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Scientific reports2023; 13(1); 16261; doi: 10.1038/s41598-023-43368-4

Single-cell transcriptomics delineates the immune cell landscape in equine lower airways and reveals upregulation of FKBP5 in horses with asthma.

Abstract: Equine asthma (EA) is a heterogenous, complex disease, with a significant negative impact on horse welfare and performance. EA and human asthma share fundamental similarities, making EA a useful model for studying the disease. One relevant sample type for investigating chronic lung inflammation is bronchoalveolar lavage fluid (BALF), which provides a snapshot of the immune cells present in the alveolar space. To investigate the immune cell landscape of the respiratory tract in horses with mild-to-moderate equine asthma (mEA) and healthy controls, single-cell RNA sequencing was conducted on equine BALF cells. We characterized the major immune cell populations present in equine BALF, as well as subtypes thereof. Interestingly, the most significantly upregulated gene discovered in cases of mEA was FKBP5, a chaperone protein involved in regulating the activity of the glucocorticoid receptor.
Publication Date: 2023-09-27 PubMed ID: 37758813PubMed Central: PMC10533524DOI: 10.1038/s41598-023-43368-4Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article explores asthmatic horses’ immune systems by using single-cell sequencing on cells found in bronchoalveolar lavage fluid, and identifies an upregulated gene, FKBP5, involved in the regulation of glucocorticoid receptors.

Examining Equine Asthma

  • The primary focus of this research is Equine Asthma (EA), a disease that creates a negative impact on horse welfare and performance. Notably, EA shares similarities with human asthma.
  • The study uses EA as a model to understand more about asthma and chronic lung inflammation. For analysis, the researchers use a sample type called bronchoalveolar lavage fluid (BALF), commonly used in investigating chronic lung inflammation.

Use of Single-cell Sequencing

  • The study applies a technique called single-cell RNA sequencing on equine BALF cells. Single-cell sequencing offers a high resolution and allows researchers to understand the activity and differences of individual cells unlike bulk sequencing where details about individual cells are lost.

Investigation on Immune Cell Landscape

  • Using the single-cell sequencing, this research attempts to explore immune cell landscape in respiratory tract of horses, particularly those with mild-to-moderate equine asthma (mEA) and healthy controls.
  • The researchers identified and characterized the major immune cell populations as well as their subtypes present in the BALF sample.

Discovery of the FKBP5 Gene

  • Among the cell populations and subtypes they examined, scientists found that a chaperone protein called FKBP5 was the most upregulated gene in cases of mEA. Chaperone proteins have numerous roles, with FKBP5 specifically known for regulating the activity of the glucocorticoid receptor.
  • Glucocorticoids are a type of steroid hormone that plays a key role in the immune response, which could be an important piece of the asthma response puzzle in equine as well as potentially providing insight into human asthma response.

Cite This Article

APA
Riihimäki M, Fegraeus K, Nordlund J, Waern I, Wernersson S, Akula S, Hellman L, Raine A. (2023). Single-cell transcriptomics delineates the immune cell landscape in equine lower airways and reveals upregulation of FKBP5 in horses with asthma. Sci Rep, 13(1), 16261. https://doi.org/10.1038/s41598-023-43368-4

Publication

ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 13
Issue: 1
Pages: 16261
PII: 16261

Researcher Affiliations

Riihimäki, Miia
  • Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Fegraeus, Kim
  • Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Nordlund, Jessica
  • Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Waern, Ida
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Wernersson, Sara
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Akula, Srinivas
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Hellman, Lars
  • Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Raine, Amanda
  • Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. Amanda.Raine@medsci.uu.se.

MeSH Terms

  • Animals
  • Asthma / genetics
  • Asthma / veterinary
  • Bronchoalveolar Lavage Fluid
  • Horse Diseases / genetics
  • Horses
  • Respiratory System
  • Transcriptome
  • Up-Regulation

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 92 references
  1. Bond S. Equine asthma: Integrative biologic relevance of a recently proposed nomenclature. J. Vet. Intern. Med. 2018;32:2088–2098.
    pmc: PMC6271326pubmed: 30294851
  2. Ramseyer A. Effects of genetic and environmental factors on chronic lower airway disease in horses. J. Vet. Intern. Med. 2007;21:149–156.
    pubmed: 17338163
  3. Holcombe SJ. Stabling is associated with airway inflammation in young Arabian horses. Equine Vet. J. 2010;33:244–249.
    pubmed: 11352345
  4. Couetil L. Equine asthma: Current understanding and future directions. Front. Vet. Sci. 2020;7:450.
    pmc: PMC7438831pubmed: 32903600
  5. Rettmer H, Hoffman AM, Lanz S, Oertly M, Gerber V. Owner-reported coughing and nasal discharge are associated with clinical findings, arterial oxygen tension, mucus score and bronchoprovocation in horses with recurrent airway obstruction in a field setting: Coughing and nasal discharge in equine recurrent airway obstruction. Equine Vet. J. 2015;47:291–295.
    pubmed: 24761754
  6. Robinson NE. Coughing, mucus accumulation, airway obstruction, and airway inflammation in control horses and horses affected with recurrent airway obstruction. Am. J. Vet. Res. 2003;64:550–557.
    pubmed: 12755293
  7. Bullone M, Lavoie J-P. Asthma, “of horses and men”—How can equine heaves help us better understand human asthma immunopathology and its functional consequences?. Mol. Immunol. 2015;66:97–105.
    pubmed: 25547716
  8. Couëtil LL. Inflammatory airway disease of horses—Revised consensus statement. J. Vet. Intern. Med. 2016;30:503–515.
    pmc: PMC4913592pubmed: 26806374
  9. Kinnison T. Mild-moderate equine asthma: A scoping review of evidence supporting the consensus definition. Vet. J. 2022;286:105865.
    pubmed: 35817389
  10. Kuruvilla ME, Lee FE-H, Lee GB. Understanding asthma phenotypes, endotypes, and mechanisms of disease. Clin. Rev. Allerg. Immunol. 2019;56:219–233.
    pmc: PMC6411459pubmed: 30206782
  11. Cian F, Monti P, Durham A. Cytology of the lower respiratory tract in horses: An updated review. Equine Vet. Educ. 2015;27:544–553.
  12. Couetil LL, Thompson CA. Airway diagnostics. Vet. Clin. N. Am. Equine Pract. 2020;36:87–103.
    pubmed: 32145836
  13. Pacholewska A, Kraft M, Gerber V, Jagannathan V. Differential expression of serum MicroRNAs supports CD4+ T cell differentiation into Th2/Th17 cells in severe equine asthma. Genes 2017;8:383.
    pmc: PMC5748701pubmed: 29231896
  14. Pacholewska A. Impaired cell cycle regulation in a natural equine model of asthma. PLoS ONE 2015;10:e0136103.
    pmc: PMC4546272pubmed: 26292153
  15. Tessier L. Impaired response of the bronchial epithelium to inflammation characterizes severe equine asthma. BMC Genomics 2017;18:708.
    pmc: PMC5591550pubmed: 28886691
  16. Tessier L, Côté O, Bienzle D. Sequence variant analysis of RNA sequences in severe equine asthma. PeerJ 2018;6:e5759.
    pmc: PMC6186407pubmed: 30324028
  17. Tessier L. Gene set enrichment analysis of the bronchial epithelium implicates contribution of cell cycle and tissue repair processes in equine asthma. Sci. Rep. 2018;8:16408.
    pmc: PMC6219531pubmed: 30401798
  18. Sage SE. Single-cell gene expression analysis of cryopreserved equine bronchoalveolar cells. Front. Immunol. 2022;13:929922.
    pmc: PMC9467276pubmed: 36105804
  19. Harman RM. Single-cell RNA sequencing of equine mesenchymal stromal cells from primary donor-matched tissue sources reveals functional heterogeneity in immune modulation and cell motility. Stem Cell Res. Ther. 2020;11:524.
    pmc: PMC7716481pubmed: 33276815
  20. Patel RS, Tomlinson JE, Divers TJ, Van de Walle GR, Rosenberg BR. Single-cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells. BMC Biol. 2021;19:13.
    pmc: PMC7820527pubmed: 33482825
  21. Macosko EZ. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015;161:1202–1214.
    pmc: PMC4481139pubmed: 26000488
  22. McInnes L, Healy J, Saul N, Großberger LUMAP. Uniform manifold approximation and projection. JOSS 2018;3:861.
  23. Lehtonen A. Gene expression profiling during differentiation of human monocytes to macrophages or dendritic cells. J. Leukoc. Biol. 2007;82:710–720.
    pubmed: 17595377
  24. Li J, Zhou L, Ouyang X, He P. Transcription factor-7-like-2 (TCF7L2) in atherosclerosis: A potential biomarker and therapeutic target. Front. Cardiovasc. Med. 2021;8:701279.
    pmc: PMC8459927pubmed: 34568447
  25. Burel JG. The challenge of distinguishing cell–cell complexes from singlet cells in non-imaging flow cytometry and single-cell sorting. Cytometry 2020;97:1127–1135.
    pmc: PMC7666012pubmed: 32400942
  26. Burel JG. Circulating T cell-monocyte complexes are markers of immune perturbations. eLife 2019;8:e46045.
    pmc: PMC6592685pubmed: 31237234
  27. Schraml BU, Reis e Sousa C. Defining dendritic cells. Curr. Opin. Immunol. 2015;32:13–20.
    pubmed: 25553392
  28. Liu J, Zhang X, Cheng Y, Cao X. Dendritic cell migration in inflammation and immunity. Cell. Mol. Immunol. 2021;18:2461–2471.
    pmc: PMC8298985pubmed: 34302064
  29. Patente TA. Human Dendritic Cells: Their heterogeneity and clinical application potential in cancer immunotherapy. Front. Immunol. 2019;9:3176.
    pmc: PMC6348254pubmed: 30719026
  30. Malaguarnera L, Marsullo A, Zorena K, Musumeci G, Di Rosa M. Vitamin D 3 regulates LAMP3 expression in monocyte derived dendritic cells. Cell. Immunol. 2017;311:13–21.
    pubmed: 27697285
  31. Choi H, Song H, Jung YW. The roles of CCR7 for the homing of memory CD8+ T cells into their survival niches. Immune Netw. 2020;20:e20.
    pmc: PMC7327150pubmed: 32655968
  32. Schoggins JW. Interferon-stimulated genes: What do they all do?. Annu. Rev. Virol. 2019;6:567–584.
    pubmed: 31283436
  33. Mould KJ, Jackson ND, Henson PM, Seibold M, Janssen WJ. Single cell RNA sequencing identifies unique inflammatory airspace macrophage subsets. JCI Insight 2019;4:e126556.
    pmc: PMC6483508pubmed: 30721157
  34. Wang L. Single-cell transcriptomic analysis reveals the immune landscape of lung in steroid-resistant asthma exacerbation. Proc. Natl. Acad. Sci. USA 2021;118:e2005590118.
    pmc: PMC7812791pubmed: 33397719
  35. Gibbings SL. Transcriptome analysis highlights the conserved difference between embryonic and postnatal-derived alveolar macrophages. Blood 2015;126:1357–1366.
    pmc: PMC4566811pubmed: 26232173
  36. Irani AA, Schechter NM, Craig SS, DeBlois G, Schwartz LB. Two types of human mast cells that have distinct neutral protease compositions. Proc. Natl. Acad. Sci. USA 1986;83:4464–4468.
    pmc: PMC323754pubmed: 3520574
  37. Caballero-Franco C, Kissler S. The autoimmunity-associated gene RGS1 affects the frequency of T follicular helper cells. Genes Immun. 2016;17:228–238.
    pmc: PMC4892947pubmed: 27029527
  38. Newton R. Regulators of G-protein signaling as asthma therapy?. Am. J. Respir. Cell. Mol. Biol. 2018;58:7–9.
    pubmed: 29286861
  39. Wang K. Locally organised and activated Fth1hi neutrophils aggravate inflammation of acute lung injury in an IL-10-dependent manner. Nat. Commun. 2022;13:7703.
    pmc: PMC9745290pubmed: 36513690
  40. Shenoy AR. GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals. Science 2012;336:481–485.
    pubmed: 22461501
  41. Xu J. Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls. Front. Immunol. 2022;13:970287.
    pmc: PMC9709423pubmed: 36466858
  42. Naessens T. Human lung conventional dendritic cells orchestrate lymphoid neogenesis during chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2020;202:535–548.
    pubmed: 32255375
  43. Finak G. MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 2015;16:278.
    pmc: PMC4676162pubmed: 26653891
  44. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    pmc: PMC4302049pubmed: 25516281
  45. Coleman JM. Epithelial eotaxin-2 and eotaxin-3 expression: Relation to asthma severity, luminal eosinophilia and age at onset. Thorax 2012;67:1061–1066.
    pmc: PMC3652589pubmed: 23015684
  46. Ali MdK. Crucial role for lung iron level and regulation in the pathogenesis and severity of asthma. Eur. Respir. J. 2020;55:1901340.
    pubmed: 32184317
  47. Do AN. Network analysis reveals causal key driver genes of severe asthma in children. J. Allergy Clin. Immunol. 2019;143:AB186.
  48. Massoud AH. An asthma-associated IL4R variant exacerbates airway inflammation by promoting conversion of regulatory T cells to TH17-like cells. Nat. Med. 2016;22:1013–1022.
    pmc: PMC5014738pubmed: 27479084
  49. Ellis KL, Contino EK, Nout-Lomas YS. Poor performance in the horse: Diagnosing the non-orthopaedic causes. Equine Vet. Educ. .
    doi: 10.1111/eve.13712google scholar: lookup
  50. Simões J, Batista M, Tilley P. The immune mechanisms of severe equine asthma—Current understanding and what is missing. Animals 2022;12:744.
    pmc: PMC8944511pubmed: 35327141
  51. Tallmadge RL, Wang M, Sun Q, Felippe MJB. Transcriptome analysis of immune genes in peripheral blood mononuclear cells of young foals and adult horses. PLoS ONE 2018;13:e0202646.
    pmc: PMC6124769pubmed: 30183726
  52. Manika K, Domvri K, Kyriazis G, Kontakiotis T, Papakosta D. BALF and BLOOD NK-cells in different stages of pulmonary sarcoidosis. Sarcoidosis Vasculitis Diffuse Lung Dis. 2022;38:e2021039.
    pmc: PMC8787376pubmed: 35115746
  53. Bhakta NR. IFN-stimulated gene expression, type 2 inflammation, and endoplasmic reticulum stress in asthma. Am. J. Respir. Crit. Care Med. 2018;197:313–324.
    pmc: PMC5811952pubmed: 29064281
  54. Raundhal M. High IFN-γ and low SLPI mark severe asthma in mice and humans. J. Clin. Invest. 2015;125:3037–3050.
    pmc: PMC4563754pubmed: 26121748
  55. Bain CC, MacDonald AS. The impact of the lung environment on macrophage development, activation and function: Diversity in the face of adversity. Mucosal Immunol. 2022;15:223–234.
    pmc: PMC8749355pubmed: 35017701
  56. Bharat A. Flow cytometry reveals similarities between lung macrophages in humans and mice. Am. J. Respir. Cell Mol. Biol. 2016;54:147–149.
    pmc: PMC4742931pubmed: 26274047
  57. Lara S. The human monocyte—A circulating sensor of infection and a potent and rapid inducer of inflammation. IJMS 2022;23:3890.
    pmc: PMC8999117pubmed: 35409250
  58. Paivandy A. Quantitative in-depth transcriptome analysis implicates peritoneal macrophages as important players in the complement and coagulation systems. IJMS 2022;23:1185.
    pmc: PMC8835655pubmed: 35163105
  59. Evren E, Ringqvist E, Willinger T. Origin and ontogeny of lung macrophages: from mice to humans. Immunology 2020;160:126–138.
    pmc: PMC7218405pubmed: 31715003
  60. Ripoll VM, Irvine KM, Ravasi T, Sweet MJ, Hume DA. Gpnmb is induced in macrophages by IFN-γ and lipopolysaccharide and acts as a feedback regulator of proinflammatory responses. J. Immunol. 2007;178:6557–6566.
    pubmed: 17475886
  61. Yaseen H. Galectin-1 facilitates macrophage reprogramming and resolution of inflammation through IFN-β. Front. Pharmacol. 2020;11:901.
    pmc: PMC7311768pubmed: 32625094
  62. Bloom JD. Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments. PeerJ 2018;6:e5578.
    pmc: PMC6126471pubmed: 30202659
  63. Guerriero JL. Macrophages. International Review of Cell and Molecular Biology 2019;342:73–93.
    pubmed: 30635094
  64. Aegerter H, Lambrecht BN, Jakubzick CV. Biology of lung macrophages in health and disease. Immunity 2022;55:1564–1580.
    pmc: PMC9533769pubmed: 36103853
  65. Akula S. Quantitative transcriptome analysis of purified equine mast cells identifies a dominant mucosal mast cell population with possible inflammatory functions in airways of asthmatic horses. IJMS 2022;23:13976.
    pmc: PMC9692376pubmed: 36430453
  66. Xie X. Single-cell transcriptome profiling reveals neutrophil heterogeneity in homeostasis and infection. Nat. Immunol. 2020;21:1119–1133.
    pmc: PMC7442692pubmed: 32719519
  67. Herteman N, Vargas A, Lavoie J-P. Characterization of circulating low-density neutrophils intrinsic properties in healthy and asthmatic horses. Sci. Rep. 2017;7:7743.
    pmc: PMC5552858pubmed: 28798364
  68. Hong Y. Single-cell transcriptome profiling reveals heterogeneous neutrophils with prognostic values in sepsis. iScience 2022;25:105301.
    pmc: PMC9593767pubmed: 36304125
  69. Davis KU, Sheats MK. Differential gene expression and Ingenuity Pathway Analysis of bronchoalveolar lavage cells from horses with mild/moderate neutrophilic or mastocytic inflammation on BAL cytology. Vet. Immunol. Immunopathol. 2021;234:110195.
    pmc: PMC8132494pubmed: 33588285
  70. Schiene-Fischer C, Yu C. Receptor accessory folding helper enzymes: The functional role of peptidyl prolyl cis/trans isomerases. FEBS Lett. 2001;495:1–6.
    pubmed: 11322937
  71. Kirschke E, Goswami D, Southworth D, Griffin PR, Agard DA. Glucocorticoid receptor function regulated by coordinated action of the Hsp90 and Hsp70 chaperone cycles. Cell 2014;157:1685–1697.
    pmc: PMC4087167pubmed: 24949977
  72. Grad I, Picard D. The glucocorticoid responses are shaped by molecular chaperones. Mol. Cell. Endocrinol. 2007;275:2–12.
    pubmed: 17628337
  73. Wochnik GM. FK506-binding proteins 51 and 52 differentially regulate dynein interaction and nuclear translocation of the glucocorticoid receptor in mammalian cells. J. Biol. Chem. 2005;280:4609–4616.
    pubmed: 15591061
  74. Westberry JM, Sadosky PW, Hubler TR, Gross KL, Scammell JG. Glucocorticoid resistance in squirrel monkeys results from a combination of a transcriptionally incompetent glucocorticoid receptor and overexpression of the glucocorticoid receptor co-chaperone FKBP51. J. Steroid Biochem. Mol. Biol. 2006;100:34–41.
    pubmed: 16723223
  75. Denny WB, Valentine DL, Reynolds PD, Smith DF, Scammell JG. Squirrel monkey immunophilin FKBP51 is a potent inhibitor of glucocorticoid receptor binding1. Endocrinology 2000;141:4107–4113.
    pubmed: 11089542
  76. Scammell JG, Denny WB, Valentine DL, Smith DF. Overexpression of the FK506-binding immunophilin FKBP51 is the common cause of glucocorticoid resistance in three new world primates. Gen. Comp. Endocrinol. 2001;124:152–165.
    pubmed: 11703081
  77. Panda L, Mabalirajan U. Recent updates on corticosteroid resistance in asthma. Emerg. Med. J. 2018;3:49–57.
  78. Thomson NC. Addressing corticosteroid insensitivity in adults with asthma. Expert Rev. Respir. Med. 2016;10:137–156.
    pubmed: 26678267
  79. Hirsch G, Lavoie-Lamoureux A, Beauchamp G, Lavoie J-P. Neutrophils are not less sensitive than other blood leukocytes to the genomic effects of glucocorticoids. PLoS ONE 2012;7:e44606.
    pmc: PMC3440353pubmed: 22984532
  80. Mainguy-Seers S, Lavoie J. Glucocorticoid treatment in horses with asthma: A narrative review. J. Vet. Intern. Med. 2021;35:2045–2057.
    pmc: PMC8295667pubmed: 34085342
  81. Ravensberg AJ. Eotaxin-2 and eotaxin-3 expression is associated with persistent eosinophilic bronchial inflammation in patients with asthma after allergen challenge. J. Allergy Clin. Immunol. 2005;115:779–785.
    pubmed: 15805998
  82. Komiya A. Concerted expression of eotaxin-1, eotaxin-2, and eotaxin-3 in human bronchial epithelial cells. Cell. Immunol. 2003;225:91–100.
    pubmed: 14698143
  83. Berkman N, Ohnona S, Chung FK, Breuer R. Eotaxin-3 but not eotaxin gene expression is upregulated in asthmatics 24 hours after allergen challenge. Am. J. Respir. Cell. Mol. Biol. 2001;24:682–687.
    pubmed: 11415932
  84. Scheicher ME. Eotaxin-2 in sputum cell culture to evaluate asthma inflammation. Eur. Respir. J. 2007;29:489–495.
    pubmed: 17079258
  85. Swinburne JE. A whole-genome scan for recurrent airway obstruction in Warmblood sport horses indicates two positional candidate regions. Mamm Genome 2009;20:504–515.
    pubmed: 19760324
  86. Ben-Yehuda C. Airway eosinophil accumulation and eotaxin-2/CCL24 expression following allergen challenge in BALB/c mice. Exp. Lung Res. 2008;34:467–479.
    pubmed: 18850374
  87. Salcher S. High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell 2022;40:1503–1520.e8.
    pmc: PMC9767679pubmed: 36368318
  88. Lun ATL. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20:63.
    pmc: PMC6431044pubmed: 30902100
  89. Korsunsky I. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 2019;16:1289–1296.
    pmc: PMC6884693pubmed: 31740819
  90. Becht E. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 2019;37:38–44.
    pubmed: 30531897
  91. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. S. Ser. B Methodol. 1995;57:289–300.
  92. Raudvere U. g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:191–198.
    pmc: PMC6602461pubmed: 31066453

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