Vivien A. Sheehan, Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Emory University School of Medicine, 1760 Haygood Dr Rm E372, Atlanta, GA 30322, USA.

Email: [email protected]

Search for more papers by this author

Varsha Bhat,

Varsha Bhat

orcid.org/0000-0002-1462-1591

Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA

Search for more papers by this author

Alka A. Potdar,

Alka A. Potdar

Pfizer Inc, South San Francisco, California, USA

Search for more papers by this author

G. Karen Yu,

G. Karen Yu

Pfizer Inc, South San Francisco, California, USA

Search for more papers by this author

Greg Gibson,

Greg Gibson

Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA

Search for more papers by this author

Vivien A. Sheehan,

Corresponding Author

Vivien A. Sheehan

[email protected]

orcid.org/0000-0003-2837-2255

Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, USA

Correspondence

Vivien A. Sheehan, Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Emory University School of Medicine, 1760 Haygood Dr Rm E372, Atlanta, GA 30322, USA.

Email: [email protected]

Search for more papers by this author

First published: 17 November 2024

https://doi.org/10.1111/bjh.19839

Share a link

Email
Wechat
Bluesky

Summary

Hydroxycarbamide (HC) is the most widely used therapeutic for individuals with sickle cell disease (SCD, including sickle cell anemia and other forms of the disease). HC's clinical benefits are primarily associated with its ability to induce foetal haemoglobin (HbF); this limited view of HC's therapeutic potential may lead to its discontinuation when a modest amount of HbF is induced. A better understanding of the HbF-independent effects of HC on genes and pathways relevant to SCD pathophysiology is therefore needed. In this study, we performed bulk RNA-Seq on whole blood samples collected from a cohort of 25 paediatric patients with SCD to identify genes and pathways that are affected by treatment with HC. At the maximum tolerated dose (MTD) of HC, patients showed altered expression levels of several genes and biological pathways. Pathways related to haeme metabolism, interferon-alpha response, and interferon-gamma response were significantly downregulated at HC MTD relative to the matched pre-HC samples. Pathways linked with IL2-STAT5 signalling and TNFα signalling via NF-Kβ were observed to be up-regulated at HC MTD. These results illustrate the range of effects exerted by HC during therapy for SCD and pave the way for an improved understanding of the HbF induction-independent benefits of HC.

Graphical Abstract

Hydroxycarbamide (HC) is the most widely used therapeutic for individuals with sickle cell disease (SCD). HC's benefits are primarily associated with its ability to induce foetal haemoglobin (HbF); this limited view of HC's therapeutic potential may lead to its discontinuation when a modest amount of HbF is induced. Here, we performed bulk RNA-Seq on whole blood samples collected from 25 paediatric patients with SCD to identify genes and pathways affected by treatment with HC. Pathways related to haeme metabolism, interferon-gamma response, and interferon-alpha response were significantly downregulated at HC MTD relative to the matched pre-HC samples. Pathways linked with IL2-STAT5 signalling and TNFα signalling via NF-Kβ were observed to be up-regulated at HC MTD. These results pave the way for an improved understanding of the HbF induction-independent benefits of HC.

CONFLICT OF INTEREST STATEMENT

Vivien A. Sheehan receives research funding from Beam Therapeutics, Pfizer, Novartis, and Agios. Alka A. Potdar and G. Karen Yu are former employees of Pfizer.

Open Research

DATA AVAILABILITY STATEMENT

The bulk RNA-Seq data generated for this study is available at GEO under accession number GSE254951.

Supporting Information

REFERENCES

1 Data & statistics on sickle cell disease. CDC [Internet] [cited 2023 Apr 4]. Available from: https://www.cdc.gov/ncbddd/sicklecell/data.html
Google Scholar
2 Adopting consensus text, general assembly urges member states, United Nations system to raise awareness of sickle–cell anaemia on 19 June each year. UN Press [Internet] [Cited 2023 Apr 4]. Available from: https://press.un.org/en/2008/ga10803.doc.htm
Google Scholar
3Grosse SD, Odame I, Atrash HK, Amendah DD, Piel FB, Williams TN. Sickle cell disease in Africa: a neglected cause of early childhood mortality. Am J Prev Med. 2011; 41(6):S398.
10.1016/j.amepre.2011.09.013
PubMed Web of Science® Google Scholar
4Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, et al. Sickle cell disease. Nat Rev Dis Primers. 2018; 4:10810.
10.1038/nrdp.2018.10
Google Scholar
5McGann PT, Ware RE. Hydroxyurea therapy for sickle cell anemia. Expert Opin Drug Saf. 2015; 14(11): 1749–1758.
10.1517/14740338.2015.1088827
CAS PubMed Web of Science® Google Scholar
6Nevitt SJ, Jones AP, Howard J. Hydroxyurea (hydroxycarbamide) for sickle cell disease. Cochrane Database Syst Rev. 2017; 4(4):CD002202.
PubMed Web of Science® Google Scholar
7Pule GD, Mowla S, Novitzky N, Wiysonge CS, Wonkam A. A systematic review of known mechanisms of hydroxyurea-induced Foetal Haemoglobin for treatment of sickle cell disease. Expert Rev Hematol. 2015; 8(5): 669–679.
10.1586/17474086.2015.1078235
CAS PubMed Web of Science® Google Scholar
8Flanagan JM, Steward S, Howard TA, Mortier NA, Kimble AC, Aygun B, et al. Microarray analysis of erythroid gene expression in sickle cell anemia patients treated with hydroxyurea. Blood. 2010; 116(21):1623.
10.1182/blood.V116.21.1623.1623
PubMed Google Scholar
9Steinberg MH, Chui DHK, Dover GJ, Sebastiani P, Alsultan A. Fetal hemoglobin in sickle cell anemia: a glass half full? Blood. 2014; 123: 481–485.
10.1182/blood-2013-09-528067
CAS PubMed Web of Science® Google Scholar
10Green NS, Barral S. Emerging science of hydroxyurea therapy for pediatric sickle cell disease. Pediatr Res. 2014; 75: 196–204.
10.1038/pr.2013.227
CAS PubMed Web of Science® Google Scholar
11Andrews S, FastQC A. Quality control tool for high throughput sequence data [Internet]. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Google Scholar
12Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29(1): 15–21.
10.1093/bioinformatics/bts635
CAS PubMed Web of Science® Google Scholar
13Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, et al. GENCODE 2021. Nucleic Acids Res. 2021; 49(D1): D916–D923.
10.1093/nar/gkaa1087
CAS PubMed Web of Science® Google Scholar
14Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139–140.
10.1093/bioinformatics/btp616
CAS PubMed Web of Science® Google Scholar
15Li J, Bushel PR, Chu TM, Wolfinger RD. Principal variance components analysis: estimating batch effects in microarray gene expression data. In: A Scherer, editor. Batch effects and noise in microarray experiments: Sources and solutions. Chichester, United Kingdom: John Wiley & Sons, Ltd; 2009. p. 141–154.
10.1002/9780470685983.ch12
Google Scholar
16Mecham BH, Nelson PS, Storey JD. Supervised normalization of microarrays. Bioinformatics. 2010; 26(10): 1308–1315.
10.1093/bioinformatics/btq118
CAS PubMed Web of Science® Google Scholar
17Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7):e47.
10.1093/nar/gkv007
CAS PubMed Web of Science® Google Scholar
18Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021;060012. https://doi.org/10.1101/060012v3
10.1101/060012v3
Google Scholar
19Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005; 102(43): 15545–15550. https://doi.org/10.1073/pnas.0506580102
10.1073/pnas.0506580102
CAS PubMed Web of Science® Google Scholar
20Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015; 1(6): 417–425.
10.1016/j.cels.2015.12.004
CAS PubMed Web of Science® Google Scholar
21Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team. nlme: Linear and nonlinear mixed effects models. R-Project; 2022. https://cran.r-project.org/package=nlme
Google Scholar
22Blighe K, Lun A. PCAtools: Everything principal components analysis. 2023. https://bioconductor.org/packages/PCAtools
Google Scholar
23Marcus SJ, Ware RE. Physiologic decline in fetal hemoglobin parameters in infants with sickle cell disease: implications for pharmacological intervention. J Pediatr Hematol Oncol. 1999; 21(5): 407–411.
10.1097/00043426-199909000-00013
CAS PubMed Web of Science® Google Scholar
24Yu G, Wang LG, Han Y, He QY. ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 2012; 16(5): 284–287.
10.1089/omi.2011.0118
CAS PubMed Web of Science® Google Scholar
25Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovations. 2021; 2(3):100141.
CAS Google Scholar
26Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022; 50(D1): D687–D692.
10.1093/nar/gkab1028
CAS PubMed Web of Science® Google Scholar
27Peslak SA, Khandros E, Huang P, Kihiu M, Abdulmalik O, Keller CA, et al. Protein phosphatase 6C (PPP6C) loss significantly raises fetal hemoglobin levels and reduces cell sickling. Blood. 2021; 138:2031.
10.1182/blood-2021-147275
PubMed Google Scholar
28Jiang J, Best S, Menzel S, Silver N, Lai MI, Surdulescu GL, et al. cMYB is involved in the regulation of fetal hemoglobin production in adults. Blood. 2006; 108(3): 1077–1083.
10.1182/blood-2006-01-008912
CAS PubMed Web of Science® Google Scholar
29Cato LD, Li R, Lu HY, Yu F, Wissman M, Mkumbe BS, et al. Genetic regulation of fetal hemoglobin across global populations. medRxiv. 2023;30.
Google Scholar
30Wang M, Tang DC, Liu W, Chin K, Zhu JG, Fibach E, et al. Hydroxyurea exerts bi-modal dose-dependent effects on erythropoiesis in human cultured erythroid cells via distinct pathways. Br J Haematol. 2002; 119(4): 1098–1105.
10.1046/j.1365-2141.2002.03958.x
CAS PubMed Web of Science® Google Scholar
31Kato H, Itoh-Nakadai A, Matsumoto M, Ishii Y, Watanabe-Matsui M, Ikeda M, et al. Infection perturbs Bach2- and Bach1-dependent erythroid lineage ‘choice’ to cause anemia. Nat Immunol. 2018; 19(10): 1059–1070.
10.1038/s41590-018-0202-3
CAS PubMed Google Scholar
32Liu Y, Pal M, Bao W, Shi PA, Lobo CA, An X, et al. Type I interferon is induced by hemolysis and drives antibody-mediated erythrophagocytosis in sickle cell disease. Blood. 2021; 138(13): 1162–1171.
10.1182/blood.2021011629
CAS PubMed Web of Science® Google Scholar
33Pathare A, Al Kindi S, Alnaqdy AA, Daar S, Knox-Macaulay H, Dennison D. Cytokine profile of sickle cell disease in Oman. Am J Hematol. 2004; 77(4): 323–328.
10.1002/ajh.20196
CAS PubMed Web of Science® Google Scholar
34Kuvibidila S, Warrier RP, Haynes J, Baliga SB. Hydroxyurea and zileuton differentially modulate cell proliferation and Interleukin-2 secretion by murine spleen cells: possible implication on the immune function and risk of pain crisis in patients with sickle cell disease. Ochsner J. 2015; 15(3): 241–247.
PubMed Google Scholar
35Wu ZH, Miyamoto S. Induction of a pro-apoptotic ATM–NF-κB pathway and its repression by ATR in response to replication stress. EMBO J. 2008; 27(14): 1963–1973.
10.1038/emboj.2008.127
CAS PubMed Web of Science® Google Scholar
36Recaldin T, Fear DJ. Transcription factors regulating B cell fate in the germinal centre. Clin Exp Immunol. 2016; 183(1): 65–75.
10.1111/cei.12702
CAS PubMed Web of Science® Google Scholar
37Xu J, Sankaran VG, Ni M, Menne TF, Puram RV, Kim W, et al. Transcriptional silencing of γ-globin by BCL11A involves long-range interactions and cooperation with SOX6. Genes Dev. 2010; 24(8): 783–798.
10.1101/gad.1897310
CAS PubMed Web of Science® Google Scholar
38Gbotosho OT, Kapetanaki MG, Kato GJ. The worst things in life are free: the role of free heme in sickle cell disease. Front Immunol. 2021; 11:561917.
10.3389/fimmu.2020.561917
PubMed Web of Science® Google Scholar
39Wiatr M, Merle NS, Boudhabhay I, Poillerat V, Rossini S, Lecerf M, et al. Anti-inflammatory activity of intravenous immunoglobulin through scavenging of heme. Mol Immunol. 2019; 111: 205–208.
10.1016/j.molimm.2019.04.020
CAS PubMed Google Scholar

Volume206, Issue2

February 2025

Pages 713-720

Impact of hydroxycarbamide treatment on the whole-blood transcriptome in sickle cell disease

Summary

Graphical Abstract

CONFLICT OF INTEREST STATEMENT

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

REFERENCES

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Impact of hydroxycarbamide treatment on the whole-blood transcriptome in sickle cell disease

Summary

Graphical Abstract

CONFLICT OF INTEREST STATEMENT

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

REFERENCES

References

Related

Information