Development of a Molecular Signature to Monitor Pharmacodynamic Responses Mediated by In Vivo Administration of Glucocorticoids

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition

Background

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.

Methods

We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.

Results

Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.

Conclusions

The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

Differential gene expression in B cells and T helper cells following high-dose glucocorticoid therapy for multiple sclerosis relapse

BACKGROUND

Despite remarkable advances in the therapy of multiple sclerosis (MS), patients with MS may still experience relapses. High-dose short-term methylprednisolone (MP) remains the standard treatment in the acute management of MS relapses due to its potent anti-inflammatory and immunosuppressive properties. However, there is a lack of studies on the cell type-specific transcriptome changes that are induced by this synthetic glucocorticoid (GC). Moreover, it is not well understood why some patients do not benefit adequately from MP therapy.

METHODS

We collected peripheral blood from MS patients in relapse immediately before and after ∼3-5 days of therapy with MP at 4 study centers. CD19+ B cells and CD4+ T cells were then isolated for profiling the transcriptome with high-density arrays. The patients' improvement of neurological symptoms was evaluated after ∼2 weeks by the treating physicians. We finally analyzed the data to identify genes that were differentially expressed in response to the therapy and whose expression differed between clinical responders and non-responders.

RESULTS

After MP treatment, a total of 33 genes in B cells and 55 genes in T helper cells were significantly up- or downregulated. The gene lists overlap in 10 genes and contain genes that have already been described as GC-responsive genes in the literature on other cell types and diseases. Their differential expression points to a rapid and coordinated modulation of multiple signaling pathways that influence transcription. Genes that were previously suggested as potential prognostic biomarkers of the clinical response to MP therapy could not be confirmed in our data. However, a greater increase in the expression of genes encoding proteins with antimicrobial activity was detected in CD4+ T cells from non-responders compared to responders.

CONCLUSION

Our study delved into the cell type-specific effects of MP at the transcriptional level. The data suggest a therapy-induced ectopic expression of some genes (e.g., AZU1, ELANE and MPO), especially in non-responders. The biological consequences of this remain to be explored in greater depth. A better understanding of the molecular mechanisms underlying clinical recovery from relapses in patients with MS will help to optimize future treatment decisions.

Preclinical Evidence for the Glucocorticoid-Sparing Potential of a Dual Toll-Like Receptor 7/8 Inhibitor in Autoimmune Diseases

Toll-like receptor 7 (TLR7) and TLR8 are single-stranded RNA-sensing endosomal pattern recognition receptors that evolved to defend against viral infections. However, aberrant TLR7/8 activation by endogenous ligands has been implicated in the pathogenesis of autoimmune diseases including systemic lupus erythematosus. TLR activation and type I interferon (IFN) were shown recently to impart resistance to glucocorticoids (GC), which are part of the standard of care for multiple autoimmune diseases. While GCs are effective, a plethora of undesirable effects limit their use. New treatment approaches that allow for the use of lower and safer doses of GCs would be highly beneficial. Herein, we report that a dual TLR7/8 inhibitor (TLR7/8i) increases the effectiveness of GCs in inflammatory settings. Human peripheral blood mononuclear cell studies revealed increased GC sensitivity in the presence of TLR7/8i for reducing inflammatory cytokine production, a synergistic effect that was most pronounced in myeloid cells, particularly monocytes. Gene expression analysis by NanoString and single-cell RNA sequencing revealed that myeloid cells were substantially impacted by combining low-dose TLR7/8i and GC, as evidenced by the effects on nuclear factor-kappa B-regulated cytokines and GC-response genes, although IFNs were affected to a smaller degree. Low dose of TLR7/8i plus GC was more efficacious then either agent alone in the MRL/lpr mouse model of lupus, with improved proteinuria and survival. Overall, our findings indicate a GC-sparing potential for TLR7/8i compounds, suggesting TLR7/8i may offer a new strategy for the treatment of autoimmune diseases. SIGNIFICANCE STATEMENT: Some features of autoimmune diseases may be resistant to glucocorticoids, mediated at least in part by toll-like receptor (TLR) activation, necessitating higher doses that are associated with considerable toxicities. We demonstrate that TLR7/8 inhibition and glucocorticoids work synergistically to reduce inflammation in a cell-type specific manner and suppress disease in a mouse model of lupus. TLR7/8 inhibition is a promising strategy for the treatment of autoimmune diseases and has glucocorticoid-sparing potential.

Jagged1 Acts as an RBP-J Target and Feedback Suppresses TNF-Mediated Inflammatory Osteoclastogenesis

TNF plays a crucial role in inflammation and bone resorption in various inflammatory diseases, including rheumatoid arthritis (RA). However, its direct ability to drive macrophages to differentiate into osteoclasts is limited. Although RBP-J is recognized as a key inhibitor of TNF-mediated osteoclastogenesis, the precise mechanisms that restrain TNF-induced differentiation of macrophages into osteoclasts are not fully elucidated. In this study, we identified that the Notch ligand Jagged1 is a previously unrecognized RBP-J target. The expression of Jagged1 is significantly induced by TNF mainly through RBP-J. The TNF-induced Jagged1 in turn functions as a feedback inhibitory regulator of TNF-mediated osteoclastogenesis. This feedback inhibition of osteoclastogenesis by Jagged1 does not exist in RANKL-induced mouse osteoclast differentiation, as RANKL does not induce Jagged1 expression. The Jagged1 level in peripheral blood monocytes/osteoclast precursors is decreased in RA compared with the nonerosive inflammatory disease systemic lupus erythematosus, suggesting a mechanism that contributes to increased osteoclast formation in RA. Moreover, recombinant Jagged1 suppresses human inflammatory osteoclastogenesis. Our findings identify Jagged1 as an RBP-J direct target that links TNF and Notch signaling pathways and restrains TNF-mediated osteoclastogenesis. Given that Jagged1 has no effect on TNF-induced expression of inflammatory genes, its use may present a new complementary therapeutic approach to mitigate inflammatory bone loss with little impact on the immune response in disease conditions.

An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients

Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications

BACKGROUND

Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

METHODS

We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.

RESULTS

Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).

CONCLUSIONS

Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Blood transcriptomic signature in type-2 biomarker-low severe asthma and asthma control

BACKGROUND

Patients with type-2 (T2) cytokine-low severe asthma often have persistent symptoms despite suppression of T2 inflammation with corticosteroids.

OBJECTIVES

We sought to analyze whole blood transcriptome from 738 samples in T2-biomarker-high/-low patients with severe asthma to relate transcriptomic signatures to T2 biomarkers and asthma symptom scores.

METHODS

Bulk RNA-seq data were generated for blood samples (baseline, week 24, week 48) from 301 participants recruited to a randomized clinical trial of corticosteroid optimization in severe asthma. Unsupervised clustering, differential gene expression analysis, and pathway analysis were performed. Patients were grouped by T2-biomarker status and symptoms. Associations between clinical characteristics and differentially expressed genes (DEGs) associated with biomarker and symptom levels were investigated.

RESULTS

Unsupervised clustering identified 2 clusters; cluster 2 patients were blood eosinophil-low/symptom-high and more likely to be receiving oral corticosteroids (OCSs). Differential gene expression analysis of these clusters, with and without stratification for OCSs, identified 2960 and 4162 DEGs, respectively. Six hundred twenty-seven of 2960 genes remained after adjusting for OCSs by subtracting OCS signature genes. Pathway analysis identified dolichyl-diphosphooligosaccharide biosynthesis and assembly of RNA polymerase I complex as significantly enriched pathways. No stable DEGs were associated with high symptoms in T2-biomarker-low patients, but numerous associated with elevated T2 biomarkers, including 15 that were upregulated at all time points irrespective of symptom level.

CONCLUSIONS

OCSs have a considerable effect on whole blood transcriptome. Differential gene expression analysis demonstrates a clear T2-biomarker transcriptomic signature, but no signature was found in association with T2-biomarker-low patients, including those with a high symptom burden.

Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4⁺ cells (T_h1 and T_h2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 T_h cells (T_h1 and T_h17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).

Molecular signature of methotrexate response among rheumatoid arthritis patients

Background

Methotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but failure of satisfying treatment response occurs in a significant proportion of patients. Here we present a longitudinal multi-omics study aimed at detecting molecular and cellular processes in peripheral blood associated with a successful methotrexate treatment of rheumatoid arthritis.

Methods

Eighty newly diagnosed patients with RA underwent clinical assessment and donated blood before initiation of MTX, and 3 months into treatment. Flow cytometry was used to describe cell types and presence of activation markers in peripheral blood, the expression of 51 proteins was measured in serum or plasma, and RNA sequencing was performed in peripheral blood mononuclear cells (PBMC). Response to treatment after 3 months was determined using the EULAR response criteria. We assessed the changes in biological phenotypes during treatment, and whether these changes differed between responders and non-responders with regression analysis. By using measurements from baseline, we also tried to find biomarkers of future MTX response or, alternatively, to predict MTX response.

Results

Among the MTX responders, (Good or Moderate according to EULAR treatment response classification, n = 60, 75%), we observed changes in 29 partly overlapping cell types proportions, levels of 13 proteins and expression of 38 genes during treatment. These changes were in most cases suppressions that were stronger among responders compared to non-responders. Within responders to treatment, we observed a suppression of FOXP3 gene expression, reduction of immunoglobulin gene expression and suppression of genes involved in cell proliferation. The proportion of many HLA-DR expressing T-cell populations were suppressed in all patients irrespective of clinical response, and the proportion of many IL21R+ T-cells were reduced exclusively in non-responders. Using only the baseline measurements we could not detect any biomarkers or prediction models that could predict response to MTX.

Conclusion

We conclude that a deep molecular and cellular phenotyping of peripheral blood cells in RA patients treated with methotrexate can reveal previously not recognized differences between responders and non-responders during 3 months of treatment with MTX. This may contribute to the understanding of MTX mode of action and explain non-responsiveness to MTX therapy.

Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression

OBJECTIVES

Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions.

METHODS

Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores.

RESULTS

MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes.

CONCLUSIONS

MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

TGFβ reprograms TNF stimulation of macrophages towards a non-canonical pathway driving inflammatory osteoclastogenesis

It is well-established that receptor activator of NF-κB ligand (RANKL) is the inducer of physiological osteoclast differentiation. However, the specific drivers and mechanisms driving inflammatory osteoclast differentiation under pathological conditions remain obscure. This is especially true given that inflammatory cytokines such as tumor necrosis factor (TNF) demonstrate little to no ability to directly drive osteoclast differentiation. Here, we found that transforming growth factor β (TGFβ) priming enables TNF to effectively induce osteoclastogenesis, independently of the canonical RANKL pathway. Lack of TGFβ signaling in macrophages suppresses inflammatory, but not basal, osteoclastogenesis and bone resorption in vivo. Mechanistically, TGFβ priming reprograms the macrophage response to TNF by remodeling chromatin accessibility and histone modifications, and enables TNF to induce a previously unrecognized non-canonical osteoclastogenic program, which includes suppression of the TNF-induced IRF1-IFNβ-IFN-stimulated-gene axis, IRF8 degradation and B-Myb induction. These mechanisms are active in rheumatoid arthritis, in which TGFβ level is elevated and correlates with osteoclast activity. Our findings identify a TGFβ/TNF-driven inflammatory osteoclastogenic program, and may lead to development of selective treatments for inflammatory osteolysis.

Lupus enhancer risk variant causes dysregulation of IRF8 through cooperative lncRNA and DNA methylation machinery

Despite strong evidence that human genetic variants affect the expression of many key transcription factors involved in autoimmune diseases, establishing biological links between non-coding risk variants and the gene targets they regulate remains a considerable challenge. Here, we combine genetic, epigenomic, and CRISPR activation approaches to screen for functional variants that regulate IRF8 expression. We demonstrate that the locus containing rs2280381 is a cell-type-specific enhancer for IRF8 that spatially interacts with the IRF8 promoter. Further, rs2280381 mediates IRF8 expression through enhancer RNA AC092723.1, which recruits TET1 to the IRF8 promoter regulating IRF8 expression by affecting methylation levels. The alleles of rs2280381 modulate PU.1 binding and chromatin state to regulate AC092723.1 and IRF8 expression differentially. Our work illustrates an integrative strategy to define functional genetic variants that regulate the expression of critical genes in autoimmune diseases and decipher the mechanisms underlying the dysregulation of IRF8 expression mediated by lupus risk variants.

MMP9 mRNA is a potential diagnostic and treatment monitoring marker for PTSD: Evidence from mice and humans

Although matrix metalloproteinase 9 (MMP9) has been found associated with various psychiatric disorders and with threat memories in humans, its role in post-traumatic stress disorder (PTSD) and related animal models is understudied. Thus, we analyzed MMP9 mRNA expression kinetics during two different stress experiments, i.e., the Trier Social Stress Test and the dexamethasone suppression test (DST), in whole blood of two independent cohorts of PTSD patients vs. non-traumatized healthy controls (HC) and, moreover, in a mouse model of PTSD and in dexamethasone-treated mice. Besides MMP9, we quantified mRNA levels of four of its regulators, i.e., interleukin (IL)-1 receptor 1 and 2 (IL1R1, IL1R2), IL-6 receptor and tumor necrosis factor receptor 1 (TNFR1) in 10 patients exposed to the DST before vs. after successful PTSD psychotherapy vs. 13 HC and, except from Il6r, also in different brain regions of the PTSD mouse model. We are the first to show that blood MMP9 mRNA concentrations were elevated after acute dexamethasone in PTSD patients, improved upon partial remission of PTSD and were, furthermore, also elevated, together with its regulator Tnfr1, in the prefrontal cortex of PTSD-like mice. In contrast, blood TNFR1 and IL1R2 were markedly underexpressed in PTSD patients. In conclusion, we found translational evidence supporting that, I, TNFR1 and MMP9 mRNA expression might be involved in PTSD pathobiology, II, might constitute potential diagnostic blood biomarkers for PTSD and, importantly, III, post-dexamethasone blood MMP9 hyperexpression, which speculatively results from post-dexamethasone underexpression of IL1R2, might serve also as potential treatment monitoring biomarker for PTSD.

Glucocorticoid gene signatures in systemic lupus erythematosus and the effects of type I interferon: a cross-sectional and in-vitro study

BACKGROUND

Glucocorticoids, used as a therapy in systemic lupus erythematosus (SLE), interact with the cytoplasmic glucocorticoid receptor to modulate gene transcription. Minimising the use of glucocorticoids is a goal in SLE; however, pharmacological measures to support clinical guidelines are scarce. We evaluated glucocorticoid-regulated genes for their potential use as biomarkers of glucocorticoid exposure in SLE. We examined interactions between changes in gene expression that are induced by glucocorticoids and type I interferon.

METHODS

Genes regulated by glucocorticoids and type I interferon were analysed in relation to glucocorticoid exposure in adult patients meeting the American College of Rheumatology criteria for SLE from three cross-sectional cohorts: a local cohort from a tertiary hospital in Melbourne, VIC, Australia, and two public datasets (GSE49454, Hospital de la Conception, Marseille, France, and GSE88884, patients enrolled in a large, multicentre clinical trial). RNA sequencing was done using RNA from healthy donor leucocytes treated with the glucocorticoid dexamethasone, or type I interferon, or both.

FINDINGS

Glucocorticoid-regulated genes were analysed in a local SLE cohort (n=18) and public dataset GSE49454 (n=62). Five genes correlated with glucocorticoid dose in both cohorts and were combined to make a glucocorticoid gene signature. Validity of the glucocorticoid gene signature was tested in the public dataset GSE88884 (n=1756). A dose-dependent association was observed with glucocorticoid dose (p<0·0001), and the glucocorticoid gene signature had moderate ability to identify patients taking high-dose glucocorticoid (area under the curve [AUC]=0·77) although was less discriminatory when including all doses (AUC=0·69). We saw no effect of glucocorticoid dose on type I interferon -regulated gene expression. Patients with a high type I interferon gene signature had reduced glucocorticoid gene signature expression compared with patients with a low type I interferon gene signature matched for glucocorticoid dose, suggesting type I interferon inhibits glucocorticoid-stimulated gene expression. In RNA sequencing experiments, type I interferon impaired the expression of glucocorticoid-induced genes, whereas dexamethasone had minimal effect on the expression of type I interferon-stimulated genes. We identified genes regulated by dexamethasone but not affected by type I interferon; combined signatures using these genes also showed moderate ability to distinguish patients taking glucocorticoids.

INTERPRETATION

A gene signature for glucocorticoid exposure was identified, but the substantial effect of type I interferon on glucocorticoid-induced genes might limit its application in SLE. These data confirm the insensitivity of type I interferon-regulated genes to glucocorticoids, and together support the concept that type I interferon has a role in glucocorticoid resistance in SLE.

FUNDING

Lupus Research Alliance and Australian National Health and Medical Research Council.

Potential Transcriptional Biomarkers to Guide Glucocorticoid Replacement in Autoimmune Addison's Disease

Background

No reliable biomarkers exist to guide glucocorticoid (GC) replacement treatment in autoimmune Addison's disease (AAD), leading to overtreatment with alarming and persistent side effects or undertreatment, which could be fatal.

Objective

To explore changes in gene expression following different GC replacement doses as a means of identifying candidate transcriptional biomarkers to guide GC replacement in AAD.

Methods

Step 1: Global microarray expression analysis on RNA from whole blood before and after intravenous infusion of 100 mg hydrocortisone (HC) in 10 patients with AAD. In 3 of the most highly upregulated genes, we performed real-time PCR (rt-PCR) to compare gene expression levels before and 3, 4, and 6 hours after the HC infusion. Step 2: Rt-PCR to compare expression levels of 93 GC-regulated genes in normal versus very low morning cortisol levels in 27 patients with AAD.

Results

Step 1: Two hours after infusion of 100 mg HC, there was a marked increase in FKBP5, MMP9, and DSIPI expression levels. MMP9 and DSIPI expression levels correlated with serum cortisol. Step 2: Expression levels of CEBPB, DDIT4, FKBP5, DSIPI, and VDR were increased and levels of ADARB1, ARIDB5, and POU2F1 decreased in normal versus very low morning cortisol. Normal serum cortisol levels positively correlated with DSIPI, DDIT4, and FKBP5 expression.

Conclusions

We introduce gene expression as a novel approach to guide GC replacement in AAD. We suggest that gene expression of DSIPI, DDIT4, and FKBP5 are particularly promising candidate biomarkers of GC replacement, followed by MMP9, CEBPB, VDR, ADARB1, ARID5B, and POU2F1.