 1317–1323.

37. Drummond AJ, Rambaut A. 2007 BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7; 214. doi: 10.1186/1471-2148-7-214 17996036

38. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 2013; 30: 2725–2729. doi: 10.1093/molbev/mst197 24132122

39. Guillard RRL, Lorenzen CJ. Yellow-green algae with Chlorophyllide. J Phycol. 1972; 8: 10–14.

40. Zhang H, Finiguerra M, Dam HG, Huang Y, Xu D, Liu G, et al. An improved method for achieving high-quality RNA for copepod transcriptomic studies. J Exp Mar Bio Ecol. 2013; 446: 57–66. doi: 10.1016/j.jembe.2013.04.021

41. Winnebeck EC., Millar CD, Warmanet GR. "Why does insect RNA look degraded?". J Insect Sci. 2010; 10: 159. doi: 10.1673/031.010.14119 21067419

42. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010; http://www.bioinformatics.babraham.ac.uk/projects/fastqc.

43. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15): 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

44. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011; 29(7): 644–652. doi: 10.1038/nbt.1883 21572440

45. Davidson NM, Hawkins ADK, Oshlack A. SuperTranscripts: A data driven reference for analysis and visualisation of transcriptomes. Genome Biol. 2017; 18(1): 1–10.

46. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: Architecture and applications. BMC Bioinformatics. 2009; 10: 421. doi: 10.1186/1471-2105-10-421 20003500

47. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011; 27(15): 2156–2158. doi: 10.1093/bioinformatics/btr330 21653522

48. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011; 27(21): 2987–2993. doi: 10.1093/bioinformatics/btr509 21903627

49. Altenhoff AM, Dessimoz C. Phylogenetic and functional assessment of orthologs inference projects and methods. PLoS Comput Biol. 2009; 5(1): e1000262. doi: 10.1371/journal.pcbi.1000262 19148271

50. Zhang Z, Li J, Zhao XQ, Wang J, Wong GKS, Yu J. KaKs_Calculator: Calculating Ka and Ks Through Model Selection and Model Averaging. Genomics, Proteomics Bioinformatics 2006; 4(4): 25–263. doi: 10.1016/S1672-0229(07)60007-2 17531802

51. Yang Z.; Bielawski J. P. (2000). "Statistical methods for detecting molecular adaptation". Trends in Ecology & Evolution. 15 (12): 496–503. doi: 10.1016/S0169-5347(00)01994-7

52. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005; 21(18): 3674–3676. doi: 10.1093/bioinformatics/bti610 16081474

53. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007; 35: 182–185. doi: 10.1093/nar/gkm321 17526522

54. R CoreTeam. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2013. (http://www.R-projest.org).

55. Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLOS Computational Biology. 2014; 10: e1003440. doi: 10.1371/journal.pcbi.1003440 24453961

56. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics. 2011; 12: 323. doi: 10.1186/1471-2105-12-323 21816040

57. Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BMG, et al. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013; 29(8): 1035–1043. doi: 10.1093/bioinformatics/btt087 23428641

58. Chen G, Hare MP. Cryptic ecological diversification of a planktonic estuarine copepod, Acartia tonsa. Mol Ecol. 2008; 17(6):1451–68. doi: 10.1111/j.1365-294X.2007.03657.x 18248575

59. Lubzens E, Wax Y, Minkoff G, Adler F. A model evaluating the contribution of environmental factors to the production of resting eggs in the rotifer Brachionus plicatilis. Hydrobiologia. 1993; 255/256: 127–138.

60. Hayes JD, Pulford DJ. The glutathione s-transferase supergene family: Regulation of GST and the contribution of the lsoenzymes to cancer chemoprotection and drug resistance part i. Crit Rev Biochem Mol Biol. 1995; 30(6): 445–520. doi: 10.3109/10409239509083491 8770536

61. Kadlubar FF. Biochemical individuality and its implications for drug and carcinogen metabolism: Recent insights from acetyltransferase and cytochrome p4501a2 phenotyping and genotyping in humans. Drug Metab Rev. 1994; 26(1–2): 37–46. doi: 10.3109/03602539409029783 8082575

62. Mou Z, He Y, Dai Y, Liu X, Li J. Deficiency in Fatty Acid Synthase Leads to Premature Cell Death and Dramatic Alterations in Plant Morphology. The Plant Cell. 2000; 12: 405–417. doi: 10.1105/tpc.12.3.405 10715326

63. Monroig Ó, Kabeya N. Desaturases and elongases involved in polyunsaturated fatty acid biosynthesis in aquatic invertebrates: a comprehensive review. Fish Scie. 2018; 84: 911–928. doi: 10.1007/s12562-018-1254-x

64. Lubzens E, Marko A, Tietz A. De novo synthesis of fatty acids in the rotifer, Brachionus plicatilis. Aquaculture. 1985; 47(1): 27–37. doi: 10.1016/0044-8486(85)90005-5

65. Yin XW, Zhao W. Studies on life history characteristics of Brachionus plicatilis O. F. Müller (Rotifera) in relation to temperature, salinity and food algae. Aquat Ecol. 2008; 42(1):165–76. doi: 10.1007/s10452-007-9092-4

66. Lee SH, Lee MC, Puthumana J, Park JC, Kang S, Han J, et al. Efects of temperature on growth and fatty acid synthesis in the cyclopoid copepod Paracyclopina nana. Fish Sci. 2017; 83:725–734. doi: 10.1007/s12562-017-1104-2

67. Lee MC, Park JC, Kim DH, Kang S, Shin KH, Park HG, et al. Interrelationship of salinity shift with oxidative stress and lipid metabolism in the monogonont rotifer Brachionus koreanus. Comp Biochem Physiol A Mol Integr Physiol. 2017; 214: 79–84. doi: 10.1016/j.cbpa.2017.09.014 28951139

68. Lee MC, Park JC, Yoon DS, Choi H, Kim HJ, Shin KH, et al. Genome-wide characterization and expression of the elongation of very long chain fatty acid (Elovl) genes and fatty acid profiles in the alga Tetraselmis suecica-fed marine rotifer Brachionus koreanus. Comp Biochem Physiol Part D Genomics Proteomics. 2019; 30:179–185 doi: 10.1016/j.cbd.2019.03.001 30884356

69. Lingueglia E. Acid-sensing ion channels in sensory perception. J Biol Chem. 2007; 282(24): 17325–17329. doi: 10.1074/jbc.R700011200 17430882

70. Meistertzheim AL, Tanguy A, Moraga D, Thébault MT. Identification of differentially expressed genes of the Pacific oyster Crassostrea gigas exposed to prolonged thermal stress. FEBS J. 2007; 274(24): 6392–6402. doi: 10.1111/j.1742-4658.2007.06156.x 18005253

71. Smith S, Bernatchez L, Beheregaray LB. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics. 2013; 14(1): 375. doi: 10.1186/1471-2164-14-375 23738713

72. Kim BM, Kim K, Choi IY, Rhee JS. Transcriptome response of the Pacific oyster, Crassostrea gigas susceptible to thermal stress: A comparison with the response of tolerant oyster. Mol Cell Toxicol. 2017; 13(1): 105–13. doi: 10.1007/s13273-017-0011-z

73. Kops GJPL, Dansen TB, Polderman PE, Saarloos I, Wirtz KWA, Coffer PJ, et al. Forkhead transcription factor FOXO3a protects quiescent cells from oxidative stress. Nature. 2002; 419(6904): 316–321. doi: 10.1038/nature01036 12239572

74. Zhang X, Liu S, Takano T. Overexpression of a mitochondrial ATP synthase small subunit gene (AtMtATP6) confers tolerance to several abiotic stresses in Saccharomyces cerevisiae and Arabidopsis thaliana. Biotechnology Letters. 2008; 30(7): 1289–1294. doi: 10.1007/s10529-008-9685-6 18338219

75. Akbarian A, Michiels J, Degroote J, Majdeddin M, Golian A, De Smet S. Association between heat stress and oxidative stress in poultry; mitochondrial dysfunction and dietary interventions with phytochemicals. J Anim Sci Biotechno. 2016; 7: 37. doi: 10.1186/s40104-016-0097-5 27354915

76. Tsai YC, Wang YH, Liu YC. Overexpression of PCNA Attenuates Oxidative Stress-Caused Delay of Gap-Filling during Repair of UV-Induced DNA Damage. J Nucleic Acids. Article ID 8154646

77. Kenney JW, Moore CE, Wang X, Proud CG. Eukaryotic elongation factor 2 kinase, an unusual enzyme with multiple roles. Adv Biol Regul. 2014; 55; 15–27. doi: 10.1016/j.jbior.2014.04.003 24853390

78. Gismondi A, Caldarola S, Lisi G, Juli G, Chellini L, Iadevaia V, et al. Ribosomal stress activates eEF2K-eEF2 pathway causing translation elongation inhibition and recruitment of Terminal Oligopyrimidine (TOP) mRNAs on polysomes. Nucleic Acids Res. 2014; 42(10): 12668–12680. doi: 10.1093/nar/gku996 25332393

79. Scheper GC, Proud CG. Does phosphorylation of the cap-binding protein eIF4E play a role in translation initiation? Eur J Biochem. 2002; 269(22): 5350–5359. doi: 10.1046/j.1432-1033.2002.03291.x 12423333

80. Chen Q, Vazquez EJ, Moghaddas S, Hoppel CL, Lesnefsky EJ. Production of reactive oxygen species by mitochondria: Central role of complex III. J Biol Chem. 2003; 278(38): 36027–36031. doi: 10.1074/jbc.M304854200 12840017

81. Kirstein-Miles J, Scior A, Deuerling E, Morimoto RI. The nascent polypeptide-associated complex is a key regulator of proteostasis. EMBO J. 2013; 32(10):1451–68. doi: 10.1038/emboj.2013.87 23604074

82. Hales NR, Schield DR, Andrew AL, Card DC, Walsh MR C T. Contrasting gene expression programs correspond with predator-induced phenotypic plasticity within and across-generations in Daphnia. Mol Ecol. 2017; 26:5003–5015. doi: 10.1111/mec.14213 28628257

83. Tang W, Xiao Y, Li G, Zheng X, Yin Y, Wang L, et al. Analysis of digital gene expression profiling in the gonad of male silkworms (Bombyx mori) under fluoride stress. Ecotoxicol Environ Saf. 2018; 153: 127–34. doi: 10.1016/j.ecoenv.2018.01.028 29425843

84. Des Marteaux LE, McKinnon AH, Udaka H, Toxopeus J, Sinclair BJ. Effects of cold-acclimation on gene expression in Fall field cricket (Gryllus pennsylvanicus) ionoregulatory tissues. BMC Genomics. 2017; 18(1): 1–17.

85. Cui M, Hu P, Wang T, Tao J, Zong S. Differential transcriptome analysis reveals genes related to cold tolerance in seabuckthorn carpenter moth, Eogystia hippophaecolus. PLoS One. 2017; 12(11): 1–16. doi: 10.1371/journal.pone.0187105 29131867

86. Xu K, Niu Q, Zhao H, Du Y, Jiang Y. Transcriptomic analysis to uncover genes affecting cold resistance in the Chinese honey bee (Apis cerana cerana). PLoS One. 2017; 12(6): 1–15. doi: 10.1371/journal.pone.0179922 28650988

Within species expressed genetic variability and gene expression response to different temperatures in the rotifer Brachionus calyciflorus sensu stricto

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz