1. Introduction
Adverse drug reactions (ADRs) are harmful and unintended reactions triggered by a drug used in the prevention, diagnosis or therapy of diseases at suitable pharmacological dosage [1]. They occur despite proper application of the respective drug and are classified as type A and type B reactions based on their pharmacological predictability [2]. Type A ADRs arise dose-dependently and are triggered by pharmacodynamic reactions and influenced by pharmacokinetics [3,4], hence these augmented reactions are rarely life-threatening. In contrast, type B reactions are not predictable based on the drug’s pharmacology and seem to be idiosyncratic. Both the innate and the adaptive immune system can be involved in the development of an idiosyncratic ADR; they occur especially in patients with a certain predisposition and result in high mortalities [5,6]. Manifestations comprise cutaneous symptoms ranging from milder exanthema to life-threatening Stevens–Johnson Syndrome (SJS) or toxic epidermal necrolysis (TEN), yet liver, kidney and blood cells might be affected as well [7].
Type B reactions are further divided into (1) immediate onset antibody-mediated reactions that cause rapid and serious allergic reaction like anaphylaxis and (2) delayed hypersensitivity reactions (DHRs) characterized by an onset of symptoms within hours or weeks post drug administration [8]. DHRs are often T-cell-mediated and triggered by drug-induced immune stimulation [9,10]. In recent years, associations between type B ADRs and distinct alleles of the highly polymorphic human leukocyte antigen (HLA) system have been discovered [11,12,13,14]. These cell surface molecules are involved in immune responses through the presentation of endogenously processed self- or pathogen-derived peptides to autologous T cells. In the case of HLA class I molecules that are expressed on almost all nucleated cells [15], the self-peptidome is presented to CD8+ T cells that recognize the trimeric complex of HLA class I heavy chain, light chain and peptide with their respective T cell receptors (TCRs) [16]. The simultaneous co-recognition of a TCR for self HLA class I molecules and peptides of self or foreign origin is unique in receptor-ligand interactions [17]. Based on the origin of the presented peptide, the immune system appreciates the individual health condition and elicits immune responses. Polymorphisms within distinct HLA allotypes result in differential structural and electrochemical features of the peptide binding region (PBR) and therefore determine the presented immunopeptidome [18].
Synthetic substances used as medical products are usually too small (<1 kDa) to directly induce an immune response. However, three hypotheses have been proposed to explain how small molecules can activate the immune system in an HLA-dependent manner: by (1) operating as a hapten or prohapten that covalently reacts with endogenous proteins or peptides resulting in a haptenated, de novo product with immunogenic features (hapten/prohapten model); (2) binding to immune receptor proteins (HLA and/or TCR) themselves in a direct, reversible and non-covalent manner (p-i model); or (3) occupying the PBR of distinct HLA alleles during HLA assembly resulting in new peptide binding motifs and the presentation of novel self-peptides as neo-antigens (altered repertoire model) [2,19]. The small molecule/HLA interaction might be drug-specific and HLA type-specific, the unique occurrence of one theoretical model or the exclusion of one theoretical model cannot be specified.
Abacavir (ABC) is a drug used as part of a highly active antiretroviral therapy (HAART) for the treatment of HIV-1+ patients. Approximately 5% of HIV-1+ patients develop DHRs post ABC treatment. This Abacavir Hypersensitivity Syndrome (AHS) is characterized by multiorgan symptoms including fever, gastrointestinal complaints, and skin rash usually appearing during the first 42 days of treatment with a median time to onset of 11 days [20]. Symptoms usually resolve 24 h after discontinuation of ABC treatment, however, if patients are re-challenged with ABC, AHS would rapidly recur with potential life-threatening consequences [21].
In the early 2000s, several clinical studies found a strong predictive association between AHS and the occurrence of the genotype HLA-B*57:01 [22,23,24]. Thus, HLA typing and screening for HLA-B*57:01 is recommended by the Food and Drug Administration (FDA) drug label of ABC since 2008 and the presence of HLA-B*57:01 is a contraindication for treatment of HIV-1+ patients with ABC [25].
The mechanism that enables ABC to activate the immune system and elicit immune responses corresponds to the altered repertoire model, this could be demonstrated unambiguously by structural evidence. ABC binds specifically within the PBR of HLA-B*57:01 and alters the biochemical features of the HLA F pocket. As a result, an altered peptide binding motif allows novel self-peptides to bind to HLA-B*57:01 and to be presented to autologous CD8+ T cells [26,27,28]. Due to missing T cell tolerance to these unknown self-peptides, autologous CD8+ T cells start eliciting polyclonal T cell responses against the neo-antigen [29].
Before admission, a medical product undergoes several approval filters. As these clinical trials include a selected and limited number of patients, it becomes obvious that the occurrence of adverse events are often overlooked or misinterpreted. Yet, there are numerous medical products that are strongly affected by the individual genetics of a patient. Knowledge about the full protein spectrum that is influenced by a drug contributes to the identification of potential risk factors and hence, is extremely important for drug safety. Full proteome analysis is a sophisticated methodology to elucidate the impact of drug treatment on cells [30].
ABC is an excellent candidate to exploit the mechanism and find possible prognostic markers responsible for HLA-associated ADRs due to the extraordinary specificity of AHS and HLA-B*57:01 [31]. This study aims to conduct an in vitro investigation into the cellular response to ABC using proteomic strategies to increase the knowledge of molecular processes involved in the development of AHS. Comprehensive understanding will provide significant fundamental insight in the area of immunology that will allow the prediction of a patient’s risk for HLA-mediated ADRs and contribute to well-advanced personalized and safe treatments.
2. Materials and Methods
2.1. Maintenance of Cell Lines
All cell lines were cultured at 37 °C and 5% CO2. The human B lymphoblastoid cell line LCL721.221 (LGC Promochem®, Wesel, Germany; HLA class I-/TPN+) was cultured in RPMI 1640 medium (Lonza, Basel, Switzerland) supplemented with 10% heat-inactivated fetal calf serum (FCS, Lonza), 2 mM L-glutamine (c. c. pro, Oberdorla, Germany), 100 U/mL penicillin and 100 µg/mL streptomycin (c. c. pro).
The human embryonal kidney cell line HEK239T (cell source ATCC, Manassas, VA, USA) was grown in Dulbecco’s Modified Eagle Medium (DMEM, Lonza) supplemented with 10% heat-inactivated FCS, 2 mM L-glutamine, 100 U/mL penicillin, 100 µg/mL streptomycin and 1 mg/mL Geneticin® (Life Technologies, Carlsbad, CA, USA).
2.2. Cloning of Constructs Encoding for Soluble HLA-B*57:01
Constructs encoding for full length HLA-B*57:01 (mHLA-B*57:01, Exon 1–7) were generated from cDNA of an HLA-B*57:01+ donor via PCR as previously described [32]. The cDNA encoding for soluble HLA-B*57:01 (sHLA-B*57:01, Exon 1–4) was generated by side-directed mutagenesis and cloned into the lentiviral vector pRRL.PPT.SFFV.mcs.pre and checked through sequencing.
2.3. Stable Transduction of LCL721.221 Cells with Lentivirus Encoding for sHLA-B*57:01
HEK293T cells were transfected with the sHLA-B*57:01 encoding transfer vector and the packaging and envelope vectors psPAX and pmD2G. Produced virus was used to stably transduce LCL721.221 cells as previously described [33,34]. The expression of sHLA-B*57:01 molecules was confirmed by ELISA as described by Celik et al. [35].
2.4. Mass Spectrometric Detection of ABC in Solution
For ABC tracking in cell lysates, 5 × 105 LCL721.221 and LCL721.221/sHLA-B*57:01 cells were incubated with or without 50 µg/mL ABC for 48 h. Cells were lysed in 250 µL RIPA buffer as described by Ho et al. [36]. For sample preparation, 50 μL of sample, standard or quality control was added to 200 μL 0.1 M ZnSO4. For precipitation, 500 μL of internal standard (IS) precipitation solution was added to the mixture and mixed for 10 s, followed by a centrifugation step at 13,000× g for 10 min. The clear supernatant was transferred into MS-vials and 1.0 μL was injected into the UPLC-MS/MS system.
ABC was separated on a 2.1 × 50-mm reverse phase column (Waters, Acquity UPLC BEH Phenyl, 1.7 μm) maintained at 50 °C using an ultra-performance liquid chromatography system directly coupled to a Waters TQ electrospray ionization-tandem mass spectrometry (TQD). The sample was applied at a flow rate of 0.5 mL/min using the following gradient program: isocratic flow of 75%/25% water/methanol containing 0.1% formic acid and 2 mmol/L ammonium acetate was performed for 60 s. After that a linear gradient over 1.3 min of 5%/95% water/methanol containing 0.1% formic acid and 2 mmol/L ammonium acetate followed. After the isocratic elution of 95% methanol for 0.5 min, the mobile phase was reversed to the initial state.
The TQD was operated in electrospray positive ionization mode. The system controls of the devices and data acquisition were performed using MassLynx NT 4.1 software. Data processing was performed by the MassLynx QuanLynx program. Nitrogen was used as the nebulizing gas and argon as the collision gas. Instrument settings were as follows: capillary voltage, 0.423 kV; source temperature, 110 °C; desolvation temperature, 480 °C; collision gas pressure, 2.6 × 10−3. Samples analysis was performed in the multiple reaction monitoring mode (MRM) of the instrument. Sample cone voltage, collision energy, dwell time, and mass transitions for all compounds are listed in Table 1.
2.5. Mass Spectrometric Analysis of ABC-Induced Modifications of the LCL721.221 Proteome
For proteome analysis, 1 × 106 LCL721.221 and LCL721.221/sHLA-B*57:01 cells were treated with 50 µg/mL ABC for 48 h. Drug treatment was repeated after 24 h. Cells were harvested and lysed in RIPA buffer as previously described. By using the Bicinchoninic Acid Assay (BCA) Protein Quantitation Kit (Interchim, San Diego, CA, USA) protein concentration was determined.
Sample preparation and MS analysis was performed as previously described by Simper et al. [37]. Proteins were digested in solution with Lys-C for 4 h at 37 °C and afterwards with trypsin overnight at 37 °C.
3. Results
3.1. Development of a Mass Spectrometric Method for the Detection of ABC in Solution
For the detection of ABC in solution, a tandem mass spectrometry (MS/MS) method using multiple reaction monitoring (MRM) mode for sample analysis was established. The principle of the MS/MS method we used is the following: the sample that was previously separated by ultra-performance liquid chromatography (UPLC) is ionized by positive electrospray ionization (ESI+). The ionized precursor/parent ion masses are scanned in the first mass analyzer (parent ion scan). Only molecule–ions with a uniquely defined mass find their way to the collision cell where the collision gas argon leads to a fragmentation of these molecule–ions. Fragmented daughter ions are analyzed in the second mass analyzer (daughter ion scan) (Figure 1A). Though every molecule features a characterized fragmentation pattern, this method offers the possibility to identify a specific analyte. To maximize the peak area corresponding to the molecular mass of the analyte ABC and its daughter ions and to achieve the desired response for ABC, MS settings (capillary and cone voltage and desolvation temperature) were optimized. The adjusted MS settings were used to establish a MS/MS method using MRM (Table 1). For ABC and deuterated ABC (ABC-D4, used as internal standard), the two most sensitive mass transitions were utilized for determination. The first mass transition allowed for quantification of the analyte. The second mass transition leads to an increase in specificity of the method. Figure 1B illustrates the MRM chromatogram of the most sensitive daughter ion for quantification of ABC and ABC-D4.
3.2. Recombinant B Cells Absorb ABC
To assure ABC uptake in recombinant B cells, parental LCL721.221 and LCL721.221 cells expressing soluble HLA-B*57:01 (LCL721.221/sHLA-B*57:01) were treated without or with 50 µg/mL ABC and cultured for 48 h. Afterwards, ABC concentration in cell lysates was measured. In LCL721.221/sHLA-B*57:01 cells 0.12 µg/mL ABC and in parental LCL721.221 cells 0.10 µg/mL ABC was detected (Figure 2). Additionally, cell viability of LCL721.221 cells treated with ABC was verified (Figure S1). The absorption of ABC in recombinant B cells was verified and ABC absorption capacity was similar in parental LCL721.221 and LCL721.221/sHLA-B*57:01 cells.
3.3. Impact of ABC Treatment on Protein Expression of Recombinant B Cells
The cellular response of LCL721.221/sHLA-B*57:01 cells and parental LCL721.221 cells to ABC was investigated by full proteome analysis. The respective recombinant B cell lines were cultivated either without or with ABC for 48 h. After 24 h ABC treatment was repeated. Label free quantification enabled the determination of relative protein abundance and relative protein amounts were calculated by MaxQuant software. Each cell line was normalized by subtracting the median value from each sample.
For visualization of significant differences between both cell lines as well as no treatment and ABC treatment, a principal component analysis (PCA) was performed. Significant differences between all conditions are detectable (Figure 3).
Due to differences of the proteomic content in parental LCL721.221 and LCL721.221/sHLA-B*57:01 cells that might have arisen through lentiviral transduction of the cells, a further normalization step was applied by subtracting the proteome content of untreated cells from ABC-treated cells. Thereby proteomic differences due to previous lentiviral transduction of LCL721.221/sHLA-B*57:01 cells were eliminated and comparison of both cell lines solely among ABC treatment was enabled.
LC-MS-based proteomic analysis of LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells following ABC treatment revealed 5620 identified protein groups; 3880 of them could be quantified. Exclusively significant regulated proteins (p-value < 0.05) and proteins that were at least altered by a factor of log2 ± 1.0 in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells were regarded as regulated. In total, 60 quantified proteins were significantly regulated; 21 of them were significantly upregulated in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells (Figure 4, depicted in red).
The 10 strongest upregulated proteins following ABC treatment of LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells are depicted in Table 2. The strongest upregulated protein is the histone variant H3.3 (H3F3A) that substitutes conventional H3.3 in a wide range of transcriptionally active chromatin [38]. Furthermore, the nucleoside diphosphate kinase A (NME1) that is involved in cell proliferation, differentiation and development as well as in signal transduction and gene expression is also significantly upregulated. The interferon-induced helicase C domain-containing protein 1 (IFIH1) belongs to the pattern recognition receptors (PRRs) and recognizes viral infection resulting in the activation of antiviral responses shows a 4.75-fold upregulation in ABC-treated LCL721.221/sHLA-B*57:01 cells compared to parental cells.
A protein–protein interaction (PPI) network created via STRING Database demonstrated the involvement of significantly regulated proteins in LCL721.221/sHLA-B*57:01 cells compared to parental cells following ABC treatment in various cellular processes (Figure 5). Upregulated proteins are particularly involved in interferon (IFN) production, RNA and mRNA processing and purine metabolism.
We utilized the Ingenuity Pathway Analysis (IPA) software to identify upstream regulators of the identified significantly up- and downregulated proteins (Table 3). The macrophage colony-stimulating factor 1 (CSF1), the histone acetyltransferase KAT2A and the interferon regulatory factor 3 (IRF3) were identified as activated in LCL721.221/sHLA-B*57:01 cells compared to parental cells following ABC treatment. CSF1 is a cytokine that has an essential function in innate immunity and inflammatory processes by promoting the release of proinflammatory chemokines upon binding to its cognate receptor. KAT2A has been described to positively regulate the activation of T cells by acetylation of histone H3 resulting in Interleukin-2 (IL-2) expression. As a transcriptional regulator of type I IFN-dependent immune responses, IRF3 has an important role in innate immune responses against viruses. In contrast, IPA revealed the signal transducer and activator of transcription 6 (STAT6) and the DNA binding protein Ikaros (IKZF1) to be inhibited in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells following ABC treatment. STAT6 induces transcription activation, transduces signals and is especially involved in IL-3- and IL-4-mediated signaling. IKZF1 is involved in the regulation of lymphocyte differentiation and proliferation. Additionally, IKZF1 plays an important role in maintenance of self-tolerance.
4. Discussion
Pharmacovigilance pre- and post-approval of a drug is fundamental for safe medication. Currently, patient-specific personalized medication is a significant topic, since over time more and more ADRs became apparent. HLA-mediated ADRs are the most prevalent problem according to the highly polymorphic HLA genetics.
Due to the extraordinary specificity of AHS and the occurrence of the HLA-B*57:01 genotype, exploiting the mechanism of ABC-induced ADR would guide towards a deeper comprehension of HLA-mediated ADRs. The low PPV for many HLA-associated ADRs indicate that not solely the occurrence of the respective HLA risk allele, but also the involvement of other patient-specific factors might contribute to the development of type B ADRs [39].
The field of proteomics has become more and more important in assessing ADRs. The proteomic repertoire of a cell reflects a real-time view on the individual’s health status. Drugs usually interfere with the structural integrity or cellular function of its primary target, but can also interact with off-targets and effect cellular pathways that might result in phenotypic effects in the treated patient [40]. Thus, the analysis of proteomic changes induced by treatment with a certain drug contributes to earlier detection of ADRs and a decrease of health hazards in patients, and consequently improves drug safety.
In this study, we compared the regulatory impact of ABC on LCL721.221/sHLA-B*57:01 and parental LCL721.221 target cells with particular attention to the influence of small molecule/HLA complexes on proteomic development [30]. To ensure the integrity of recombinant LCL721.221/HLA-B*57:01 target cells treated with ABC, a cytotoxicity assay according to Jedema et al. [41] was established that allowed for the detection of ABC-specific cytotoxicity of CD8+ cells from HLA-B*57:01 carriers towards LCL721.221/HLA-B*57:01 target cells treated with ABC (Figure S2).
Full proteome analysis revealed significant differences in the cellular response of the respective proteomes following ABC treatment. In 2012, Ostrov et al. analyzed peptide sequences eluted from untreated and ABC-treated LCL721.221/sHLA-B*57:01 cells [28]. Consistent with the peptides (KSRPEDQRSSF and RTIKKQRKY
IFIH1 encoding the melanoma differentiation-associated protein 5 (MDA5), is significantly upregulated in LCL721.221/sHLA-B*57:01 cells compared to LCL721.221 cells. MDA5 belongs to the PPR family and is involved in antiviral responses through sensing viral double-stranded RNA (dsDNA) resulting in type I interferon (IFN) production and secretion. MDA5 also plays an essential role in autoimmune diseases, i.e., in multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus and type 1 diabetes (T1D) [42,43,44,45,46]. The molecular mechanism of how MDA5 contributes to these autoimmune diseases is not fully revealed yet, however it has been proposed that the chronic induction of IFN induces or enhances autoinflammation. SNPs in the IFIH1 gene resulting in the expression of a truncated, non-functional protein or impaired splicing of the IFIH1 transcript have been found to be associated with a decreased risk to develop T1D [47]. Conversely, SNPs resulting in increased IFIH1 transcription and MDA5 expressing or a constitutively active MDA5 protein might lead to autoimmune responses towards cellular dsRNA [48,49,50]. We observed an upregulation of MDA5 in LCL721.221 cells expressing the ABC risk allele HLA-B*57:01 when compared to parental LCL721.221 cells following ABC treatment that might favor autoimmune responses triggered by dsRNA derived from cellular sources. Though MDA5 is upregulated in ABC-treated LCL721.221/sHLA-B*57:01, but not in ABC-treated parental LCL721.221 cells, MDA5 function in autoimmunity seems to be associated with the ABC risk allele HLA-B*57:01.
The PPI network of significantly regulated proteins in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells revealed the involvement of upregulated proteins, particularly in IFN production, RNA and mRNA processing and purine metabolism. In contrast, downregulated proteins are rather involved regulatory processes and small molecule metabolism. IFN production plays a crucial role in the development of systemic autoimmune diseases by activating antigen presenting cells and immune effector cells with potential autoreactive activity [51]. Upregulated proteins in LCL721.221/sHLA-B*57:01 cells compared to parental cells that function in RNA and mRNA processing particularly include proteins belonging to the spliceosome (PRP31, CTNNBL1, SMU1, SF3B6, SRSF2). Spliceosome components play a role in immunity, i.e., the serine/arginine-rich splicing factor 2 (SRSF2) can induce inflammatory cytokine production through TLR-mediated NFкB activation [52].
IPA enabled the identification of upstream regulators that might be activated or inhibited by ABC in LCL721.221/sHLA-B*57:01 cells compared to parental cells. CSF1, KAT2 and IRF3 have been identified as activated upstream regulators of various proteins. These proteins play an essential role in innate and adaptive immune responses as well as in inflammatory processes. The cytokine CSF1 (also named macrophage colony-stimulating factor) is a ligand for the tyrosine kinase macrophage-colony stimulating factor 1 receptor (CSF1R) on mononuclear phagocytes and elicits production and differentiation of macrophages [53,54]. Furthermore, CFS1 induces the production of proinflammatory cytokines and chemokines in human whole blood [55] and an involvement in various inflammatory diseases has been described for CSF1 [53]. IRF3 has an essential function in innate antiviral immune responses. Upon recognition of microbial molecular components by PPRs, IRF3 activation leads to the expression of type I IFNs. Overexpression of IRF3 provokes the induction of an efficient antiviral state [56]. Despite the effective function in antiviral immunity, certain IFN-driven autoimmune diseases are caused by unregulated IRF3 [57,58].
Here, we demonstrated the regulatory impact of ABC/HLA-B*57:01. Altogether, we revealed widespread effects of ABC on proteins involved in immune functions and inflammation. This study exemplifies the complexity of regulatory cellular processes induced by drugs that contribute to HLA-mediated ADRs and emphasizes the requirement to fundamentally comprehend molecular and cellular mechanisms affected by drug application to improve drug safety and facilitate personalized medicine.
Conceptualization, F.J.H. and C.B.-D.; methodology, F.J.H., E.G., G.-G.T.H. and W.H.; software, F.J.H., A.P., F.S. and J.K.; validation, F.J.H., E.G. and W.H.; formal analysis, F.J.H., C.B.-D., A.P. and J.K.; investigation, F.J.H. and C.B.-D.; resources, R.B.; writing—original draft preparation, F.J.H. and C.B.-D.; writing—review and editing, F.J.H. and C.B.-D.; visualization, F.J.H.; supervision, C.B.-D.; funding acquisition, C.B.-D. and R.B. All authors have read and agreed to the published version of the manuscript.
This research was funded by the Hector Foundation (MED1912).
Informed consent was obtained from all subjects involved in the study.
The excellent technical assistance and scientific contribution of Ulrike Schrameck and Karsten Heidrich is gratefully acknowledged.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. MS/MS method for the detection of ABC. (A) Principle of a tandem mass spectrometer. (B) MRM chromatograms of the first mass transition for analyte quantification of ABC-D4 and ABC.
Enlarge this image.Figure 2. ABC concentration in parental LCL721.221 and LCL721.221/sHLA-B*57:01 cell lysates. Cells were incubated without or with 50 µg/mL ABC for 48 h and ABC concentrations were measured in cell lysates of three technically independent replicates (n = 3) by UPLC-MS/MS.
Enlarge this image.Figure 3. Principal component analysis (PCA) of proteins that were significantly altered (p < 0.05) in parental LCL721.221 cells (blue) and LCL721.221/sHLA-B*57:01 cells (red) without ABC treatment (circle) and following ABC treatment (triangle). Cells were incubated without (ø) or with (+) ABC for 48 h in three technically independent replicates (n = 3).
Enlarge this image.Figure 4. Differences in protein abundance in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells following ABC treatment. The volcano plot illustrates significantly differentially abundant proteins after ABC treatment of three technically independent replicates (n = 3). The log2-fold change of LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells is plotted against the –log10 p-value. Proteins were regarded as regulated in LCL721.221/sHLA-B*57:01 cells from factor ± 1.0 and p < 0.05. Downregulated proteins are labelled in green; unregulated proteins are colored in grey and upregulated proteins are given in red.
Enlarge this image.Figure 5. Protein–protein interaction network of significantly up- and downregulated proteins following ABC treatment in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells and cellular processes they are involved in (based on GO/KEGG). Network was constructed by STRING Database (Version 11.5) and visualized using Cytoscape (Version 3.8.2). Significant regulated proteins (p < 0.05) at least altered by a factor of log2 ± 0.5 were considered. Upregulated proteins are illustrated in red; downregulated proteins are depicted in green. Color intensity reflects the log2-fold difference between LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells.
Multiple reaction monitoring (MRM) transitions monitored (m/z) with cone and collision energy.
| Analyte | MRM [m/z] | Dwell [s] | Cone [V] | Collision [eV] |
|---|---|---|---|---|
| ABC | 287.2 → 191.1 | 0.05 | 36 | 20 |
| 287.2 → 78.9 | 0.05 | 36 | 31 | |
| ABC-D4 | 291.3 → 195.1 | 0.05 | 37 | 29 |
| 191.3 → 78.9 | 0.05 | 37 | 20 |
Strongest upregulated proteins in LCL721.221/sHLA-B*57:01 cells compared to parental LCL721.221 cells after ABC treatment.
| Protein Name | Gene Code | Regulation | p-Value |
|---|---|---|---|
| Histone H3.3 | H3F3A | 25.63 | <0.001 |
| Nucleoside diphosphate kinase A | NME1 | 8.69 | 0.003 |
| Interferon-induced helicase C domain-containing protein 1 | IFIH1 | 4.75 | 0.002 |
| Periphilin-1 | PPHLN1 | 3.92 | 0.001 |
| Glucosylceramidase | GBA | 3.72 | 0.044 |
| Ras-related protein Rab-35 | RAB35 | 3.71 | 0.008 |
| Mitochondrial fission process protein 1 | MTFP1 | 3.61 | 0.045 |
| Hermansky-Pudlak syndrome 5 protein | HPS5 | 3.14 | 0.021 |
| Transcription factor A, mitochondrial | TFAM | 3.12 | 0.023 |
| Prefoldin subunit 3 | VBP1 | 2.95 | 0.010 |
Upstream regulator of significantly up- and downregulated proteins in LCL721.221/sHLA-B*57:01 cells compared to parental cells following ABC treatment.
| Protein Name of |
Gene Code | Predicted |
Activation |
Target Molecules |
|---|---|---|---|---|
| Macrophage colony-stimulating factor 1 | CSF1 | Activated | 3.411 | ACSL4, ATP1B1, CD36, CFL1, CPT2, DHCR7, ETFA, FASN, FDFT1, FDPS, HADHB, HSPD1, IDH3A, IDI1, IQGAP1, LCP1, MT-CO2, RPL5, RRM2, RUVBL2, SCP2, SLC30A1, TFAM, UHRF1 |
| Histone acetyltransferase KAT2A | KAT2 | Activated | 2.236 | HSP90AA1, HSPD1, LDHA, PRKDC, SAE1, XPO1 |
| Interferon regulatory |
IRF3 | Activated | 2.187 | AHNAK, ANXA4, CD58, IFI44L, IFIH1, PTMS, STAT1, TMPO |
| Signal transducer and |
STAT6 | Inhibited | −2.228 | CDK6, G6PD, PFKL, PYGL |
| DNA binding protein |
IKZF1 | Inhibited | −2.000 | AHNAK, ANXA1, CDK2, CTSS, FASN, FSCN1, IFI44L, IFIH1, RAB35, STAT5B, SYNGR2 |
Supplementary Materials
The following are available online at
References
1. WHO. International drug monitoring: The role of national centres. Report of a WHO meeting. World Health Organ. Tech. Rep. Ser.; 1972; 498, pp. 1-25.
2. Simper, G.S.; Celik, A.A.; Kunze-Schumacher, H.; Blasczyk, R.; Bade-Doding, C. Physiology and Pathology of Drug Hypersensitivity: Role of Human Leukocyte Antigens. Physiology and Pathology of Drug Hypersensitivity: Role of Human Leukocyte Antigens, Physiology and Pathology of Immunology; Rezaei, N. IntechOpen: London, UK, 2017; pp. 55-74. [DOI: https://dx.doi.org/10.5772/intechopen.72133]
3. Pichler, W.J.; Hausmann, O. Classification of Drug Hypersensitivity into Allergic, p-i, and Pseudo-Allergic Forms. Int. Arch. Allergy Immunol.; 2016; 171, pp. 166-179. [DOI: https://dx.doi.org/10.1159/000453265]
4. Adam, J.; Pichler, W.J.; Yerly, D. Delayed drug hypersensitivity: Models of T-cell stimulation. Br. J. Clin. Pharmacol.; 2011; 71, pp. 701-707. [DOI: https://dx.doi.org/10.1111/j.1365-2125.2010.03764.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21480949]
5. Rawlins, M.D. Clinical pharmacology. Adverse reactions to drugs. Br. Med. J. (Clin. Res. Ed.); 1981; 282, pp. 974-976. [DOI: https://dx.doi.org/10.1136/bmj.282.6268.974]
6. Edwards, I.R.; Aronson, J.K. Adverse drug reactions: Definitions, diagnosis, and management. Lancet; 2000; 356, pp. 1255-1259. [DOI: https://dx.doi.org/10.1016/S0140-6736(00)02799-9]
7. Uetrecht, J.; Naisbitt, D.J. Idiosyncratic adverse drug reactions: Current concepts. Pharmacol. Rev.; 2013; 65, pp. 779-808. [DOI: https://dx.doi.org/10.1124/pr.113.007450]
8. Deshpande, P.; Hertzman, R.J.; Palubinsky, A.M.; Giles, J.B.; Karnes, J.H.; Gibson, A.; Phillips, E.J. Immunopharmacogenomics: Mechanisms of HLA-associated drug reactions. Clin. Pharmacol. Ther.; 2021; 110, 2343. [DOI: https://dx.doi.org/10.1002/cpt.2343] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34143437]
9. Illing, P.T.; Purcell, A.W.; McCluskey, J. The role of HLA genes in pharmacogenomics: Unravelling HLA associated adverse drug reactions. Immunogenetics; 2017; 69, pp. 617-630. [DOI: https://dx.doi.org/10.1007/s00251-017-1007-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28695285]
10. Pichler, W.J. Delayed drug hypersensitivity reactions. Ann. Intern. Med.; 2003; 139, pp. 683-693. [DOI: https://dx.doi.org/10.7326/0003-4819-139-8-200310210-00012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14568857]
11. Chung, W.H.; Hung, S.I.; Hong, H.S.; Hsih, M.S.; Yang, L.C.; Ho, H.C.; Wu, J.Y.; Chen, Y.T. Medical genetics: A marker for Stevens-Johnson syndrome. Nature; 2004; 428, 486. [DOI: https://dx.doi.org/10.1038/428486a] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15057820]
12. Hung, S.I.; Chung, W.H.; Liou, L.B.; Chu, C.C.; Lin, M.; Huang, H.P.; Lin, Y.L.; Lan, J.L.; Yang, L.C.; Hong, H.S. et al. HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol. Proc. Natl. Acad. Sci. USA; 2005; 102, pp. 4134-4139. [DOI: https://dx.doi.org/10.1073/pnas.0409500102] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15743917]
13. Wolf, R.; Matz, H.; Orion, E.; Tuzun, B.; Tuzun, Y. Dapsone. Dermatol. Online J.; 2002; 8, 2. [DOI: https://dx.doi.org/10.5070/D330M4B5KR]
14. Nakkam, N.; Gibson, A.; Mouhtouris, E.; Konvinse, K.C.; Holmes, N.E.; Chua, K.Y.; Deshpande, P.; Li, D.; Ostrov, D.A.; Trubiano, J. et al. Cross-reactivity between vancomycin, teicoplanin, and telavancin in patients with HLA-A *32:01-positive vancomycin-induced DRESS sharing an HLA class II haplotype. J. Allergy Clin. Immunol.; 2021; 147, pp. 403-405. [DOI: https://dx.doi.org/10.1016/j.jaci.2020.04.056]
15. Hewitt, E.W. The MHC class I antigen presentation pathway: Strategies for viral immune evasion. Immunology; 2003; 110, pp. 163-169. [DOI: https://dx.doi.org/10.1046/j.1365-2567.2003.01738.x]
16. Neefjes, J.; Jongsma, M.L.; Paul, P.; Bakke, O. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat. Rev. Immunol.; 2011; 11, pp. 823-836. [DOI: https://dx.doi.org/10.1038/nri3084]
17. Zinkernagel, R.M.; Doherty, P.C. Restriction of in vitro T cell-mediated cytotoxicity in lymphocytic choriomeningitis within a syngeneic or semiallogeneic system. Nature; 1974; 248, pp. 701-702. [DOI: https://dx.doi.org/10.1038/248701a0]
18. Huyton, T.; Ladas, N.; Schumacher, H.; Blasczyk, R.; Bade-Doeding, C. Pocketcheck: Updating the HLA class I peptide specificity roadmap. Tissue Antigens; 2012; 80, pp. 239-248. [DOI: https://dx.doi.org/10.1111/j.1399-0039.2012.01928.x]
19. Redwood, A.J.; Pavlos, R.K.; White, K.D.; Phillips, E.J. HLAs: Key regulators of T-cell-mediated drug hypersensitivity. HLA; 2018; 91, pp. 3-16. [DOI: https://dx.doi.org/10.1111/tan.13183] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29171940]
20. Hetherington, S.; McGuirk, S.; Powell, G.; Cutrell, A.; Naderer, O.; Spreen, B.; Lafon, S.; Pearce, G.; Steel, H. Hypersensitivity reactions during therapy with the nucleoside reverse transcriptase inhibitor abacavir. Clin. Ther.; 2001; 23, pp. 1603-1614. [DOI: https://dx.doi.org/10.1016/S0149-2918(01)80132-6]
21. Escaut, L.; Liotier, J.Y.; Albengres, E.; Cheminot, N.; Vittecoq, D. Abacavir rechallenge has to be avoided in case of hypersensitivity reaction. AIDS; 1999; 13, pp. 1419-1420. [DOI: https://dx.doi.org/10.1097/00002030-199907300-00026]
22. Mallal, S.; Nolan, D.; Witt, C.; Masel, G.; Martin, A.M.; Moore, C.; Sayer, D.; Castley, A.; Mamotte, C.; Maxwell, D. et al. Association between presence of HLA-B*5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse-transcriptase inhibitor abacavir. Lancet; 2002; 359, pp. 727-732. [DOI: https://dx.doi.org/10.1016/S0140-6736(02)07873-X]
23. Hetherington, S.; Hughes, A.R.; Mosteller, M.; Shortino, D.; Baker, K.L.; Spreen, W.; Lai, E.; Davies, K.; Handley, A.; Dow, D.J. et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet; 2002; 359, pp. 1121-1122. [DOI: https://dx.doi.org/10.1016/S0140-6736(02)08158-8]
24. Martin, A.M.; Nolan, D.; Gaudieri, S.; Almeida, C.A.; Nolan, R.; James, I.; Carvalho, F.; Phillips, E.; Christiansen, F.T.; Purcell, A.W. et al. Predisposition to abacavir hypersensitivity conferred by HLA-B*5701 and a haplotypic Hsp70-Hom variant. Proc. Natl. Acad. Sci. USA; 2004; 101, pp. 4180-4185. [DOI: https://dx.doi.org/10.1073/pnas.0307067101]
25. Martin, M.A.; Kroetz, D.L. Abacavir pharmacogenetics--from initial reports to standard of care. Pharmacotherapy; 2013; 33, pp. 765-775. [DOI: https://dx.doi.org/10.1002/phar.1278]
26. Illing, P.T.; Vivian, J.P.; Dudek, N.L.; Kostenko, L.; Chen, Z.; Bharadwaj, M.; Miles, J.J.; Kjer-Nielsen, L.; Gras, S.; Williamson, N.A. et al. Immune self-reactivity triggered by drug-modified HLA-peptide repertoire. Nature; 2012; 486, pp. 554-558. [DOI: https://dx.doi.org/10.1038/nature11147]
27. Norcross, M.A.; Luo, S.; Lu, L.; Boyne, M.T.; Gomarteli, M.; Rennels, A.D.; Woodcock, J.; Margulies, D.H.; McMurtrey, C.; Vernon, S. et al. Abacavir induces loading of novel self-peptides into HLA-B*57: 01: An autoimmune model for HLA-associated drug hypersensitivity. AIDS; 2012; 26, pp. F21-F29. [DOI: https://dx.doi.org/10.1097/QAD.0b013e328355fe8f] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22617051]
28. Ostrov, D.A.; Grant, B.J.; Pompeu, Y.A.; Sidney, J.; Harndahl, M.; Southwood, S.; Oseroff, C.; Lu, S.; Jakoncic, J.; de Oliveira, C.A. et al. Drug hypersensitivity caused by alteration of the MHC-presented self-peptide repertoire. Proc. Natl. Acad. Sci. USA; 2012; 109, pp. 9959-9964. [DOI: https://dx.doi.org/10.1073/pnas.1207934109] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22645359]
29. Chessman, D.; Kostenko, L.; Lethborg, T.; Purcell, A.W.; Williamson, N.A.; Chen, Z.; Kjer-Nielsen, L.; Mifsud, N.A.; Tait, B.D.; Holdsworth, R. et al. Human leukocyte antigen class I-restricted activation of CD8+ T cells provides the immunogenetic basis of a systemic drug hypersensitivity. Immunity; 2008; 28, pp. 822-832. [DOI: https://dx.doi.org/10.1016/j.immuni.2008.04.020] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18549801]
30. Ho, G.T.; Hiemisch, W.; Pich, A.; Matern, M.; Gräser, L.S.; Blasczyk, R.; Bade-Doding, C.; Simper, G.S. Small molecule/HLA complexes alter the cellular proteomic content. New Insights into the Future of Pharmacoepidemiology and Drug Safety; Herdeiro, M.T.; Roque, F.; Figueiras, A.; Silva, T.M. IntechOpen: London, UK, 2021; pp. 91-108. [DOI: https://dx.doi.org/10.5772/intechopen.97373]
31. Mallal, S.; Phillips, E.; Carosi, G.; Molina, J.M.; Workman, C.; Tomazic, J.; Jagel-Guedes, E.; Rugina, S.; Kozyrev, O.; Cid, J.F. et al. HLA-B*5701 screening for hypersensitivity to abacavir. N. Engl. J. Med.; 2008; 358, pp. 568-579. [DOI: https://dx.doi.org/10.1056/NEJMoa0706135]
32. Badrinath, S.; Kunze-Schumacher, H.; Blasczyk, R.; Huyton, T.; Bade-Doeding, C. A Micropolymorphism Altering the Residue Triad 97/114/156 Determines the Relative Levels of Tapasin Independence and Distinct Peptide Profiles for HLA-A(*)24 Allotypes. J. Immunol. Res.; 2014; 2014, 298145. [DOI: https://dx.doi.org/10.1155/2014/298145]
33. Bade-Doding, C.; Theodossis, A.; Gras, S.; Kjer-Nielsen, L.; Eiz-Vesper, B.; Seltsam, A.; Huyton, T.; Rossjohn, J.; McCluskey, J.; Blasczyk, R. The impact of human leukocyte antigen (HLA) micropolymorphism on ligand specificity within the HLA-B*41 allotypic family. Haematologica; 2011; 96, pp. 110-118. [DOI: https://dx.doi.org/10.3324/haematol.2010.030924]
34. Ho, G.T.; Heinen, F.J.; Huyton, T.; Blasczyk, R.; Bade-Doding, C. HLA-F*01:01 presents peptides with N-terminal flexibility and a preferred length of 16 residues. Immunogenetics; 2019; 71, pp. 353-360. [DOI: https://dx.doi.org/10.1007/s00251-019-01112-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30941482]
35. Celik, A.A.; Simper, G.S.; Hiemisch, W.; Blasczyk, R.; Bade-Döding, C. HLA-G peptide preferences change in transformed cells: Impact on the binding motif. Immunogenetics; 2018; 70, pp. 485-494. [DOI: https://dx.doi.org/10.1007/s00251-018-1058-2]
36. Ho, G.T.; Heinen, F.J.; Blasczyk, R.; Pich, A.; Bade-Doeding, C. HLA-F Allele-Specific Peptide Restriction Represents an Exceptional Proteomic Footprint. Int. J. Mol. Sci.; 2019; 20, 5572. [DOI: https://dx.doi.org/10.3390/ijms20225572] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31717259]
37. Simper, G.S.; Ho, G.T.; Celik, A.A.; Huyton, T.; Kuhn, J.; Kunze-Schumacher, H.; Blasczyk, R.; Bade-Doding, C. Carbamazepine-Mediated Adverse Drug Reactions: CBZ-10,11-epoxide but Not Carbamazepine Induces the Alteration of Peptides Presented by HLA-B *15:02. J. Immunol. Res.; 2018; 2018, 5086503. [DOI: https://dx.doi.org/10.1155/2018/5086503]
38. Yuen, B.T.; Knoepfler, P.S. Histone H3.3 mutations: A variant path to cancer. Cancer Cell; 2013; 24, pp. 567-574.
39. Li, Y.; Deshpande, P.; Hertzman, R.J.; Palubinsky, A.M.; Gibson, A.; Phillips, E.J. Genomic Risk Factors Driving Immune-Mediated Delayed Drug Hypersensitivity Reactions. Front. Genet.; 2021; 12, 641905. [DOI: https://dx.doi.org/10.3389/fgene.2021.641905] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33936169]
40. Chen, X.; Wang, Y.; Wang, P.; Lian, B.; Li, C.; Wang, J.; Li, X.; Jiang, W. Systematic analysis of the associations between adverse drug reactions and pathways. Biomed. Res. Int.; 2015; 2015, 670949. [DOI: https://dx.doi.org/10.1155/2015/670949] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26495310]
41. Jedema, I.; van der Werff, N.M.; Barge, R.M.; Willemze, R.; Falkenburg, J.H. New CFSE-based assay to determine susceptibility to lysis by cytotoxic T cells of leukemic precursor cells within a heterogeneous target cell population. Blood; 2004; 103, pp. 2677-2682. [DOI: https://dx.doi.org/10.1182/blood-2003-06-2070]
42. Enevold, C.; Oturai, A.B.; Sorensen, P.S.; Ryder, L.P.; Koch-Henriksen, N.; Bendtzen, K. Multiple sclerosis and polymorphisms of innate pattern recognition receptors TLR1-10, NOD1-2, DDX58, and IFIH1. J. Neuroimmunol.; 2009; 212, pp. 125-131. [DOI: https://dx.doi.org/10.1016/j.jneuroim.2009.04.008]
43. Sheng, Y.; Jin, X.; Xu, J.; Gao, J.; Du, X.; Duan, D.; Li, B.; Zhao, J.; Zhan, W.; Tang, H. et al. Sequencing-based approach identified three new susceptibility loci for psoriasis. Nat. Commun.; 2014; 5, 4331. [DOI: https://dx.doi.org/10.1038/ncomms5331]
44. Cunninghame Graham, D.S.; Morris, D.L.; Bhangale, T.R.; Criswell, L.A.; Syvanen, A.C.; Ronnblom, L.; Behrens, T.W.; Graham, R.R.; Vyse, T.J. Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with systemic lupus erythematosus. PLoS Genet.; 2011; 7, e1002341. [DOI: https://dx.doi.org/10.1371/journal.pgen.1002341]
45. Martinez, A.; Varade, J.; Lamas, J.R.; Fernandez-Arquero, M.; Jover, J.A.; de la Concha, E.G.; Fernandez-Gutierrez, B.; Urcelay, E. Association of the IFIH1-GCA-KCNH7 chromosomal region with rheumatoid arthritis. Ann. Rheum. Dis.; 2008; 67, pp. 137-138. [DOI: https://dx.doi.org/10.1136/ard.2007.073213]
46. Smyth, D.J.; Cooper, J.D.; Bailey, R.; Field, S.; Burren, O.; Smink, L.J.; Guja, C.; Ionescu-Tirgoviste, C.; Widmer, B.; Dunger, D.B. et al. A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat. Genet.; 2006; 38, pp. 617-619. [DOI: https://dx.doi.org/10.1038/ng1800]
47. Nejentsev, S.; Walker, N.; Riches, D.; Egholm, M.; Todd, J.A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science; 2009; 324, pp. 387-389. [DOI: https://dx.doi.org/10.1126/science.1167728]
48. Gorman, J.A.; Hundhausen, C.; Errett, J.S.; Stone, A.E.; Allenspach, E.J.; Ge, Y.; Arkatkar, T.; Clough, C.; Dai, X.; Khim, S. et al. The A946T variant of the RNA sensor IFIH1 mediates an interferon program that limits viral infection but increases the risk for autoimmunity. Nat. Immunol.; 2017; 18, pp. 744-752. [DOI: https://dx.doi.org/10.1038/ni.3766]
49. Downes, K.; Pekalski, M.; Angus, K.L.; Hardy, M.; Nutland, S.; Smyth, D.J.; Walker, N.M.; Wallace, C.; Todd, J.A. Reduced expression of IFIH1 is protective for type 1 diabetes. PLoS ONE; 2010; 5, e12646. [DOI: https://dx.doi.org/10.1371/journal.pone.0012646]
50. Lincez, P.J.; Shanina, I.; Horwitz, M.S. Reduced expression of the MDA5 Gene IFIH1 prevents autoimmune diabetes. Diabetes; 2015; 64, pp. 2184-2193. [DOI: https://dx.doi.org/10.2337/db14-1223]
51. Chasset, F.; Dayer, J.M.; Chizzolini, C. Type I Interferons in Systemic Autoimmune Diseases: Distinguishing Between Afferent and Efferent Functions for Precision Medicine and Individualized Treatment. Front. Pharmacol.; 2021; 12, 633821. [DOI: https://dx.doi.org/10.3389/fphar.2021.633821]
52. Yang, H.; Beutler, B.; Zhang, D. Emerging roles of spliceosome in cancer and immunity. Protein Cell; 2021; [DOI: https://dx.doi.org/10.1007/s13238-021-00856-5]
53. Chitu, V.; Stanley, E.R. Colony-stimulating factor-1 in immunity and inflammation. Curr. Opin. Immunol.; 2006; 18, pp. 39-48. [DOI: https://dx.doi.org/10.1016/j.coi.2005.11.006]
54. Warren, M.K.; Ralph, P. Macrophage growth factor CSF-1 stimulates human monocyte production of interferon, tumor necrosis factor, and colony stimulating activity. J. Immunol.; 1986; 137, pp. 2281-2285.
55. Eda, H.; Zhang, J.; Keith, R.H.; Michener, M.; Beidler, D.R.; Monahan, J.B. Macrophage-colony stimulating factor and interleukin-34 induce chemokines in human whole blood. Cytokine; 2010; 52, pp. 215-220. [DOI: https://dx.doi.org/10.1016/j.cyto.2010.08.005]
56. Lin, R.; Mamane, Y.; Hiscott, J. Structural and functional analysis of interferon regulatory factor 3: Localization of the transactivation and autoinhibitory domains. Mol. Cell Biol.; 1999; 19, pp. 2465-2474. [DOI: https://dx.doi.org/10.1128/MCB.19.4.2465]
57. Jefferies, C.A. Regulating IRFs in IFN Driven Disease. Front. Immunol.; 2019; 10, 325. [DOI: https://dx.doi.org/10.3389/fimmu.2019.00325]
58. Petro, T.M. IFN Regulatory Factor 3 in Health and Disease. J. Immunol.; 2020; 205, pp. 1981-1989. [DOI: https://dx.doi.org/10.4049/jimmunol.2000462]
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Details
; Kuhn, Joachim 3 ; Blasczyk, Rainer 1
; Pich, Andreas 2
; Bade-Döding, Christina 1
1 Institute for Transfusion Medicine and Transplantat Engineering, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany;
2 Institute of Toxicology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany;
3 Institute for Laboratory and Transfusion Medicine, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Georgstr. 11, 44801 Bochum, Germany;