| Literature DB >> 28421078 |
Rihab Gam1, Pranali Shah2, Rachel E Crossland1, Jean Norden1, Anne M Dickinson1, Ralf Dressel2.
Abstract
The outcome of hematopoietic stem cell transplantation (HSCT) is controlled by genetic factors among which the leukocyte antigen human leukocyte antigen (HLA) matching is most important. In addition, minor histocompatibility antigens and non-HLA gene polymorphisms in genes controlling immune responses are known to contribute to the risks associated with HSCT. Besides single-nucleotide polymorphisms (SNPs) in protein coding genes, SNPs in regulatory elements such as microRNAs (miRNAs) contribute to these genetic risks. However, genetic risks require for their realization the expression of the respective gene or miRNA. Thus, gene and miRNA expression studies may help to identify genes and SNPs that indeed affect the outcome of HSCT. In this review, we summarize gene expression profiling studies that were performed in recent years in both patients and animal models to identify genes regulated during HSCT. We discuss SNP-mRNA-miRNA regulatory networks and their contribution to the risks associated with HSCT in specific examples, including forkheadbox protein 3 and regulatory T cells, the role of the miR-155 and miR-146a regulatory network for graft-versus-host disease, and the function of MICA and its receptor NKG2D for the outcome of HSCT. These examples demonstrate how SNPs affect expression or function of proteins that modulate the alloimmune response and influence the outcome of HSCT. Specific miRNAs targeting these genes and directly affecting expression of mRNAs are identified. It might be valuable in the future to determine SNPs and to analyze miRNA and mRNA expression in parallel in cohorts of HSCT patients to further elucidate genetic risks of HSCT.Entities:
Keywords: MICA; forkheadbox protein 3; gene expression profiling; miR-146a; miR-155; microRNAs; non-human leukocyte antigen single-nucleotide polymorphisms; regulatory networks
Year: 2017 PMID: 28421078 PMCID: PMC5377073 DOI: 10.3389/fimmu.2017.00380
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1Regulation of microRNAs (miRNAs). Expression of miRNAs can be altered at various stages of its biogenesis by genomic [single-nucleotide polymorphisms (SNPs) and mutations] and epigenetic alterations. Changes in the expression and function of Drosha and Dicer, part of the miRNA processing machinery, lead to the deregulation of mature miRNAs. The figure has been adapted from Ref. (14, 15).
Figure 2Genetic regulation of transplant outcome. Genetic variants in protein encoding genes and microRNAs (miRNAs) alter gene expression as well as protein and cellular functions which in turn contribute to the regulating the outcome of transplantation. The color code introduced here is used for genes, miRNAs, mRNAs, and proteins in Figures 3–5.
Figure 3Interaction between microRNAs and forkheadbox protein 3 (FOXP3) in regulatory T cells (T. In Tregs, miR-10a stabilizes FOXP3, and FOXP3 can positively regulate expression of miR-155. This leads to a downregulation of the target SOCS1 (⊤), which in turn results indirectly in increased expression of STAT5. FOXP3 can also be regulated by miR-21, which indirectly positively regulates FOXP3 in a process that is not yet completely understood. Moreover, FOXP3 is downregulated by miR-31 by direct targeting of the 3′ untranslated region. To further complicate this regulatory network, single-nucleotide polymorphisms (SNPs) in FOXP3 also affect its expression in Tregs, such as the SNP rs3761548 or a GT(n) microsatellites (Msat).
Figure 5Regulation of MICA expression and interaction with NKG2D. The single-nucleotide polymorphism (SNP) rs1051792 results in a valine to a methionine exchange at position 129 of MICA and distinguishes MICA variants into those binding the receptor NKG2D with high (MICA-129Met) or low (MICA-129Val) avidity. This polymorphism also affects the cell surface expression of MICA protein. The SNP rs2596542 in the promoter of MICA affects mRNA expression. Moreover, several microRNAs target MICA and downregulate its expression. Moreover, cellular and genotoxic stress induces the expression of MICA. The MICA receptor NKG2D is encoded by the KLRK1 gene and can be targeted by miR-1245.
Figure 4Interaction between miR-146 and miR-155, their effects on the nuclear factor (NF)-kB pathway, and the expression of IRAK1 and tumor necrosis factor (TNF)-α. Activation of the NF-kB pathway represents a hallmark of the pathophysiology of GvHD. NF-kB activation induces expression of miR-146a and in turn, miR-146a inhibits these pathways through targeting key adapter proteins, IRAK1(⊥) and TRAF6 (⊥). The presence of single-nucleotide polymorphisms in coding regions of these genes, such as rs3027898 in IRAK1, further influences expression within the network. MiR-146a expression can also be stimulated by lipopolysaccharide (LPS) release during GvHD conditioning. The miR-146a and miR-155 mediate an increase in TNF-α, which in turn can positively regulate miR-155 in a feedback loop.
Summary of large-scale mRNA expression profiling studies during GvHD.
| Species | Tissue | Disease | Upregulated | Downregulated | Technique | Reference |
|---|---|---|---|---|---|---|
| Human | Peripheral blood mononuclear cells (PBMCs) | Acute graft-versus-host disease (aGvHD) | qRT-PCR | ( | ||
| Human | PBMC | aGvHD | Microarray | ( | ||
| Human | CD4+ and CD8+ T cells | Chronic graft-versus-host disease (cGvHD) | qRT-PCR | ( | ||
| Human | PBMC | aGvHD | Microarray | ( | ||
| Human | PBMC | cGvHD | Microarray | ( | ||
| Human | Conjunctiva | cGvHD-DE | qRT-PCR | ( | ||
| Human | Conjunctiva | GvHD-DE | qRT-PCR | ( | ||
| Mouse | Ear skin day 7 | aGvHD | Microarray | ( | ||
| Ear skin day 40 | ||||||
| Mouse | Liver day 7 | aGvHD | Microarray | ( | ||
| Liver day 35 | ||||||
| Mouse | Liver versus kidney | aGvHD | Microarray | ( | ||
| Mouse | Scl | GvHD | Microarray | ( | ||
| Rat | Skin explant model | aGvHD | Microarray | ( |
The listed mRNA profiling studies were carried out with human or rodent tissues obtained during both aGvHD and cGvHD. The genes regulated during GvHD with the highest fold changes and significant P-values are indicated.
.
Figure 6Alterations in cytokine levels, such as interleukin (IL)-10, IL-6, and IL-2, . Binding of cytotoxic T lymphocyte antigen 4 (CTLA4) with its receptor (possibly also via functional single-nucleotide polymorphisms) with CD80/CD86 proteins on dendritic cells (DCs) can lead to induction of indoleamine 2,3 dioxygenase (IDO) and the catabolism of tryptophan into proapoptotic metabolites causing immunosuppression of Teff. Altered binding of CTLA4 may also lead to reduced immunosuppression via Tregs and GvHD. High IL-6 levels induced in DCs by Treg interaction can also cause alteration of Tregs to Th17 cells and may lead to exacerbation of GvHD. The figure has been taken from Ref. (170). Professional illustration by Alice Y. Chen.