1932

Abstract

Pharmacology and toxicology are part of a much broader effort to understand the relationship between chemistry and biology. While biomedicine has necessarily focused on specific cases, typically of direct human relevance, there are real advantages in pursuing more systematic approaches to characterizing how health and disease are influenced by small molecules and other interventions. In this context, the zebrafish is now established as the representative screenable vertebrate and, through ongoing advances in the available scale of genome editing and automated phenotyping, is beginning to address systems-level solutions to some biomedical problems. The addition of broader efforts to integrate information content across preclinical model organisms and the incorporation of rigorous analytics, including closed-loop deep learning, will facilitate efforts to create systems pharmacology and toxicology with the ability to continuously optimize chemical biological interactions around societal needs. In this review, we outline progress toward this goal.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-051421-105617
2023-01-20
2024-10-08
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/63/1/annurev-pharmtox-051421-105617.html?itemId=/content/journals/10.1146/annurev-pharmtox-051421-105617&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Peterson RT, Link BA, Dowling JE, Schreiber SL. 2000. Small molecule developmental screens reveal the logic and timing of vertebrate development. PNAS 97:12965–69
    [Google Scholar]
  2. 2.
    Peterson RT, Shaw SY, Peterson TA, Milan DJ, Zhong TP et al. 2004. Chemical suppression of a genetic mutation in a zebrafish model of aortic coarctation. Nat. Biotechnol. 22:595–99
    [Google Scholar]
  3. 3.
    Lindner MD. 2007. Clinical attrition due to biased preclinical assessments of potential efficacy. Pharmacol. Ther. 115:148–75
    [Google Scholar]
  4. 4.
    Zhu W, Guo S, Homilius M, Nsubuga C, Wright SH et al. 2022. PIEZO1 mediates a mechanothrombotic pathway in diabetes. Sci. Transl. Med. 14:eabk1707
    [Google Scholar]
  5. 5.
    Chi LH, Burrows AD, Anderson RL. 2022. Can preclinical drug development help to predict adverse events in clinical trials?. Drug Discov. Today 27:257–68
    [Google Scholar]
  6. 6.
    Doudna JA, Charpentier E. 2014. The new frontier of genome engineering with CRISPR-Cas9. Science 346:1258096
    [Google Scholar]
  7. 7.
    Mohr SE, Smith JA, Shamu CE, Neumuller RA, Perrimon N. 2014. RNAi screening comes of age: improved techniques and complementary approaches. Nat. Rev. Mol. Cell Biol. 15:591–600
    [Google Scholar]
  8. 8.
    Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA et al. 2014. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343:84–87
    [Google Scholar]
  9. 9.
    Kampmann M, Bassik MC, Weissman JS. 2014. Functional genomics platform for pooled screening and generation of mammalian genetic interaction maps. Nat. Protoc. 9:1825–47
    [Google Scholar]
  10. 10.
    Ong SE, Schenone M, Margolin AA, Li X, Do K et al. 2009. Identifying the proteins to which small-molecule probes and drugs bind in cells. PNAS 106:4617–22
    [Google Scholar]
  11. 11.
    Schreiber SL. 2021. The rise of molecular glues. Cell 184:3–9
    [Google Scholar]
  12. 12.
    Renaud JP, Chari A, Ciferri C, Liu WT, Remigy HW et al. 2018. Cryo-EM in drug discovery: achievements, limitations and prospects. Nat. Rev. Drug Discov. 17:471–92
    [Google Scholar]
  13. 13.
    Lavecchia A. 2019. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov. Today 24:2017–32
    [Google Scholar]
  14. 14.
    Gibson CC, Zhu W, Davis CT, Bowman-Kirigin JA, Chan AC et al. 2014. Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation 131:289–99
    [Google Scholar]
  15. 15.
    Maltarollo VG, Gertrudes JC, Oliveira PR, Honorio KM. 2015. Applying machine learning techniques for ADME-Tox prediction: a review. Expert Opin. Drug Metab. Toxicol. 11:259–71
    [Google Scholar]
  16. 16.
    Bleicher KH, Bohm HJ, Muller K, Alanine AI. 2003. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov. 2:369–78
    [Google Scholar]
  17. 17.
    Kulkarni JA, Witzigmann D, Thomson SB, Chen S, Leavitt BR et al. 2021. The current landscape of nucleic acid therapeutics. Nat. Nanotechnol. 16:630–43
    [Google Scholar]
  18. 18.
    Pelkonen O, Raunio H. 2005. In vitro screening of drug metabolism during drug development: can we trust the predictions?. Expert Opin. Drug Metab. Toxicol. 1:49–59
    [Google Scholar]
  19. 19.
    Sharma A, Sances S, Workman MJ, Svendsen CN. 2020. Multi-lineage human iPSC-derived platforms for disease modeling and drug discovery. Cell Stem Cell 26:309–29
    [Google Scholar]
  20. 20.
    Prantil-Baun R, Novak R, Das D, Somayaji MR, Przekwas A, Ingber DE. 2018. Physiologically based pharmacokinetic and pharmacodynamic analysis enabled by microfluidically linked organs-on-chips. Annu. Rev. Pharmacol. Toxicol. 58:37–64
    [Google Scholar]
  21. 21.
    Carpenter AE, Sabatini DM. 2004. Systematic genome-wide screens of gene function. Nat. Rev. Genet. 5:11–22
    [Google Scholar]
  22. 22.
    Bagheri N, Carpenter AE, Lundberg E, Plant AL, Horwitz R. 2022. The new era of quantitative cell imaging-challenges and opportunities. Mol. Cell 82:241–47
    [Google Scholar]
  23. 23.
    Filippatos TD, Klouras E, Barkas F, Elisaf M. 2016. Cholesteryl ester transfer protein inhibitors: challenges and perspectives. Expert Rev. Cardiovasc. Ther. 14:953–62
    [Google Scholar]
  24. 24.
    St Johnston D. 2002. The art and design of genetic screens: Drosophila melanogaster. Nat. Rev. Genet. 3:176–88
    [Google Scholar]
  25. 25.
    Yeh JR, Crews CM. 2003. Chemical genetics: adding to the developmental biology toolbox. Dev. Cell 5:11–19
    [Google Scholar]
  26. 26.
    Stockwell BR. 2000. Chemical genetics: ligand-based discovery of gene function. Nat. Rev. Genet. 1:116–25
    [Google Scholar]
  27. 27.
    Kokel D, Bryan J, Laggner C, White R, Cheung CY et al. 2010. Rapid behavior-based identification of neuroactive small molecules in the zebrafish. Nat. Chem. Biol. 6:231–37
    [Google Scholar]
  28. 28.
    Patton EE, Zon LI, Langenau DM. 2021. Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. Nat. Rev. Drug Discov. 20:611–28
    [Google Scholar]
  29. 29.
    Parvez S, Herdman C, Beerens M, Chakraborti K, Harmer ZP et al. 2021. MIC-Drop: a platform for large-scale in vivo CRISPR screens. Science 373:1146–51
    [Google Scholar]
  30. 30.
    Rihel J, Prober DA, Arvanites A, Lam K, Zimmerman S et al. 2010. Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 327:348–51
    [Google Scholar]
  31. 31.
    Cheng KC, Katz SR, Lin AY, Xin X, Ding Y. 2016. Whole-organism cellular pathology: a systems approach to phenomics. Adv. Genet. 95:89–115
    [Google Scholar]
  32. 32.
    Choi TY, Choi TI, Lee YR, Choe SK, Kim CH. 2021. Zebrafish as an animal model for biomedical research. Exp. Mol. Med. 53:310–17
    [Google Scholar]
  33. 33.
    Patton EE, Zon LI. 2001. The art and design of genetic screens: zebrafish. Nat. Rev. Genet. 2:956–66
    [Google Scholar]
  34. 34.
    Milan DJ, Kim AM, Winterfield JR, Jones IL, Pfeufer A et al. 2009. Drug-sensitized zebrafish screen identifies multiple genes, including GINS3, as regulators of myocardial repolarization. Circulation 120:553–59
    [Google Scholar]
  35. 35.
    Vaz RL, Outeiro TF, Ferreira JJ. 2018. Zebrafish as an animal model for drug discovery in Parkinson's disease and other movement disorders: a systematic review. Front. Neurol. 9:347
    [Google Scholar]
  36. 36.
    Adatto I, Lawrence C, Thompson M, Zon LI. 2011. A new system for the rapid collection of large numbers of developmentally staged zebrafish embryos. PLOS ONE 6:e21715
    [Google Scholar]
  37. 37.
    Suurvali J, Whiteley AR, Zheng Y, Gharbi K, Leptin M, Wiehe T. 2020. The laboratory domestication of zebrafish: from diverse populations to inbred substrains. Mol. Biol. Evol. 37:1056–69
    [Google Scholar]
  38. 38.
    Mullins MC, Hammerschmidt M, Haffter P, Nusslein-Volhard C. 1994. Large-scale mutagenesis in the zebrafish: in search of genes controlling development in a vertebrate. Curr. Biol. 4:189–202
    [Google Scholar]
  39. 39.
    Golling G, Amsterdam A, Sun Z, Antonelli M, Maldonado E et al. 2002. Insertional mutagenesis in zebrafish rapidly identifies genes essential for early vertebrate development. Nat. Genet. 31:135–40
    [Google Scholar]
  40. 40.
    Prykhozhij SV, Caceres L, Berman JN. 2017. New developments in CRISPR/Cas-based functional genomics and their implications for research using zebrafish. Curr. Gene Ther. 17:286–300
    [Google Scholar]
  41. 41.
    Wu RS, Lam II, Clay H, Duong DN, Deo RC, Coughlin SR. 2018. A rapid method for directed gene knockout for screening in G0 zebrafish. Dev. Cell 46:112–25.e4
    [Google Scholar]
  42. 42.
    Hoshijima K, Jurynec MJ, Grunwald DJ. 2016. Precise editing of the zebrafish genome made simple and efficient. Dev. Cell 36:654–67
    [Google Scholar]
  43. 43.
    Zhang Y, Huang H, Zhang B, Lin S 2016. TALEN- and CRISPR-enhanced DNA homologous recombination for gene editing in zebrafish. Methods Cell Biol. 135:107–20
    [Google Scholar]
  44. 44.
    Lawrence C, Mason T. 2012. Zebrafish housing systems: a review of basic operating principles and considerations for design and functionality. ILAR J. 53:179–91
    [Google Scholar]
  45. 45.
    Pardo-Martin C, Chang TY, Koo BK, Gilleland CL, Wasserman SC, Yanik MF. 2010. High-throughput in vivo vertebrate screening. Nat. Methods 7:634–36
    [Google Scholar]
  46. 46.
    Milan DJ, Peterson TA, Ruskin JN, Peterson RT, MacRae CA. 2003. Drugs that induce repolarization abnormalities cause bradycardia in zebrafish. Circulation 107:1355–58
    [Google Scholar]
  47. 47.
    Zhou W, Hildebrandt F. 2012. Inducible podocyte injury and proteinuria in transgenic zebrafish. J. Am. Soc. Nephrol. 23:1039–47
    [Google Scholar]
  48. 48.
    van Ham TJ, Mapes J, Kokel D, Peterson RT. 2010. Live imaging of apoptotic cells in zebrafish. FASEB J. 24:4336–42
    [Google Scholar]
  49. 49.
    Niethammer P, Grabher C, Look AT, Mitchison TJ. 2009. A tissue-scale gradient of hydrogen peroxide mediates rapid wound detection in zebrafish. Nature 459:996–99
    [Google Scholar]
  50. 50.
    Kumar S, Alibhai D, Margineanu A, Laine R, Kennedy G et al. 2011. FLIM FRET technology for drug discovery: automated multiwell-plate high-content analysis, multiplexed readouts and application in situ. ChemPhysChem 12:609–26
    [Google Scholar]
  51. 51.
    Xie Y, Ottolia M, John SA, Chen JN, Philipson KD. 2008. Conformational changes of a Ca2+-binding domain of the Na+/Ca2+ exchanger monitored by FRET in transgenic zebrafish heart. Am. J. Physiol. Cell Physiol. 295:C388–93
    [Google Scholar]
  52. 52.
    Farber SA, Pack M, Ho SY, Johnson ID, Wagner DS et al. 2001. Genetic analysis of digestive physiology using fluorescent phospholipid reporters. Science 292:1385–88
    [Google Scholar]
  53. 53.
    Adamantidis AR, Zhang F, Aravanis AM, Deisseroth K, de Lecea L. 2007. Neural substrates of awakening probed with optogenetic control of hypocretin neurons. Nature 450:420–24
    [Google Scholar]
  54. 54.
    Stuckenholz C, Lu L, Thakur P, Kaminski N, Bahary N. 2009. FACS-assisted microarray profiling implicates novel genes and pathways in zebrafish gastrointestinal tract development. Gastroenterology 137:1321–32
    [Google Scholar]
  55. 55.
    Gallardo VE, Behra M. 2013. Fluorescent activated cell sorting (FACS) combined with gene expression microarrays for transcription enrichment profiling of zebrafish lateral line cells. Methods 62:226–31
    [Google Scholar]
  56. 56.
    Becker JR, Chatterjee S, Robinson TY, Bennett JS, Panakova D et al. 2014. Differential activation of natriuretic peptide receptors modulates cardiomyocyte proliferation during development. Development 141:335–45
    [Google Scholar]
  57. 57.
    Schutera M, Dickmeis T, Mione M, Peravali R, Marcato D et al. 2016. Automated phenotype pattern recognition of zebrafish for high-throughput screening. Bioengineered 7:261–65
    [Google Scholar]
  58. 58.
    Satija R, Farrell JA, Gennert D, Schier AF, Regev A. 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33:495–502
    [Google Scholar]
  59. 59.
    Avagyan S, Weber MC, Ma S, Prasad M, Mannherz WP et al. 2021. Single-cell ATAC-seq reveals GATA2-dependent priming defect in myeloid and a maturation bottleneck in lymphoid lineages. Blood Adv 5:2673–86
    [Google Scholar]
  60. 60.
    Sips PY, Shi X, Musso G, Nath AK, Zhao Y et al. 2018. Identification of specific metabolic pathways as druggable targets regulating the sensitivity to cyanide poisoning. PLOS ONE 13:e0193889
    [Google Scholar]
  61. 61.
    Otis JP, Shen MC, Caldwell BA, Reyes Gaido OE, Farber SA 2019. Dietary cholesterol and apolipoprotein A-I are trafficked in endosomes and lysosomes in the live zebrafish intestine. Am. J. Physiol. Gastrointest. Liver Physiol. 316:G350–65
    [Google Scholar]
  62. 62.
    Winkler S, Gscheidel N, Brand M. 2011. Mutant generation in vertebrate model organisms by TILLING. Methods Mol. Biol. 770:475–504
    [Google Scholar]
  63. 63.
    Gjini E, Mansour MR, Sander JD, Moritz N, Nguyen AT et al. 2015. A zebrafish model of myelodysplastic syndrome produced through tet2 genomic editing. Mol. Cell. Biol. 35:789–804
    [Google Scholar]
  64. 64.
    Avagyan S, Henninger JE, Mannherz WP, Mistry M, Yoon J et al. 2021. Resistance to inflammation underlies enhanced fitness in clonal hematopoiesis. Science 374:768–72
    [Google Scholar]
  65. 65.
    Okazaki F, Zang L, Nakayama H, Chen Z, Gao ZJ et al. 2019. Microbiome alteration in type 2 diabetes mellitus model of zebrafish. Sci. Rep. 9:867
    [Google Scholar]
  66. 66.
    Nasevicius A, Ekker SC. 2000. Effective targeted gene ‘knockdown’ in zebrafish. Nat. Genet. 26:216–20
    [Google Scholar]
  67. 67.
    Rossi A, Kontarakis Z, Gerri C, Nolte H, Holper S et al. 2015. Genetic compensation induced by deleterious mutations but not gene knockdowns. Nature 524:230–33
    [Google Scholar]
  68. 68.
    Kok FO, Shin M, Ni CW, Gupta A, Grosse AS et al. 2015. Reverse genetic screening reveals poor correlation between morpholino-induced and mutant phenotypes in zebrafish. Dev. Cell 32:97–108
    [Google Scholar]
  69. 69.
    Musso G, Tasan M, Mosimann C, Beaver JE, Plovie E et al. 2014. Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafish. Development 141:224–35
    [Google Scholar]
  70. 70.
    Wienholds E, Schulte-Merker S, Walderich B, Plasterk RH. 2002. Target-selected inactivation of the zebrafish rag1 gene. Science 297:99–102
    [Google Scholar]
  71. 71.
    Meng X, Noyes MB, Zhu LJ, Lawson ND, Wolfe SA. 2008. Targeted gene inactivation in zebrafish using engineered zinc-finger nucleases. Nat. Biotechnol. 26:695–701
    [Google Scholar]
  72. 72.
    Foley JE, Maeder ML, Pearlberg J, Joung JK, Peterson RT, Yeh JR. 2009. Targeted mutagenesis in zebrafish using customized zinc-finger nucleases. Nat. Protoc. 4:1855–67
    [Google Scholar]
  73. 73.
    Varshney GK, Carrington B, Pei W, Bishop K, Chen Z et al. 2016. A high-throughput functional genomics workflow based on CRISPR/Cas9-mediated targeted mutagenesis in zebrafish. Nat. Protoc. 11:2357–75
    [Google Scholar]
  74. 74.
    MacRae CA, Vasan RS. 2016. The future of genetics and genomics: closing the phenotype gap in precision medicine. Circulation 133:2634–39
    [Google Scholar]
  75. 75.
    Zon LI, Peterson RT. 2005. In vivo drug discovery in the zebrafish. Nat. Rev. Drug Discov. 4:35–44
    [Google Scholar]
  76. 76.
    MacRae CA. 2019. Closing the ‘phenotype gap’ in precision medicine: improving what we measure to understand complex disease mechanisms. Mamm. Genome 30:201–11
    [Google Scholar]
  77. 77.
    Milan DJ, Giokas AC, Serluca FC, Peterson RT, MacRae CA. 2006. Notch1b and neuregulin are required for specification of central cardiac conduction tissue. Development 133:1125–32
    [Google Scholar]
  78. 78.
    Ton C, Stamatiou D, Dzau VJ, Liew CC. 2002. Construction of a zebrafish cDNA microarray: gene expression profiling of the zebrafish during development. Biochem. Biophys. Res. Commun. 296:1134–42
    [Google Scholar]
  79. 79.
    Carney SA, Chen J, Burns CG, Xiong KM, Peterson RE, Heideman W. 2006. Aryl hydrocarbon receptor activation produces heart-specific transcriptional and toxic responses in developing zebrafish. Mol. Pharmacol. 70:549–61
    [Google Scholar]
  80. 80.
    Sul JY, Wu CW, Zeng F, Jochems J, Lee MT et al. 2009. Transcriptome transfer produces a predictable cellular phenotype. PNAS 106:7624–29
    [Google Scholar]
  81. 81.
    Hayashi S, Akiyama S, Tamaru Y, Takeda Y, Fujiwara T et al. 2009. A novel application of metabolomics in vertebrate development. Biochem. Biophys. Res. Commun. 386:268–72
    [Google Scholar]
  82. 82.
    Ong ES, Chor CF, Zou L, Ong CN. 2009. A multi-analytical approach for metabolomic profiling of zebrafish (Danio rerio) livers. Mol. Biosyst. 5:288–98
    [Google Scholar]
  83. 83.
    Perelsman O, Asano S, Freifeld L. 2022. Expansion microscopy of larval zebrafish brains and zebrafish embryos. Methods Mol. Biol. 2440:211–22
    [Google Scholar]
  84. 84.
    Kuroda J, Itabashi T, Iwane AH, Aramaki T, Kondo S. 2020. The physical role of mesenchymal cells driven by the actin cytoskeleton is essential for the orientation of collagen fibrils in zebrafish fins. Front. Cell Dev. Biol. 8:580520
    [Google Scholar]
  85. 85.
    MacRae CA, Peterson RT. 2003. Zebrafish-based small molecule discovery. Chem. Biol. 10:901–8
    [Google Scholar]
  86. 86.
    Yu PB, Deng DY, Lai CS, Hong CC, Cuny GD et al. 2008. BMP type I receptor inhibition reduces heterotopic ossification. Nat. Med. 14:1363–69
    [Google Scholar]
  87. 87.
    Yu PB, Hong CC, Sachidanandan C, Babitt JL, Deng DY et al. 2008. Dorsomorphin inhibits BMP signals required for embryogenesis and iron metabolism. Nat. Chem. Biol. 4:33–41
    [Google Scholar]
  88. 88.
    Hao J, Daleo MA, Murphy CK, Yu PB, Ho JN et al. 2008. Dorsomorphin, a selective small molecule inhibitor of BMP signaling, promotes cardiomyogenesis in embryonic stem cells. PLOS ONE 3:e2904
    [Google Scholar]
  89. 89.
    Gunsalus KC, Ge H, Schetter AJ, Goldberg DS, Han JD et al. 2005. Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Nature 436:861–65
    [Google Scholar]
  90. 90.
    Walhout AJ, Reboul J, Shtanko O, Bertin N, Vaglio P et al. 2002. Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline. Curr. Biol. 12:1952–58
    [Google Scholar]
  91. 91.
    Trevarrow B, Robison B. 2004. Genetic backgrounds, standard lines, and husbandry of zebrafish. Methods Cell Biol 77:599–616
    [Google Scholar]
  92. 92.
    Tran TC, Sneed B, Haider J, Blavo D, White A et al. 2007. Automated, quantitative screening assay for antiangiogenic compounds using transgenic zebrafish. Cancer Res 67:11386–92
    [Google Scholar]
  93. 93.
    Cassar S, Adatto I, Freeman JL, Gamse JT, Iturria I et al. 2020. Use of zebrafish in drug discovery toxicology. Chem. Res. Toxicol. 33:95–118
    [Google Scholar]
  94. 94.
    Gustafson AL, Stedman DB, Ball J, Hillegass JM, Flood A et al. 2012. Inter-laboratory assessment of a harmonized zebrafish developmental toxicology assay—progress report on phase I. Reprod. Toxicol. 33:155–64
    [Google Scholar]
  95. 95.
    Peal DS, Burns CG, Macrae CA, Milan D. 2009. Chondroitin sulfate expression is required for cardiac atrioventricular canal formation. Dev. Dyn. 238:3103–10
    [Google Scholar]
  96. 96.
    Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R. 2006. Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24:167–75
    [Google Scholar]
  97. 97.
    Chen L, Su B, Yu J, Wang J, Hu H et al. 2022. Combined effects of arsenic and 2,2-dichloroacetamide on different cell populations of zebrafish liver. Sci. Total Environ. 821:152961
    [Google Scholar]
  98. 98.
    Yu J, Cheng W, Jia M, Chen L, Gu C et al. 2022. Toxicity of perfluorooctanoic acid on zebrafish early embryonic development determined by single-cell RNA sequencing. J. Hazard. Mater. 427:127888
    [Google Scholar]
  99. 99.
    Yang P, Takahashi H, Murase M, Itoh M. 2021. Zebrafish behavior feature recognition using three-dimensional tracking and machine learning. Sci. Rep. 11:13492
    [Google Scholar]
  100. 100.
    Ghazizadeh Z, Kiviniemi T, Olafsson S, Plotnick D, Beerens ME et al. 2020. Metastable atrial state underlies the primary genetic substrate for MYL4 mutation-associated atrial fibrillation. Circulation 141:301–12
    [Google Scholar]
  101. 101.
    Macrae CA. 2010. Cardiac arrhythmia: in vivo screening in the zebrafish to overcome complexity in drug discovery. Expert Opin. Drug Discov. 5:619–32
    [Google Scholar]
  102. 102.
    Xu X, Meiler SE, Zhong TP, Mohideen M, Crossley DA et al. 2002. Cardiomyopathy in zebrafish due to mutation in an alternatively spliced exon of titin. Nat. Genet. 30:205–9
    [Google Scholar]
  103. 103.
    Gerull B, Gramlich M, Atherton J, McNabb M, Trombitas K et al. 2002. Mutations of TTN, encoding the giant muscle filament titin, cause familial dilated cardiomyopathy. Nat. Genet. 30:201–4
    [Google Scholar]
  104. 104.
    Khersonsky SM, Jung DW, Kang TW, Walsh DP, Moon HS et al. 2003. Facilitated forward chemical genetics using a tagged triazine library and zebrafish embryo screening. J. Am. Chem. Soc. 125:11804–5
    [Google Scholar]
  105. 105.
    Langheinrich U. 2003. Zebrafish: a new model on the pharmaceutical catwalk. Bioessays 25:904–12
    [Google Scholar]
  106. 106.
    Weinstein BM, Stemple DL, Driever W, Fishman MC. 1995. gridlock, a localized heritable vascular patterning defect in the zebrafish. Nat. Med. 1:1143–47
    [Google Scholar]
  107. 107.
    Ren B, Deng Y, Mukhopadhyay A, Lanahan AA, Zhuang ZW et al. 2010. ERK1/2-Akt1 crosstalk regulates arteriogenesis in mice and zebrafish. J. Clin. Investig. 120:1217–28
    [Google Scholar]
  108. 108.
    Deo RC, Macrae CA. 2011. The zebrafish: scalable in vivo modeling for systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 3:335–46
    [Google Scholar]
  109. 109.
    Goessling W, North TE, Loewer S, Lord AM, Lee S et al. 2009. Genetic interaction of PGE2 and Wnt signaling regulates developmental specification of stem cells and regeneration. Cell 136:1136–47
    [Google Scholar]
  110. 110.
    Liu Y, Asnani A, Zou L, Bentley VL, Yu M et al. 2014. Visnagin protects against doxorubicin-induced cardiomyopathy through modulation of mitochondrial malate dehydrogenase. Sci. Transl. Med. 6:266ra170
    [Google Scholar]
  111. 111.
    Lam PY, Kutchukian P, Anand R, Imbriglio J, Andrews C et al. 2020. Cyp1 inhibition prevents doxorubicin-induced cardiomyopathy in a zebrafish heart-failure model. ChemBioChem 21:1905–10
    [Google Scholar]
  112. 112.
    Owens KN, Santos F, Roberts B, Linbo T, Coffin AB et al. 2008. Identification of genetic and chemical modulators of zebrafish mechanosensory hair cell death. PLOS Genet 4:e1000020
    [Google Scholar]
  113. 113.
    Chowdhury S, Owens KN, Herr RJ, Jiang Q, Chen X et al. 2018. Phenotypic optimization of urea-thiophene carboxamides to yield potent, well tolerated, and orally active protective agents against aminoglycoside-induced hearing loss. J. Med. Chem. 61:84–97
    [Google Scholar]
  114. 114.
    Nath AK, Roberts LD, Liu Y, Mahon SB, Kim S et al. 2013. Chemical and metabolomic screens identify novel biomarkers and antidotes for cyanide exposure. FASEB J 27:1928–38
    [Google Scholar]
  115. 115.
    MacRae CA, Boss G, Brenner M, Gerszten RE, Mahon S, Peterson RT. 2016. A countermeasure development pipeline. Ann. N. Y. Acad. Sci. 1378:58–67
    [Google Scholar]
  116. 116.
    Nath AK, Shi X, Harrison DL, Morningstar JE, Mahon S et al. 2017. Cisplatin analogs confer protection against cyanide poisoning. Cell Chem. Biol. 24:565–75.e4
    [Google Scholar]
  117. 117.
    Morningstar J, Lee J, Hendry-Hofer T, Witeof A, Lyle LT et al. 2019. Intramuscular administration of hexachloroplatinate reverses cyanide-induced metabolic derangements and counteracts severe cyanide poisoning. FASEB Bioadv. 1:81–92
    [Google Scholar]
  118. 118.
    Nielson JR, Nath AK, Doane KP, Shi X, Lee J et al. 2022. Glyoxylate protects against cyanide toxicity through metabolic modulation. Sci. Rep. 12:4982
    [Google Scholar]
  119. 119.
    Peterson RT, Macrae CA. 2012. Systematic approaches to toxicology in the zebrafish. Annu. Rev. Pharmacol. Toxicol. 52:433–53
    [Google Scholar]
  120. 120.
    Horzmann KA, Freeman JL. 2018. Making waves: new developments in toxicology with the zebrafish. Toxicol. Sci. 163:5–12
    [Google Scholar]
  121. 121.
    Lai KP, Gong Z, Tse WKF. 2021. Zebrafish as the toxicant screening model: transgenic and omics approaches. Aquat. Toxicol. 234:105813
    [Google Scholar]
  122. 122.
    McAleer MF, Davidson C, Davidson WR, Yentzer B, Farber SA et al. 2005. Novel use of zebrafish as a vertebrate model to screen radiation protectors and sensitizers. Int. J. Radiat. Oncol. Biol. Phys. 61:10–13
    [Google Scholar]
  123. 123.
    Lammer E, Kamp HG, Hisgen V, Koch M, Reinhard D et al. 2009. Development of a flow-through system for the fish embryo toxicity test (FET) with the zebrafish (Danio rerio). Toxicol. In Vitro 23:1436–42
    [Google Scholar]
  124. 124.
    Augustine-Rauch K, Zhang CX, Panzica-Kelly JM. 2010. In vitro developmental toxicology assays: a review of the state of the science of rodent and zebrafish whole embryo culture and embryonic stem cell assays. Birth Defects Res. C Embryo Today 90:87–98
    [Google Scholar]
  125. 125.
    Brannen KC, Panzica-Kelly JM, Danberry TL, Augustine-Rauch KA. 2010. Development of a zebrafish embryo teratogenicity assay and quantitative prediction model. Birth Defects Res. B Dev. Reprod. Toxicol. 89:66–77
    [Google Scholar]
  126. 126.
    Truong L, Harper SL, Tanguay RL. 2011. Evaluation of embryotoxicity using the zebrafish model. Methods Mol. Biol. 691:271–79
    [Google Scholar]
  127. 127.
    Roden DM. 2004. Drug-induced prolongation of the QT interval. N. Engl. J. Med. 350:1013–22
    [Google Scholar]
  128. 128.
    Cavero I, Mestre M, Guillon JM, Crumb W. 2000. Drugs that prolong QT interval as an unwanted effect: assessing their likelihood of inducing hazardous cardiac dysrhythmias. Expert Opin. Pharmacother. 1:947–73
    [Google Scholar]
  129. 129.
    Furutani M, Trudeau MC, Hagiwara N, Seki A, Gong Q et al. 1999. Novel mechanism associated with an inherited cardiac arrhythmia: defective protein trafficking by the mutant HERG (G601S) potassium channel. Circulation 99:2290–94
    [Google Scholar]
  130. 130.
    Eckardt L, Haverkamp W, Borggrefe M, Breithardt G. 1998. Experimental models of torsade de pointes. Cardiovasc. Res. 39:178–93
    [Google Scholar]
  131. 131.
    Langheinrich U, Vacun G, Wagner T. 2003. Zebrafish embryos express an orthologue of HERG and are sensitive toward a range of QT-prolonging drugs inducing severe arrhythmia. Toxicol. Appl. Pharmacol. 193:370–82
    [Google Scholar]
  132. 132.
    Nerbonne JM, Kass RS. 2005. Molecular physiology of cardiac repolarization. Physiol. Rev. 85:1205–53
    [Google Scholar]
  133. 133.
    Panáková D, Werdich AA, Macrae CA. 2010. Wnt11 patterns a myocardial electrical gradient through regulation of the L-type Ca2+ channel. Nature 466:874–78
    [Google Scholar]
  134. 134.
    Langenbacher AD, Dong Y, Shu X, Choi J, Nicoll DA et al. 2005. Mutation in sodium-calcium exchanger 1 (NCX1) causes cardiac fibrillation in zebrafish. PNAS 102:17699–704
    [Google Scholar]
  135. 135.
    Ung CY, Lam SH, Hlaing MM, Winata CL, Korzh S et al. 2010. Mercury-induced hepatotoxicity in zebrafish: in vivo mechanistic insights from transcriptome analysis, phenotype anchoring and targeted gene expression validation. BMC Genom. 11:212
    [Google Scholar]
  136. 136.
    Cox AG, Saunders DC, Kelsey PB Jr., Conway AA, Tesmenitsky Y et al. 2014. S-nitrosothiol signaling regulates liver development and improves outcome following toxic liver injury. Cell Rep 6:56–69
    [Google Scholar]
  137. 137.
    Shwartz A, Goessling W, Yin C. 2019. Macrophages in zebrafish models of liver diseases. Front. Immunol. 10:2840
    [Google Scholar]
  138. 138.
    Weeks O, Bosse GD, Oderberg IM, Akle S, Houvras Y et al. 2020. Fetal alcohol spectrum disorder predisposes to metabolic abnormalities in adulthood. J. Clin. Investig. 130:2252–69
    [Google Scholar]
  139. 139.
    Ebarasi L, Oddsson A, Hultenby K, Betsholtz C, Tryggvason K. 2011. Zebrafish: a model system for the study of vertebrate renal development, function, and pathophysiology. Curr. Opin. Nephrol. Hypertens. 20:416–24
    [Google Scholar]
  140. 140.
    Drummond IA, Majumdar A, Hentschel H, Elger M, Solnica-Krezel L et al. 1998. Early development of the zebrafish pronephros and analysis of mutations affecting pronephric function. Development 125:4655–67
    [Google Scholar]
  141. 141.
    Kramer-Zucker AG, Wiessner S, Jensen AM, Drummond IA. 2005. Organization of the pronephric filtration apparatus in zebrafish requires Nephrin, Podocin and the FERM domain protein Mosaic eyes. Dev. Biol. 285:316–29
    [Google Scholar]
  142. 142.
    Kolatsi-Joannou M, Osborn D 2020. A technique for studying glomerular filtration integrity in the zebrafish pronephros. Methods Mol. Biol. 2067:25–39
    [Google Scholar]
  143. 143.
    Darland T, Dowling JE. 2001. Behavioral screening for cocaine sensitivity in mutagenized zebrafish. PNAS 98:11691–96
    [Google Scholar]
  144. 144.
    Irons TD, MacPhail RC, Hunter DL, Padilla S. 2010. Acute neuroactive drug exposures alter locomotor activity in larval zebrafish. Neurotoxicol. Teratol. 32:84–90
    [Google Scholar]
  145. 145.
    Bruni G, Rennekamp AJ, Velenich A, McCarroll M, Gendelev L et al. 2016. Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds. Nat. Chem. Biol. 12:559–66
    [Google Scholar]
  146. 146.
    Rennekamp AJ, Huang XP, Wang Y, Patel S, Lorello PJ et al. 2016. σ1 Receptor ligands control a switch between passive and active threat responses. Nat. Chem. Biol. 12:552–58
    [Google Scholar]
  147. 147.
    Tang W, Davidson JD, Zhang G, Conen KE, Fang J et al. 2020. Genetic control of collective behavior in zebrafish. iScience 23:100942
    [Google Scholar]
  148. 148.
    Marquez-Legorreta E, Constantin L, Piber M, Favre-Bulle IA, Taylor MA et al. 2022. Brain-wide visual habituation networks in wild type and fmr1 zebrafish. Nat. Commun. 13:895
    [Google Scholar]
  149. 149.
    Clifton JD, Lucumi E, Myers MC, Napper A, Hama K et al. 2010. Identification of novel inhibitors of dietary lipid absorption using zebrafish. PLOS ONE 5:e12386
    [Google Scholar]
  150. 150.
    Berghmans S, Butler P, Goldsmith P, Waldron G, Gardner I et al. 2008. Zebrafish based assays for the assessment of cardiac, visual and gut function–potential safety screens for early drug discovery. J. Pharmacol. Toxicol. Methods 58:59–68
    [Google Scholar]
  151. 151.
    Field HA, Kelley KA, Martell L, Goldstein AM, Serluca FC. 2009. Analysis of gastrointestinal physiology using a novel intestinal transit assay in zebrafish. Neurogastroenterol. Motil. 21:304–12
    [Google Scholar]
  152. 152.
    Hanai J, Cao P, Tanksale P, Imamura S, Koshimizu E et al. 2007. The muscle-specific ubiquitin ligase atrogin-1/MAFbx mediates statin-induced muscle toxicity. J. Clin. Investig. 117:3940–51
    [Google Scholar]
  153. 153.
    Goessling W, North TE, Zon LI. 2007. New waves of discovery: modeling cancer in zebrafish. J. Clin. Oncol. 25:2473–79
    [Google Scholar]
  154. 154.
    Gjini E, Jing CB, Nguyen AT, Reyon D, Gans E et al. 2019. Disruption of asxl1 results in myeloproliferative neoplasms in zebrafish. Dis. Model. Mech. 12:dmm035790
    [Google Scholar]
  155. 155.
    Kobar K, Collett K, Prykhozhij SV, Berman JN. 2021. Zebrafish cancer predisposition models. Front. Cell Dev. Biol. 9:660069
    [Google Scholar]
  156. 156.
    Liu F, Gentles A, Theodorakis CW. 2008. Arsenate and perchlorate toxicity, growth effects, and thyroid histopathology in hypothyroid zebrafish Danio rerio. Chemosphere 71:1369–76
    [Google Scholar]
  157. 157.
    Tang Q, Abdelfattah NS, Blackburn JS, Moore JC, Martinez SA et al. 2014. Optimized cell transplantation using adult rag2 mutant zebrafish. Nat. Methods 11:821–24
    [Google Scholar]
  158. 158.
    Beck DB, Ferrada MA, Sikora KA, Ombrello AK, Collins JC et al. 2020. Somatic mutations in UBA1 and severe adult-onset autoinflammatory disease. N. Engl. J. Med. 383:2628–38
    [Google Scholar]
  159. 159.
    Roden DM. 1998. Mechanisms and management of proarrhythmia. Am. J. Cardiol. 82:49I–57I
    [Google Scholar]
  160. 160.
    Keating MT, Sanguinetti MC. 2001. Molecular and cellular mechanisms of cardiac arrhythmias. Cell 104:569–80
    [Google Scholar]
  161. 161.
    Camm AJ, Janse MJ, Roden DM, Rosen MR, Cinca J, Cobbe SM. 2000. Congenital and acquired long QT syndrome. Eur. Heart J. 21:1232–37
    [Google Scholar]
  162. 162.
    Sanguinetti MC, Jiang C, Curran ME, Keating MT. 1995. A mechanistic link between an inherited and an acquired cardiac arrhythmia: HERG encodes the IKr potassium channel. Cell 81:299–307
    [Google Scholar]
  163. 163.
    Roy M, Dumaine R, Brown AM. 1996. HERG, a primary human ventricular target of the nonsedating antihistamine terfenadine. Circulation 94:817–23
    [Google Scholar]
  164. 164.
    Mitcheson JS, Chen J, Lin M, Culberson C, Sanguinetti MC. 2000. A structural basis for drug-induced long QT syndrome. PNAS 97:12329–33
    [Google Scholar]
  165. 165.
    Yang Y, Xu C, Ge F, Lu Z, Zhu G et al. 2001. Heterologous expression of the single-mutation glucose isomerase (GIG138P) gene in Streptomyces lividans and its genetic instability. Curr. Microbiol. 42:241–47
    [Google Scholar]
  166. 166.
    Roden DM. 2001. Defective ion channel function in the long QT syndrome: multiple unexpected mechanisms. J. Mol. Cell. Cardiol. 33:185–87
    [Google Scholar]
  167. 167.
    Martin RL, McDermott JS, Salmen HJ, Palmatier J, Cox BF, Gintant GA. 2004. The utility of hERG and repolarization assays in evaluating delayed cardiac repolarization: influence of multi-channel block. J. Cardiovasc. Pharmacol. 43:369–79
    [Google Scholar]
  168. 168.
    Baker K, Warren KS, Yellen G, Fishman MC. 1997. Defective “pacemaker” current (Ih) in a zebrafish mutant with a slow heart rate. PNAS 94:4554–59
    [Google Scholar]
  169. 169.
    Nemtsas P, Wettwer E, Christ T, Weidinger G, Ravens U. 2010. Adult zebrafish heart as a model for human heart? An electrophysiological study. J. Mol. Cell. Cardiol. 48:161–71
    [Google Scholar]
  170. 170.
    Chen JN, Haffter P, Odenthal J, Vogelsang E, Brand M et al. 1996. Mutations affecting the cardiovascular system and other internal organs in zebrafish. Development 123:293–302
    [Google Scholar]
  171. 171.
    Shah RR, Hondeghem LM. 2005. Refining detection of drug-induced proarrhythmia: QT interval and TRIaD. Heart Rhythm 2:758–72
    [Google Scholar]
  172. 172.
    Arking DE, Pfeufer A, Post W, Kao WH, Newton-Cheh C et al. 2006. A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat. Genet. 38:644–51
    [Google Scholar]
  173. 173.
    Crotti L, Monti MC, Insolia R, Peljto A, Goosen A et al. 2009. NOS1AP is a genetic modifier of the long-QT syndrome. Circulation 120:1657–63
    [Google Scholar]
  174. 174.
    Peal DS, Mills RW, Lynch SN, Mosley JM, Lim E et al. 2011. Novel chemical suppressors of long QT syndrome identified by an in vivo functional screen. Circulation 123:23–30
    [Google Scholar]
  175. 175.
    Barazandeh M, Kriti D, Nislow C, Giaever G 2022. The cellular response to drug perturbation is limited: comparison of large-scale chemogenomic fitness signatures. BMC Genom. 23:197
    [Google Scholar]
  176. 176.
    Shin JT, Pomerantsev EV, Mably JD, MacRae CA. 2010. High-resolution cardiovascular function confirms functional orthology of myocardial contractility pathways in zebrafish. Physiol. Genom. 42:300–9
    [Google Scholar]
  177. 177.
    Cheng H, Kari G, Dicker AP, Rodeck U, Koch WJ, Force T. 2011. A novel preclinical strategy for identifying cardiotoxic kinase inhibitors and mechanisms of cardiotoxicity. Circ. Res. 109:1401–9
    [Google Scholar]
  178. 178.
    Tucker B, Lardelli M. 2007. A rapid apoptosis assay measuring relative acridine orange fluorescence in zebrafish embryos. Zebrafish 4:113–16
    [Google Scholar]
  179. 179.
    Jordi J, Guggiana-Nilo D, Bolton AD, Prabha S, Ballotti K et al. 2018. High-throughput screening for selective appetite modulators: a multibehavioral and translational drug discovery strategy. Sci. Adv. 4:eaav1966
    [Google Scholar]
  180. 180.
    Wolman MA, Jain RA, Liss L, Granato M. 2011. Chemical modulation of memory formation in larval zebrafish. PNAS 108:15468–73
    [Google Scholar]
  181. 181.
    Muto A, Kawakami K. 2011. Imaging functional neural circuits in zebrafish with a new GCaMP and the Gal4FF-UAS system. Commun. Integr. Biol. 4:566–68
    [Google Scholar]
  182. 182.
    Muto A, Ohkura M, Abe G, Nakai J, Kawakami K. 2013. Real-time visualization of neuronal activity during perception. Curr. Biol. 23:307–11
    [Google Scholar]
  183. 183.
    Thyme SB, Pieper LM, Li EH, Pandey S, Wang Y et al. 2019. Phenotypic landscape of schizophrenia-associated genes defines candidates and their shared functions. Cell 177:478–91.e20
    [Google Scholar]
  184. 184.
    Amatruda JF, Shepard JL, Stern HM, Zon LI. 2002. Zebrafish as a cancer model system. Cancer Cell 1:229–31
    [Google Scholar]
  185. 185.
    Spitsbergen JM, Tsai HW, Reddy A, Miller T, Arbogast D et al. 2000. Neoplasia in zebrafish (Danio rerio) treated with 7,12-dimethylbenz[a]anthracene by two exposure routes at different developmental stages. Toxicol. Pathol. 28:705–15
    [Google Scholar]
  186. 186.
    Taylor AM, Zon LI. 2009. Zebrafish tumor assays: the state of transplantation. Zebrafish 6:339–46
    [Google Scholar]
  187. 187.
    Lin HD, Tseng YK, Yuh CH, Chen SC. 2022. Low concentrations of 4-ABP promote liver carcinogenesis in human liver cells and a zebrafish model. J. Hazard. Mater. 423:126954
    [Google Scholar]
  188. 188.
    Chen S, Dang Y, Gong Z, Letcher RJ, Liu C. 2019. Progression of liver tumor was promoted by tris(1,3-dichloro-2-propyl) phosphate through the induction of inflammatory responses in krasV12 transgenic zebrafish. Environ. Pollut. 255:113315
    [Google Scholar]
  189. 189.
    Baggiolini A, Callahan SJ, Montal E, Weiss JM, Trieu T et al. 2021. Developmental chromatin programs determine oncogenic competence in melanoma. Science 373:eabc1048
    [Google Scholar]
  190. 190.
    Baron M, Tagore M, Hunter MV, Kim IS, Moncada R et al. 2020. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst 11:536–46.e7
    [Google Scholar]
  191. 191.
    Koide K, Song F, de Groh ED, Garner AL, Mitchell VD et al. 2008. Scalable and concise synthesis of dichlorofluorescein derivatives displaying tissue permeation in live zebrafish embryos. ChemBioChem 9:214–18
    [Google Scholar]
  192. 192.
    Burns CG, MacRae CA. 2006. Purification of hearts from zebrafish embryos. Biotechniques 40:274
    [Google Scholar]
  193. 193.
    Goldstone JV, McArthur AG, Kubota A, Zanette J, Parente T et al. 2010. Identification and developmental expression of the full complement of Cytochrome P450 genes in zebrafish. BMC Genom 11:643
    [Google Scholar]
  194. 194.
    Grimes AC, Stadt HA, Shepherd IT, Kirby ML. 2006. Solving an enigma: arterial pole development in the zebrafish heart. Dev. Biol. 290:265–76
    [Google Scholar]
  195. 195.
    Busch W, Duis K, Fenske M, Maack G, Legler J et al. 2011. The zebrafish embryo model in toxicology and teratology, September 2–3, 2010, Karlsruhe, Germany. Reprod. Toxicol. 31:585–88
    [Google Scholar]
  196. 196.
    Green AJ, Mohlenkamp MJ, Das J, Chaudhari M, Truong L et al. 2021. Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology. PLOS Comput. Biol. 17:e1009135
    [Google Scholar]
  197. 197.
    Truong L, Marvel S, Reif DM, Thomas DG, Pande P et al. 2020. The multi-dimensional embryonic zebrafish platform predicts flame retardant bioactivity. Reprod. Toxicol. 96:359–69
    [Google Scholar]
  198. 198.
    Sandin S, Lichtenstein P, Kuja-Halkola R, Larsson H, Hultman CM, Reichenberg A. 2014. The familial risk of autism. JAMA 311:1770–77
    [Google Scholar]
  199. 199.
    Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP et al. 2014. Most genetic risk for autism resides with common variation. Nat. Genet. 46:881–85
    [Google Scholar]
  200. 200.
    Geng Y, Zhang T, Godar SC, Pluimer BR, Harrison DL et al. 2021. Top2a promotes the development of social behavior via PRC2 and H3K27me3. bioRxiv 2021.09.20.461107v2. https://www.biorxiv.org/content/10.1101/2021.09.20.461107v2
  201. 201.
    Schuttler A, Jakobs G, Fix JM, Krauss M, Kruger J et al. 2021. Transcriptome-wide prediction and measurement of combined effects induced by chemical mixture exposure in zebrafish embryos. Environ. Health Perspect. 129:47006
    [Google Scholar]
  202. 202.
    Wang A, Zhang Q, Han Y, Megason S, Hormoz S et al. 2022. A novel deep learning-based 3D cell segmentation framework for future image-based disease detection. Sci. Rep. 12:342
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-051421-105617
Loading
/content/journals/10.1146/annurev-pharmtox-051421-105617
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error