Differential genes expression of immune tolerance induction in hemophilia A: an exploratory RNA-seq test from a Chinese hemophilia comprehensive care centre

Introduction

Hemophilia A (HA) is a bleeding disease that is inherited in an X-linked recessive manner, resulting from genetic mutations affecting either factor VIII (FVIII). Infusion of exogenous FVIII products is the fundamental choice for the treatment of HA. However, it is estimated that 20% to 30% of individuals with severe HA may develop neutralizing antibodies against exogenous FVIII (1,2), exacerbating bleeding, increasing mortality and disability rates, and significantly affecting the effectiveness of treatment (3). Therefore, the appearance of inhibitors is a significant complication and challenge in the replacement therapy for HA (3). Immune tolerance induction (ITI) remains the primary treatment approach for eliminating inhibitors in hemophilia patients. The overall success rate of ITI ranges from 50% to 80% (4-7), but different studies report varying success rates.

The efficacy of ITI is influenced by multiple factors, including race, type of F8 gene mutation, inhibitor titer, treatment regimen, and immune regulatory mechanisms (4,5,8). Exploring differently expressed genes before treatment in patients with different treatment responses can help predict outcomes. RNA sequencing (RNA-seq) is a method to accurately identify genes related to the phenotype of human specific diseases. It has a broad prospect for identifying specific gene maps related to detection, prognosis and chemical sensitivity of other diseases (9,10). Co-analysis can clarify the complex causes of human diseases and related regulatory mechanisms (11).

In this study, we divided patients into two groups based on whether ITI was successfully implemented. Using RNA-seq technology, we systematically analyzed the transcriptional changes in peripheral blood cells before treatment in both groups. By comparing the differentially expressed genes (DEGs) between the two groups, we tried to explore predictive markers of ITI outcome. Then we also analyzed the transcriptional changes in peripheral blood cells after treatment in both groups with RNA-seq, trying to explore potential targets for realizing precision therapy.

Methods

Patient cohort and blood sample collection

This exploratory cohort study, conducted at a single center, involved children with HA who had high-titer inhibitors and took place from April 2019 to October 2020 at Beijing Children’s Hospital. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Review Committee of Beijing Children’s Hospital (No. 2017-k-49), and the informed consent form was acquired from the guardians before participation in the study.

The inclusion criteria were as follows: (I) patients with an established diagnosis of HA (12); (II) with high-titer inhibitors [titers of ≥5 Bethesda units/mL (BU/mL) on at least one occasion from our medical records]; (III) received ITI strategy and followed for more than 24 months, which could gain the final outcomes about ITI.

Patients were divided into two groups by ITI outcome. Patients in Success group achieved inhibitor elimination, while those in Failure group not.

Clinical data collection

Clinical and laboratory information for the patients was extracted from the medical records at the Hemophilia Comprehensive Care Center, which included baseline FVIII:C levels, age, inhibitor titer at the initiation of ITI, ITI treatment regimens, follow-up data, and outcomes of ITI.

RNA sequencing

Peripheral blood mononuclear cells from patients with HA were separated from 2 mL of EDTA-K2 anticoagulated venous blood through density gradient centrifugation. Total RNA was extracted using TRIzol reagent (Life Technologies, Carlsbad, CA, USA) for transcriptomics. RNA integrity was examined using a Fragment Analyzer (Agilent, Santa Clara, USA). The RNAs were fragmented and subsequently reverse transcribed into cDNAs with the use of random primers for the purpose of constructing a library. Following this, sequencing was performed on the prepared library. All raw sequencing reads generated underwent filtering to produce clean reads, which were then saved in FASTQ format. The alignment of these clean reads to the reference gene and genome was carried out using Bowtie2 and HISAT, respectively. Gene expression levels [fragments per kilobase of transcript per million fragments mapped (FPKM)] were computed through RSEM, while read counts for each gene were obtained via the SubRead package. Normalization along with differential expression analysis was executed using edgeR software. In terms of RNA sequencing (RNA-seq) analysis, genes that displayed |Log₂ fold change| ≥2.5 and a false discovery rate (FDR) <0.01 were classified as DEGs.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses

The analysis of all differentially expressed messenger RNAs (mRNAs) was conducted using the GO enrichment and KEGG pathway databases to elucidate the biological significance of these transcripts. GO terms and KEGG pathways were deemed significantly enriched if their corrected P values were less than 0.01.

Statistical analyses

Statistical evaluations were performed utilizing IBM SPSS version 26.0 for Windows (IBM Corp., Armonk, NY, USA). Continuous variables, represented as the median (range), were analyzed using the Mann-Whitney U test for comparison. Categorical variables, expressed as frequency and percentage, were compared by Fisher’s exact test. Differences were considered statistically significant when P<0.05. The statistical analysis was carried out using R software version 4.1.3, with P values and FDR q values less than 0.01 considered to be statistically significant.

Results

Clinical characteristics

There were thirteen cases enrolled, the Success group with seven cases and the Failure group with six cases. The clinical characteristics with the cooperabilities between two groups were presented in Table 1 (P>0.05).

Differentially expressed transcripts in Success group (Figure 1)

Figure 1 Transcription profile of patients in the Success group. (A) Volcano plot representing the expression level of mRNAs in terms of the P value (<0.01). (B) mRNAs with up-regulated differential expression acquired through GO enrichment analysis. (C) mRNAs with down-regulated differential expression acquired through GO enrichment analysis. (D) mRNAs with up-regulated differential expression acquired through KEGG enrichment analysis. (E) mRNAs with down-regulated differential expression acquired through KEGG enrichment analysis. mRNAs, messenger RNA; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ITI, immune tolerance induction; FDR, false discovery rate; BP, biological process; CC, cellular components; MF, molecular function.

There were 4,967 mRNA transcripts expressed transcripts meeting the threshold of Log₂ fold change ≤−2.5 or ≥2.5 with 57.7% (2,865/4,967) mRNAs were upregulated, and 42.3% (2,102/4,967) were downregulated. Following the GO and KEGG enrichment analyses, the five most significantly enriched GO terms for the upregulated differentially expressed mRNAs were identified in relation to biological processes (BPs), cellular components (CCs), and molecular functions (MFs).

The DEGs were predominantly associated with cellular BPs in the context of BP, with three out of five GO terms linked to DNA replication. The remaining terms pertained to the transition phases of the mitotic cell cycle and organelle fission.

Five top DEGs were enriched in the mitochondrial matrix, chromosomal region, transferase complex, transferring phosphorus-containing groups and condensed chromosome spindle in terms of CC. In terms of MF, the five top DEGs were enriched in the Adenosine triphosphate (ATP) hydrolysis activity, single-stranded DNA binding, coreceptor activity, helicase activity and kinase regulator activity.

The five most significantly enriched GO terms for the downregulated differentially expressed mRNAs, in relation to BP, CC, and MF, were associated with processes such as xenobiotic metabolism, small molecule catabolism, striated muscle contraction, general muscle contraction, and vascular transport within the BP category.

Five top DEGs were enriched in the apical plasma membrane, endocytic vesicle lumen, apical part of cell, myofilament and hemoglobin complex in terms of CC. Three of the five DEGs in terms of MF were related to potassium channel regulator activity, while the rest were related to the haptoglobin binding and action potential repolarization.

The top three most enriched KEGG overrepresented upregulated pathways were cell cycle pathway, T cell receptor signaling pathway and human T-cell leukemia virus 1 infection pathway. The top three most enriched KEGG overrepresented downregulated pathways were cytoskeleton in muscle cells pathway, glycine, serine and threonine metabolism pathway and serotonergic synapse pathway.

Differentially expressed transcripts in Failure group (Figure 2)

Figure 2 Transcription profile of patients in the Failure group. (A) Volcano plot representing the expression level of mRNAs in terms of the P value (<0.01). (B) mRNAs with up-regulated differential expression acquired through GO enrichment analysis. (C) mRNAs with down-regulated differential expression acquired through GO enrichment analysis. (D) mRNAs with up-regulated differential expression acquired through KEGG enrichment analysis. (E) mRNAs with down-regulated differential expression acquired through KEGG enrichment analysis. mRNAs, messenger RNA; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ITI, immune tolerance induction; FDR, false discovery rate; BP, biological process; CC, cellular components; MF, molecular function; CMG, CDC45-MCM2-7-GINS.

Differentially expressed transcripts between the sample of before-treatment and after-treatment in the Failure group were first analyzed (Figure 2A). In total, 515 mRNA transcripts were identified as differentially expressed based on the criteria of Log₂ fold change ≤−2.5 or ≥2.5. Among these, 354 mRNAs were upregulated, and 161 mRNAs were downregulated.

GO and KEGG enrichment analyses were conducted, and Figure 2B displays the five most significantly enriched GO terms for the upregulated differentially expressed mRNAs across BP, CC, and MF. The DEGs were primarily associated with cellular BPs in the context of BP. Among the five GO terms, three were connected to coagulation and hemostasis, while the others pertained to wound healing and the regulation of body fluid levels. DEGs were mainly enriched in platelet and hemoglobin in terms of CC. In terms of MF, the five top DEGs were mainly enriched in the oxygen transport.

Figure 2C illustrates the five most significantly enriched GO terms for the downregulated differentially expressed mRNAs concerning BP, CC and MF. Five top DEGs were mainly enriched in the meiotic cell cycle and innate immune response in terms of BP. Five top DEGs were enriched in the specific granule lumen, specific granule, Golgi lumen, CMG complex and primary lysosome in terms of CC. Five top DEGs were enriched in the bitter taste receptor activity, taste receptor activity, polyamine transmembrane transporter activity, serine-type endopeptidase activity and cysteine-type deubiquitinate activity in terms of MF.

The findings from the KEGG enrichment analysis are presented in Figure 2D,2E. The three most significantly enriched upregulated pathways identified were the malaria pathway, complement and coagulation cascades pathway, and hematopoietic cell lineage pathway. The top three most enriched KEGG overrepresented downregulated pathways were cytoskeleton in asthma pathway, staphylococcus aureus infection pathway and allograft rejection pathway.

DEGs between the two groups (Figure 3)

Figure 3 Different transcription profile of patients between the two groups. FC, fold change.

Based on the results of the above analysis, we further screened for DEGs that showed opposite trends between the Success group and the Failure group. There were 328 mRNAs upregulated in the Success group after ITI while downregulated in the Failure group, seventeen of these were related with immune system. And there were 605 mRNAs which were upregulated in the Failure group after ITI while downregulated in the Success group, one hundred of these were related with immune system.

Discussion

This study made a comparison of the gene expression level among HAI patients having diverse outcomes of ITI. The DEGs were primarily distributed within the domain of immune response regulation, and similar outcomes were identified both in the enrichment analysis of GO terms and KEGG pathways. There are also many studies which have explored the immune regulatory mechanisms underlying ITI (13,14), highlighting the roles of memory B cells, T cells, long-lived plasma cells, and regulatory T cells in promoting tolerance to FVIII (15). This discovery is consistent with the traditional theory that the generation of FVIII inhibitors is a process relying on T cells. Once FVIII epitopes are identified, T cells become activated, thereby promoting the proliferation and differentiation of B cells into plasma cells and ultimately leading to the formation of inhibitors. Evidence suggests that CD4+ T cells play a crucial role in the development of exogenous FVIII antibodies. Additionally, Th17, Th1, and Th2 cells have been regarded as significant elements in the progress of FVIII inhibitor formation (16,17). Apostolou et al. (18) indicates that chronic exposure to antigens in the primary immune system may induce immune tolerance by generating FVIII-specific T cells. These T cells inhibit the activity of anti-FVIII-specific effector T cells. Since anti-FVIII-specific B cells are no longer regulated by effector T cells, their differentiation is hindered, ultimately inhibitors production is hindered and B cells are cleaned. At this point, memory cells, both memory B cells and memory CD4⁺ T cells, take the lead in the immune response. Memory B cells expressing high-affinity antigen receptors, upon re-exposure to specific antigens, promptly differentiate into plasma cells and generate antibodies. Additionally, high-potency antigen-presenting cells activate and induce the differentiation of FVIII specific memory B cells by stimulating T cells. Exploring pre- and post-treatment differences in gene regulation in patients with different therapeutic responses can help find therapeutic targets.

In our study, the Failure group had longer Interval-time than the Success group [17.6 (range 0.2–57.1) vs. 2.9 (range 0.3–32.7)], which shows that persistent suppression leading to a poor prognosis, and mechanisms that modulate long-term immunity may play an important role. We found out that the gene Bcl6 in the Failure group was significantly up-regulated, which is necessary to establish and maintain T and B cell immune memory. Hausl et al. (19) contends that the re-stimulation of antigen specific T cells is capable of potentiating the stimulation and differentiation of FVIII specific memory B cells. The absence of such restimulation leads to the selective inhibition of FVIII-specific memory B cells, which may be an important mechanism. However, there are some different views that suggest that memory B cells are not the only factor. Since FVIII-specific memory B cells have been shown to be associated with persistent positivity for inhibitors, but not all patients with persistent inhibitors can be detected (20-22).

Research on signaling pathways related to inhibitor production and immune tolerance in hemophilia is less extensive. Some studies suggest that the Toll-like receptor (TLR) and tumor necrosis factor signaling pathways may be associated with the production of inhibitors in HA (23-25). In this study, the genes that negatively regulate the signal transduction of TLR were significantly up-regulated in the successful ITI group after treatment. TLR signal transduction induces upregulation of maturation markers and costimulatory molecules (such as CD80, CD83 and CD86) on dendritic cells (26), these molecules can enhance the stimulation of CD4⁺ T cells needed to induce memory B cell differentiation. This finding may suggest that down-regulation of TLR signal transduction may play an important role in the reconstruction of immune tolerance.

There are some limitations in this study. We did not classify immune cells and were unable to determine whether the DEGs were significantly enriched in which particular cell types. In the future, it might be feasible to conduct single-cell sequencing to further explore information on key cell types. The type of gene mutation in the patients we selected was not consistent, and previous studies have confirmed that the type of gene mutation is an important predictor of the efficacy of ITI. Additionally, our sample size was relatively small.

Conclusions

In summary, our study shows that the increased expression of genes which related to immune system activation suggests a possibly favorable therapeutic outcome of ITI. These findings have the potential to reveal novel therapeutic targets for prevention and treatment of inhibitors.

Acknowledgments

Funding: This study was supported by National Natural Science Foundation of China (No. 82270133), Beijing Municipal Science and Technology Commission (No. KZ20231002538), and Capital Health Development Research Project (No. 2022-22093).

Footnote

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-300/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-24-300/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-24-300/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Review Committee of Beijing Children’s Hospital (No. 2017-k-49), and the informed consent form was acquired from the guardians before participation in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Wight J, Paisley S. The epidemiology of inhibitors in haemophilia A: a systematic review. Haemophilia 2003;9:418-35. [Crossref] [PubMed]
2. van den Berg HM, Fischer K, Carcao M, et al. Timing of inhibitor development in more than 1000 previously untreated patients with severe hemophilia A. Blood 2019;134:317-20. [Crossref] [PubMed]
3. Young G. How I treat children with haemophilia and inhibitors. Br J Haematol 2019;186:400-8. [Crossref] [PubMed]
4. DiMichele DM, Kroner BLNorth American Immune Tolerance Study Group. The North American Immune Tolerance Registry: practices, outcomes, outcome predictors. Thromb Haemost 2002;87:52-7.
5. Coppola A, Margaglione M, Santagostino E, et al. Factor VIII gene (F8) mutations as predictors of outcome in immune tolerance induction of hemophilia A patients with high-responding inhibitors. J Thromb Haemost 2009;7:1809-15. [Crossref] [PubMed]
6. Nakar C, Shapiro A. Hemophilia A with inhibitor: Immune tolerance induction (ITI) in the mirror of time. Transfus Apher Sci 2019;58:578-89. [Crossref] [PubMed]
7. Pratt KP, Arruda VR, Lacroix-Desmazes S. Inhibitors-Recent insights. Haemophilia 2021;27:28-36. [Crossref] [PubMed]
8. Mariani G, Scheibel E, Nogao T, et al. Immunetolerance as treatment of alloantibodies to factor VIII in hemophilia. The International Registry of Immunetolerance Protocols. Semin Hematol 1994;31:62-4.
9. Lin S, Wang L, Han C, et al. Targeting HTR2B suppresses nonfunctioning pituitary adenoma growth and sensitizes cabergoline treatment via inhibiting Gαq/PLC/PKCγ/STAT3 axis. Neuro Oncol 2024;26:2010-26. [Crossref] [PubMed]
10. Yang B, Zhai F, Li Z, et al. Identification of ferroptosis-related gene signature for tuberculosis diagnosis and therapy efficacy. iScience 2024;27:110182. [Crossref] [PubMed]
11. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63. [Crossref] [PubMed]
12. Srivastava A, Brewer AK, Mauser-Bunschoten EP, et al. Guidelines for the management of hemophilia. Haemophilia 2013;19:e1-47. [Crossref] [PubMed]
13. Schep SJ, Schutgens REG, Fischer K, et al. Role of Regulatory Cells in Immune Tolerance Induction in Hemophilia A. Hemasphere 2021;5:e557. [Crossref] [PubMed]
14. Astermark J. Immune tolerance induction in patients with hemophilia A. Thromb Res 2011;127:S6-9. [Crossref] [PubMed]
15. Lacroix-Desmazes S, Voorberg J, Lillicrap D, et al. Tolerating Factor VIII: Recent Progress. Front Immunol 2019;10:2991. [Crossref] [PubMed]
16. Lai JD, Cartier D, Hartholt RB, et al. Early cellular interactions and immune transcriptome profiles in human factor VIII-exposed hemophilia A mice. J Thromb Haemost 2018;16:533-45. [Crossref] [PubMed]
17. Reding MT, Lei S, Lei H, et al. Distribution of Th1- and Th2-induced anti-factor VIII IgG subclasses in congenital and acquired hemophilia patients. Thromb Haemost 2002;88:568-75.
18. Apostolou I, von Boehmer H. In vivo instruction of suppressor commitment in naive T cells. J Exp Med 2004;199:1401-8. [Crossref] [PubMed]
19. Hausl C, Ahmad RU, Schwarz HP, et al. Preventing restimulation of memory B cells in hemophilia A: a potential new strategy for the treatment of antibody-dependent immune disorders. Blood 2004;104:115-22. [Crossref] [PubMed]
20. van Helden PM, Kaijen PH, Fijnvandraat K, et al. Factor VIII-specific memory B cells in patients with hemophilia A. J Thromb Haemost 2007;5:2306-8. [Crossref] [PubMed]
21. van Helden PM, Van Haren SD, Fijnvandraat K, et al. Factor VIII-specific B cell responses in haemophilia A patients with inhibitors. Haemophilia 2010;16:35-43. [Crossref] [PubMed]
22. Benito JI, Gil-Carcedo LM, Martín MC, et al. A new system for the induction of experimental acoustic trauma by the application of recorded noise. An Otorrinolaringol Ibero Am 1991;18:177-87.
23. Allacher P, Baumgartner CK, Pordes AG, et al. Stimulation and inhibition of FVIII-specific memory B-cell responses by CpG-B (ODN 1826), a ligand for Toll-like receptor 9. Blood 2011;117:259-67. [Crossref] [PubMed]
24. Astermark J. FVIII inhibitors: pathogenesis and avoidance. Blood 2015;125:2045-51. [Crossref] [PubMed]
25. Dwivedi SD, Shukla R, Yadav K, et al. Mechanistic insight on the role of iRhom2-TNF-α-BAFF signaling pathway in various autoimmune disorders. Adv Biol Regul 2024;92:101011. [Crossref] [PubMed]
26. Steinman RM, Hemmi H. Dendritic cells: translating innate to adaptive immunity. Curr Top Microbiol Immunol 2006;311:17-58. [Crossref] [PubMed]